From 334314f6d1145388a3440a13d829e4d46eb15fc1 Mon Sep 17 00:00:00 2001
From: bbauvin <baptiste.bauvin@centrale-marseille.fr>
Date: Thu, 27 Oct 2016 16:02:44 -0400
Subject: [PATCH] I leave

---
 Code/MonoMutliViewClassifiers/ExecClassif.py  |   30 +-
 .../Monoview/ExecClassifMonoView.py           |    3 +-
 .../Multiview/ExecMultiview.py                |    3 +-
 .../Multiview/Fusion/analyzeResults.py        |    2 +
 .../ResultAnalysis.py                         |   24 +-
 ...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log |    0
 ...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log |    0
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 delete mode 100644 Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
 delete mode 100644 Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
 delete mode 100644 Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
 delete mode 100644 Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
 delete mode 100644 Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
 delete mode 100644 Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
 delete mode 100644 Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
 delete mode 100644 Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
 create mode 100644 Code/MonoMutliViewClassifiers/utils/Transformations.py

diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index 0d876e6a..72edbcbb 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -25,6 +25,11 @@ from ResultAnalysis import resultAnalysis
 from Versions import testVersions
 import MonoviewClassifiers
 
+import matplotlib.pyplot as plt
+from matplotlib import cm
+from numpy.random import randint
+import random
+
 # Author-Info
 __author__ 	= "Baptiste Bauvin"
 __status__ 	= "Prototype"                           # Production, Development, Prototype
@@ -104,7 +109,7 @@ def initBenchmark(args):
         allMumboAlgos = [name for _, name, isPackage in
                          pkgutil.iter_modules(['Multiview/Mumbo/Classifiers'])
                          if not isPackage and not name in ["SubSampling", "ModifiedMulticlass", "Kover"]]
-        allMultiviewAlgos = {"Fusion": allFusionAlgos, "Mumbo": allMumboAlgos}
+        allMultiviewAlgos = {"Fusion": allFusionAlgos}#, "Mumbo": allMumboAlgos}
         benchmark = {"Monoview": allMonoviewAlgos, "Multiview": allMultiviewAlgos}
 
     if "Multiview" in args.CL_type.strip(":"):
@@ -290,6 +295,24 @@ def initMultiviewArguments(args, benchmark, views, viewsIndices, accuracies, cla
             pass
     return argumentDictionaries
 
+
+def analyzeLabels(labelsArrays, realLabels, classifiersNames):
+    nbClassifiers = len(classifiersNames)
+    nbExamples = realLabels.shape[0]
+    nbIter = nbExamples/nbClassifiers
+    data = np.zeros((nbExamples, nbClassifiers*nbIter))
+    tempData = np.array([labelsArray == realLabels for labelsArray in labelsArrays]).astype(int)
+    for classifierIndex in range(nbClassifiers):
+        for iterIndex in range(nbIter):
+            data[:,classifierIndex*nbIter+iterIndex] = tempData[:,classifierIndex]
+    fig, ax = plt.subplots()
+    cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
+    ax.set_title('Gaussian noise with vertical colorbar')
+    cbar = fig.colorbar(cax, ticks=[0, 1])
+    cbar.ax.set_yticklabels(['Wrong', ' Right'])
+    fig.savefig("test.png")
+
+
 parser = argparse.ArgumentParser(
     description='This file is used to benchmark the accuracies fo multiple classification algorithm on multiview data.',
     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@@ -480,6 +503,7 @@ if nbCores>1:
     accuracies = [[result[1][1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
     classifiersNames = [[result[1][0] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
     classifiersConfigs = [[result[1][1][:-1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
+
 else:
     resultsMonoview+=([ExecMonoview(DATASET.get("View"+str(arguments["viewIndex"])),
                                     DATASET.get("Labels").value, args.name, labelsNames,
@@ -513,7 +537,9 @@ if nbCores>1:
     logging.debug("Start:\t Deleting "+str(nbCores)+" temporary datasets for multiprocessing")
     datasetFiles = DB.deleteHDF5(args.pathF, args.name, nbCores)
     logging.debug("Start:\t Deleting datasets for multiprocessing")
-
+labels = np.array([resultMonoview[1][3] for resultMonoview in resultsMonoview]+[resultMultiview[3] for resultMultiview in resultsMultiview]).transpose()
+trueLabels = DATASET.get("Labels").value
+analyzeLabels(labels, trueLabels, ["" in range(labels.shape[1])])
 times = [dataBaseTime, monoviewTime, multiviewTime]
 # times=[]
 results = (resultsMonoview, resultsMultiview)
diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index 37619e1f..5c9c9ece 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -103,6 +103,7 @@ def ExecMonoview(X, Y, name, labelsNames, learningRate, nbFolds, nbCores, databa
         y_train_preds.append(y_train_pred)
         y_tests.append(y_test)
         y_test_preds.append(y_test_pred)
+        full_labels = cl_res.predict(X)
         logging.debug("Done:\t Predicting")
     t_end  = time.time() - t_start
     logging.debug("Info:\t Time for training and predicting: " + str(t_end) + "[s]")
@@ -138,7 +139,7 @@ def ExecMonoview(X, Y, name, labelsNames, learningRate, nbFolds, nbCores, databa
 
     logging.info("Done:\t Result Analysis")
     viewIndex = args["viewIndex"]
-    return viewIndex, [CL_type, cl_desc+[feat], metricsScores]
+    return viewIndex, [CL_type, cl_desc+[feat], metricsScores, full_labels]
     # # Classification Report with Precision, Recall, F1 , Support
     # logging.debug("Info:\t Classification report:")
     # filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-Report"
diff --git a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
index 434f8091..37e02981 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
@@ -95,6 +95,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
         classifier.fit_hdf5(DATASET, trainIndices=learningIndices, viewsIndices=viewsIndices)
         trainLabels = classifier.predict_hdf5(DATASET, usedIndices=learningIndices, viewsIndices=viewsIndices)
         testLabels = classifier.predict_hdf5(DATASET, usedIndices=validationIndices, viewsIndices=viewsIndices)
+        fullLabels = classifier.predict_hdf5(DATASET, viewsIndices=viewsIndices)
         trainLabelsIterations.append(trainLabels)
         testLabelsIterations.append(testLabels)
         ivalidationIndices.append(validationIndices)
@@ -144,7 +145,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
             imagesAnalysis[imageName].savefig(outputFileName + imageName + '.png')
 
     logging.info("Done:\t Result Analysis")
-    return CL_type, classificationKWARGS, metricsScores
+    return CL_type, classificationKWARGS, metricsScores, fullLabels
 
 
 if __name__=='__main__':
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
index 50755fc7..874452b2 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
@@ -10,6 +10,7 @@ import Methods.LateFusion
 import Metrics
 
 
+
 # Author-Info
 __author__ 	= "Baptiste Bauvin"
 __status__ 	= "Prototype"                           # Production, Development, Prototype
@@ -20,6 +21,7 @@ def error(testLabels, computedLabels):
     return float(error) * 100 / len(computedLabels)
 
 
+
 def printMetricScore(metricScores, metrics):
     metricScoreString = "\n\n"
     for metric in metrics:
diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index 99f4b271..22fee825 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -8,9 +8,11 @@ import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 import numpy as np
+from matplotlib import cm
 
 #Import own Modules
 import Metrics
+from utils.Transformations import signLabels
 
 # Author-Info
 __author__ 	= "Baptiste Bauvin"
@@ -26,8 +28,10 @@ def autolabel(rects, ax):
 
 
 def resultAnalysis(benchmark, results, name, times, metrics):
+    mono, multi = results
+    labelsByView = np.array([res[0][3] for res in mono]+[res[3] for res in multi])
+    makeColorMap(labelsByView, name)
     for metric in metrics:
-        mono, multi = results
         names = [res[1][0]+"-"+res[1][1][-1] for res in mono]
         names+=[type_ for type_, a, b in multi if type_ != "Fusion"]
         names+=[ "Late-"+str(a["fusionMethod"]) for type_, a, b in multi if type_ == "Fusion" and a["fusionType"] != "EarlyFusion"]
@@ -63,3 +67,21 @@ def resultAnalysis(benchmark, results, name, times, metrics):
     logging.info("Extraction time : "+str(times[0])+"s, Monoview time : "+str(times[1])+"s, Multiview Time : "+str(times[2])+"s")
 
 
+def makeColorMap(labelsByView, name):
+    nb_view = labelsByView.shape[1]
+    nbExamples = labelsByView.shape[0]
+    # Make plot with vertical (default) colorbar
+    fig, ax = plt.subplots()
+    data = np.zeros((nbExamples,nbExamples), dtype=int)
+    datap = np.array([signLabels(labels) for labels in labelsByView])
+    nbRepet = nbExamples/nb_view
+    for j in range(nb_view):
+        for i in range(nbRepet):
+            data[:, j*50+i] = datap[:, j]
+
+    cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
+    ax.set_title('Labels per view')
+    cbar = fig.colorbar(cax, ticks=[0, 1])
+    cbar.ax.set_yticklabels(['-1', ' 1'])  # vertically oriented colorbar
+    plt.show()
+    fig.savefig("Results/"+time.strftime("%Y%m%d-%H%M%S")+"-"+name+"-labels.png")
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-075939-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-075939-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index e69de29b..00000000
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-075959-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-075959-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index e69de29b..00000000
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080453-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-080453-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 1c28900d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080453-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1472 +0,0 @@
-2016-09-06 08:04:53,806 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:04:53,807 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000152125 Gbytes /!\ 
-2016-09-06 08:04:58,817 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 08:04:58,819 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 08:04:58,908 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:04:58,909 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:04:58,909 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:04:58,911 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:04:58,912 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:04:58,912 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:04:59,094 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:04:59,094 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:04:59,094 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:04:59,094 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:04:59,094 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:04:59,094 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:04:59,095 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:04:59,095 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:04:59,204 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:04:59,205 DEBUG: Start:	 Training
-2016-09-06 08:04:59,209 DEBUG: Info:	 Time for Training: 0.297763824463[s]
-2016-09-06 08:04:59,209 DEBUG: Done:	 Training
-2016-09-06 08:04:59,209 DEBUG: Start:	 Predicting
-2016-09-06 08:04:59,246 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:04:59,246 DEBUG: Start:	 Training
-2016-09-06 08:04:59,253 DEBUG: Info:	 Time for Training: 0.345137834549[s]
-2016-09-06 08:04:59,253 DEBUG: Done:	 Training
-2016-09-06 08:04:59,253 DEBUG: Start:	 Predicting
-2016-09-06 08:04:59,427 DEBUG: Done:	 Predicting
-2016-09-06 08:04:59,427 DEBUG: Done:	 Predicting
-2016-09-06 08:04:59,428 DEBUG: Start:	 Getting Results
-2016-09-06 08:04:59,428 DEBUG: Start:	 Getting Results
-2016-09-06 08:04:59,429 DEBUG: Done:	 Getting Results
-2016-09-06 08:04:59,429 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:04:59,430 INFO: Done:	 Result Analysis
-2016-09-06 08:04:59,430 DEBUG: Done:	 Getting Results
-2016-09-06 08:04:59,431 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:04:59,431 INFO: Done:	 Result Analysis
-2016-09-06 08:04:59,576 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:04:59,577 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:04:59,577 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:04:59,578 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:04:59,578 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:04:59,578 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:04:59,578 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:04:59,581 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:04:59,581 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:04:59,581 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:04:59,582 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:04:59,582 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:04:59,582 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:04:59,582 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:04:59,670 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:04:59,670 DEBUG: Start:	 Training
-2016-09-06 08:04:59,671 DEBUG: Info:	 Time for Training: 0.0952470302582[s]
-2016-09-06 08:04:59,671 DEBUG: Done:	 Training
-2016-09-06 08:04:59,671 DEBUG: Start:	 Predicting
-2016-09-06 08:04:59,682 DEBUG: Done:	 Predicting
-2016-09-06 08:04:59,683 DEBUG: Start:	 Getting Results
-2016-09-06 08:04:59,684 DEBUG: Done:	 Getting Results
-2016-09-06 08:04:59,684 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:04:59,685 INFO: Done:	 Result Analysis
-2016-09-06 08:05:00,340 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:00,340 DEBUG: Start:	 Training
-2016-09-06 08:05:00,410 DEBUG: Info:	 Time for Training: 0.829602956772[s]
-2016-09-06 08:05:00,410 DEBUG: Done:	 Training
-2016-09-06 08:05:00,410 DEBUG: Start:	 Predicting
-2016-09-06 08:05:00,418 DEBUG: Done:	 Predicting
-2016-09-06 08:05:00,418 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:00,419 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:00,419 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:05:00,419 INFO: Done:	 Result Analysis
-2016-09-06 08:05:00,524 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:00,524 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:05:00,524 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:00,524 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:00,524 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:05:00,524 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:00,525 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:05:00,525 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:05:00,525 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:05:00,525 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:05:00,525 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:00,525 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:00,525 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:00,525 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:00,733 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:00,733 DEBUG: Start:	 Training
-2016-09-06 08:05:00,759 DEBUG: Info:	 Time for Training: 0.235524892807[s]
-2016-09-06 08:05:00,759 DEBUG: Done:	 Training
-2016-09-06 08:05:00,759 DEBUG: Start:	 Predicting
-2016-09-06 08:05:00,766 DEBUG: Done:	 Predicting
-2016-09-06 08:05:00,766 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:00,768 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:00,768 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.428571428571
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.428571428571
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:05:00,768 INFO: Done:	 Result Analysis
-2016-09-06 08:05:01,477 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:01,477 DEBUG: Start:	 Training
-2016-09-06 08:05:01,478 DEBUG: Info:	 Time for Training: 0.954334020615[s]
-2016-09-06 08:05:01,478 DEBUG: Done:	 Training
-2016-09-06 08:05:01,478 DEBUG: Start:	 Predicting
-2016-09-06 08:05:01,564 DEBUG: Done:	 Predicting
-2016-09-06 08:05:01,564 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:01,565 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:01,566 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:05:01,566 INFO: Done:	 Result Analysis
-2016-09-06 08:05:01,694 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:01,694 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:01,694 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:05:01,694 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:05:01,694 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:01,694 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:01,695 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:05:01,695 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:05:01,695 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:05:01,696 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:01,696 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:01,695 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:05:01,698 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:01,698 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:01,818 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:01,818 DEBUG: Start:	 Training
-2016-09-06 08:05:01,845 DEBUG: Info:	 Time for Training: 0.151879787445[s]
-2016-09-06 08:05:01,845 DEBUG: Done:	 Training
-2016-09-06 08:05:01,845 DEBUG: Start:	 Predicting
-2016-09-06 08:05:01,855 DEBUG: Done:	 Predicting
-2016-09-06 08:05:01,855 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:01,857 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:01,857 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:05:01,857 INFO: Done:	 Result Analysis
-2016-09-06 08:05:01,862 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:01,862 DEBUG: Start:	 Training
-2016-09-06 08:05:01,887 DEBUG: Info:	 Time for Training: 0.19349193573[s]
-2016-09-06 08:05:01,887 DEBUG: Done:	 Training
-2016-09-06 08:05:01,887 DEBUG: Start:	 Predicting
-2016-09-06 08:05:01,891 DEBUG: Done:	 Predicting
-2016-09-06 08:05:01,892 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:01,893 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:01,893 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6464
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:05:01,893 INFO: Done:	 Result Analysis
-2016-09-06 08:05:02,058 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:02,059 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:05:02,059 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:02,059 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:02,060 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:05:02,060 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:02,060 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:02,060 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:02,060 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:02,060 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:02,060 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:02,060 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:02,061 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:02,061 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:02,164 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:02,165 DEBUG: Start:	 Training
-2016-09-06 08:05:02,168 DEBUG: Info:	 Time for Training: 0.10915017128[s]
-2016-09-06 08:05:02,168 DEBUG: Done:	 Training
-2016-09-06 08:05:02,168 DEBUG: Start:	 Predicting
-2016-09-06 08:05:02,172 DEBUG: Done:	 Predicting
-2016-09-06 08:05:02,172 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:02,173 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:02,173 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:05:02,173 INFO: Done:	 Result Analysis
-2016-09-06 08:05:02,219 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:02,220 DEBUG: Start:	 Training
-2016-09-06 08:05:02,229 DEBUG: Info:	 Time for Training: 0.171218156815[s]
-2016-09-06 08:05:02,229 DEBUG: Done:	 Training
-2016-09-06 08:05:02,229 DEBUG: Start:	 Predicting
-2016-09-06 08:05:02,234 DEBUG: Done:	 Predicting
-2016-09-06 08:05:02,234 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:02,237 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:02,237 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:05:02,238 INFO: Done:	 Result Analysis
-2016-09-06 08:05:02,318 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:02,318 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:02,318 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:05:02,318 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:05:02,319 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:02,319 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:02,319 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:02,319 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:02,320 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:02,320 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:02,320 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:02,320 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:02,320 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:02,320 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:02,415 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:02,415 DEBUG: Start:	 Training
-2016-09-06 08:05:02,416 DEBUG: Info:	 Time for Training: 0.0986800193787[s]
-2016-09-06 08:05:02,416 DEBUG: Done:	 Training
-2016-09-06 08:05:02,416 DEBUG: Start:	 Predicting
-2016-09-06 08:05:02,427 DEBUG: Done:	 Predicting
-2016-09-06 08:05:02,427 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:02,429 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:02,429 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:05:02,429 INFO: Done:	 Result Analysis
-2016-09-06 08:05:03,195 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:03,196 DEBUG: Start:	 Training
-2016-09-06 08:05:03,273 DEBUG: Info:	 Time for Training: 0.955595016479[s]
-2016-09-06 08:05:03,273 DEBUG: Done:	 Training
-2016-09-06 08:05:03,273 DEBUG: Start:	 Predicting
-2016-09-06 08:05:03,282 DEBUG: Done:	 Predicting
-2016-09-06 08:05:03,282 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:03,283 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:03,283 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,283 INFO: Done:	 Result Analysis
-2016-09-06 08:05:03,393 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,393 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,393 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:05:03,393 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:05:03,393 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:03,393 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:03,394 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:03,394 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:03,394 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:03,394 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,395 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:03,395 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:03,395 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:03,395 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,506 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:03,507 DEBUG: Start:	 Training
-2016-09-06 08:05:03,508 DEBUG: Info:	 Time for Training: 0.115790128708[s]
-2016-09-06 08:05:03,508 DEBUG: Done:	 Training
-2016-09-06 08:05:03,508 DEBUG: Start:	 Predicting
-2016-09-06 08:05:03,538 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:03,538 DEBUG: Start:	 Training
-2016-09-06 08:05:03,549 DEBUG: Done:	 Predicting
-2016-09-06 08:05:03,549 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:03,551 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:03,551 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,551 INFO: Done:	 Result Analysis
-2016-09-06 08:05:03,570 DEBUG: Info:	 Time for Training: 0.178539991379[s]
-2016-09-06 08:05:03,571 DEBUG: Done:	 Training
-2016-09-06 08:05:03,571 DEBUG: Start:	 Predicting
-2016-09-06 08:05:03,577 DEBUG: Done:	 Predicting
-2016-09-06 08:05:03,577 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:03,578 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:03,579 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,579 INFO: Done:	 Result Analysis
-2016-09-06 08:05:03,664 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,664 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,664 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:05:03,664 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:05:03,664 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:03,664 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:03,665 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:03,665 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:03,665 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:03,665 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:03,665 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:03,665 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:03,666 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,666 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,764 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:03,764 DEBUG: Start:	 Training
-2016-09-06 08:05:03,783 DEBUG: Info:	 Time for Training: 0.12015581131[s]
-2016-09-06 08:05:03,784 DEBUG: Done:	 Training
-2016-09-06 08:05:03,784 DEBUG: Start:	 Predicting
-2016-09-06 08:05:03,790 DEBUG: Done:	 Predicting
-2016-09-06 08:05:03,790 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:03,791 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:03,791 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,792 INFO: Done:	 Result Analysis
-2016-09-06 08:05:03,818 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:03,818 DEBUG: Start:	 Training
-2016-09-06 08:05:03,846 DEBUG: Info:	 Time for Training: 0.183187961578[s]
-2016-09-06 08:05:03,847 DEBUG: Done:	 Training
-2016-09-06 08:05:03,847 DEBUG: Start:	 Predicting
-2016-09-06 08:05:03,851 DEBUG: Done:	 Predicting
-2016-09-06 08:05:03,851 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:03,852 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:03,853 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6464
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,853 INFO: Done:	 Result Analysis
-2016-09-06 08:05:03,911 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,911 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:05:03,911 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:03,912 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:03,912 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:03,912 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:03,912 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,913 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,913 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:05:03,913 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:03,914 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:03,914 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:03,914 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:03,914 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,985 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:03,985 DEBUG: Start:	 Training
-2016-09-06 08:05:03,987 DEBUG: Info:	 Time for Training: 0.0755009651184[s]
-2016-09-06 08:05:03,988 DEBUG: Done:	 Training
-2016-09-06 08:05:03,988 DEBUG: Start:	 Predicting
-2016-09-06 08:05:03,990 DEBUG: Done:	 Predicting
-2016-09-06 08:05:03,990 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:03,991 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:03,991 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,992 INFO: Done:	 Result Analysis
-2016-09-06 08:05:04,007 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:04,007 DEBUG: Start:	 Training
-2016-09-06 08:05:04,011 DEBUG: Info:	 Time for Training: 0.100708961487[s]
-2016-09-06 08:05:04,011 DEBUG: Done:	 Training
-2016-09-06 08:05:04,011 DEBUG: Start:	 Predicting
-2016-09-06 08:05:04,014 DEBUG: Done:	 Predicting
-2016-09-06 08:05:04,015 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:04,016 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:04,016 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:05:04,017 INFO: Done:	 Result Analysis
-2016-09-06 08:05:04,155 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:04,155 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:04,155 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:05:04,155 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:05:04,155 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:04,155 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:04,156 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:04,156 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:04,156 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:04,156 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:04,156 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:04,156 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:04,156 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:04,156 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:04,209 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:04,209 DEBUG: Start:	 Training
-2016-09-06 08:05:04,210 DEBUG: Info:	 Time for Training: 0.0557448863983[s]
-2016-09-06 08:05:04,210 DEBUG: Done:	 Training
-2016-09-06 08:05:04,210 DEBUG: Start:	 Predicting
-2016-09-06 08:05:04,216 DEBUG: Done:	 Predicting
-2016-09-06 08:05:04,217 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:04,217 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:04,218 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:05:04,218 INFO: Done:	 Result Analysis
-2016-09-06 08:05:04,803 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:04,803 DEBUG: Start:	 Training
-2016-09-06 08:05:04,871 DEBUG: Info:	 Time for Training: 0.716722011566[s]
-2016-09-06 08:05:04,871 DEBUG: Done:	 Training
-2016-09-06 08:05:04,871 DEBUG: Start:	 Predicting
-2016-09-06 08:05:04,879 DEBUG: Done:	 Predicting
-2016-09-06 08:05:04,879 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:04,880 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:04,880 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:05:04,880 INFO: Done:	 Result Analysis
-2016-09-06 08:05:05,007 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,007 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,007 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:05:05,007 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:05:05,007 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:05,007 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:05,008 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:05,008 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:05,008 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:05,008 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:05,009 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:05,009 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:05,009 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,009 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,082 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:05,082 DEBUG: Start:	 Training
-2016-09-06 08:05:05,083 DEBUG: Info:	 Time for Training: 0.0776901245117[s]
-2016-09-06 08:05:05,083 DEBUG: Done:	 Training
-2016-09-06 08:05:05,083 DEBUG: Start:	 Predicting
-2016-09-06 08:05:05,094 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:05,094 DEBUG: Start:	 Training
-2016-09-06 08:05:05,098 DEBUG: Done:	 Predicting
-2016-09-06 08:05:05,098 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:05,100 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:05,100 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,100 INFO: Done:	 Result Analysis
-2016-09-06 08:05:05,119 DEBUG: Info:	 Time for Training: 0.113911151886[s]
-2016-09-06 08:05:05,120 DEBUG: Done:	 Training
-2016-09-06 08:05:05,120 DEBUG: Start:	 Predicting
-2016-09-06 08:05:05,123 DEBUG: Done:	 Predicting
-2016-09-06 08:05:05,123 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:05,124 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:05,124 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,124 INFO: Done:	 Result Analysis
-2016-09-06 08:05:05,259 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,259 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,260 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:05:05,260 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:05:05,260 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:05,260 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:05,261 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:05,261 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:05,261 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:05,261 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:05,261 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:05,261 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:05,262 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,262 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,378 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:05,378 DEBUG: Start:	 Training
-2016-09-06 08:05:05,392 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:05,392 DEBUG: Start:	 Training
-2016-09-06 08:05:05,401 DEBUG: Info:	 Time for Training: 0.142508983612[s]
-2016-09-06 08:05:05,401 DEBUG: Done:	 Training
-2016-09-06 08:05:05,401 DEBUG: Start:	 Predicting
-2016-09-06 08:05:05,408 DEBUG: Done:	 Predicting
-2016-09-06 08:05:05,408 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:05,410 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:05,410 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,410 INFO: Done:	 Result Analysis
-2016-09-06 08:05:05,417 DEBUG: Info:	 Time for Training: 0.158962965012[s]
-2016-09-06 08:05:05,417 DEBUG: Done:	 Training
-2016-09-06 08:05:05,418 DEBUG: Start:	 Predicting
-2016-09-06 08:05:05,421 DEBUG: Done:	 Predicting
-2016-09-06 08:05:05,421 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:05,422 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:05,422 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,423 INFO: Done:	 Result Analysis
-2016-09-06 08:05:05,499 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,500 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:05:05,500 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:05,500 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,500 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:05,500 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:05,500 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:05:05,500 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:05,500 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:05,500 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,501 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:05,501 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:05,501 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:05,501 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,564 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:05,564 DEBUG: Start:	 Training
-2016-09-06 08:05:05,566 DEBUG: Info:	 Time for Training: 0.0668630599976[s]
-2016-09-06 08:05:05,566 DEBUG: Done:	 Training
-2016-09-06 08:05:05,566 DEBUG: Start:	 Predicting
-2016-09-06 08:05:05,568 DEBUG: Done:	 Predicting
-2016-09-06 08:05:05,568 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:05,569 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:05,569 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,570 INFO: Done:	 Result Analysis
-2016-09-06 08:05:05,593 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:05,593 DEBUG: Start:	 Training
-2016-09-06 08:05:05,597 DEBUG: Info:	 Time for Training: 0.0977339744568[s]
-2016-09-06 08:05:05,597 DEBUG: Done:	 Training
-2016-09-06 08:05:05,597 DEBUG: Start:	 Predicting
-2016-09-06 08:05:05,600 DEBUG: Done:	 Predicting
-2016-09-06 08:05:05,600 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:05,602 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:05,602 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,603 INFO: Done:	 Result Analysis
-2016-09-06 08:05:05,747 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,747 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,747 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:05:05,747 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:05:05,747 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:05,747 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:05,748 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:05,748 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:05,748 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:05,748 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:05,748 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:05,748 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:05,748 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,748 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,831 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:05,831 DEBUG: Start:	 Training
-2016-09-06 08:05:05,832 DEBUG: Info:	 Time for Training: 0.0860028266907[s]
-2016-09-06 08:05:05,832 DEBUG: Done:	 Training
-2016-09-06 08:05:05,832 DEBUG: Start:	 Predicting
-2016-09-06 08:05:05,842 DEBUG: Done:	 Predicting
-2016-09-06 08:05:05,842 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:05,843 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:05,843 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,844 INFO: Done:	 Result Analysis
-2016-09-06 08:05:06,432 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:06,432 DEBUG: Start:	 Training
-2016-09-06 08:05:06,500 DEBUG: Info:	 Time for Training: 0.753455877304[s]
-2016-09-06 08:05:06,500 DEBUG: Done:	 Training
-2016-09-06 08:05:06,500 DEBUG: Start:	 Predicting
-2016-09-06 08:05:06,507 DEBUG: Done:	 Predicting
-2016-09-06 08:05:06,508 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:06,508 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:06,509 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:05:06,509 INFO: Done:	 Result Analysis
-2016-09-06 08:05:06,597 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:06,597 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:06,597 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:05:06,597 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:05:06,597 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:06,597 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:05:06,598 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:06,598 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:06,598 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:06,598 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:06,598 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:06,598 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:05:06,598 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:06,598 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:05:06,693 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:06,693 DEBUG: Start:	 Training
-2016-09-06 08:05:06,694 DEBUG: Info:	 Time for Training: 0.0977959632874[s]
-2016-09-06 08:05:06,694 DEBUG: Done:	 Training
-2016-09-06 08:05:06,694 DEBUG: Start:	 Predicting
-2016-09-06 08:05:06,704 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:05:06,704 DEBUG: Start:	 Training
-2016-09-06 08:05:06,718 DEBUG: Done:	 Predicting
-2016-09-06 08:05:06,718 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:06,719 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:06,719 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:05:06,719 INFO: Done:	 Result Analysis
-2016-09-06 08:05:06,724 DEBUG: Info:	 Time for Training: 0.127656936646[s]
-2016-09-06 08:05:06,724 DEBUG: Done:	 Training
-2016-09-06 08:05:06,724 DEBUG: Start:	 Predicting
-2016-09-06 08:05:06,727 DEBUG: Done:	 Predicting
-2016-09-06 08:05:06,727 DEBUG: Start:	 Getting Results
-2016-09-06 08:05:06,728 DEBUG: Done:	 Getting Results
-2016-09-06 08:05:06,728 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:05:06,728 INFO: Done:	 Result Analysis
-2016-09-06 08:05:06,989 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:05:06,989 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:05:06,990 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:05:06,990 INFO: Info:	 Shape of View0 :(300, 17)
-2016-09-06 08:05:06,990 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:05:06,990 INFO: Info:	 Shape of View1 :(300, 14)
-2016-09-06 08:05:06,990 INFO: Info:	 Shape of View0 :(300, 17)
-2016-09-06 08:05:06,991 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 08:05:06,991 INFO: Info:	 Shape of View1 :(300, 14)
-2016-09-06 08:05:06,991 INFO: Info:	 Shape of View3 :(300, 15)
-2016-09-06 08:05:06,991 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 08:05:06,991 INFO: Done:	 Read Database Files
-2016-09-06 08:05:06,991 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:05:06,991 INFO: Info:	 Shape of View3 :(300, 15)
-2016-09-06 08:05:06,992 INFO: Done:	 Read Database Files
-2016-09-06 08:05:06,992 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:05:06,995 INFO: Done:	 Determine validation split
-2016-09-06 08:05:06,995 INFO: Start:	 Determine 5 folds
-2016-09-06 08:05:06,995 INFO: Done:	 Determine validation split
-2016-09-06 08:05:06,995 INFO: Start:	 Determine 5 folds
-2016-09-06 08:05:07,002 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:05:07,002 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:05:07,002 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:05:07,002 INFO: Done:	 Determine folds
-2016-09-06 08:05:07,002 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:05:07,003 INFO: Start:	 Classification
-2016-09-06 08:05:07,003 INFO: 	Start:	 Fold number 1
-2016-09-06 08:05:07,004 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:05:07,004 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:05:07,004 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:05:07,004 INFO: Done:	 Determine folds
-2016-09-06 08:05:07,004 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 08:05:07,005 INFO: Start:	 Classification
-2016-09-06 08:05:07,005 INFO: 	Start:	 Fold number 1
-2016-09-06 08:05:07,104 INFO: 	Start: 	 Classification
-2016-09-06 08:05:07,188 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:05:07,188 INFO: 	Start:	 Fold number 2
-2016-09-06 08:05:07,286 INFO: 	Start: 	 Classification
-2016-09-06 08:05:07,370 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:05:07,371 INFO: 	Start:	 Fold number 3
-2016-09-06 08:05:07,470 INFO: 	Start: 	 Classification
-2016-09-06 08:05:07,555 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:05:07,556 INFO: 	Start:	 Fold number 4
-2016-09-06 08:05:07,655 INFO: 	Start: 	 Classification
-2016-09-06 08:05:07,739 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:05:07,739 INFO: 	Start:	 Fold number 5
-2016-09-06 08:05:07,838 INFO: 	Start: 	 Classification
-2016-09-06 08:05:07,923 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:05:07,924 INFO: Done:	 Classification
-2016-09-06 08:05:07,924 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:05:07,924 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:05:07,929 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 45.2380952381
-	-On Validation : 83.8202247191
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 31
-		- Random Forest with num_esimators : 26, max_depth : 23
-		- K nearest Neighbors with  n_neighbors: 31
-		- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:05:07,929 INFO: Done:	 Result Analysis
-2016-09-06 08:05:08,912 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:05:08,920 DEBUG: 			View 0 : 0.513812154696
-2016-09-06 08:05:08,927 DEBUG: 			View 1 : 0.569060773481
-2016-09-06 08:05:08,934 DEBUG: 			View 2 : 0.497237569061
-2016-09-06 08:05:08,941 DEBUG: 			View 3 : 0.524861878453
-2016-09-06 08:05:08,984 DEBUG: 			 Best view : 		View3
-2016-09-06 08:05:09,067 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:05:09,074 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:09,081 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:09,089 DEBUG: 			View 2 : 0.674033149171
-2016-09-06 08:05:09,096 DEBUG: 			View 3 : 0.690607734807
-2016-09-06 08:05:09,145 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:09,295 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:05:09,303 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:09,310 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:09,317 DEBUG: 			View 2 : 0.674033149171
-2016-09-06 08:05:09,325 DEBUG: 			View 3 : 0.690607734807
-2016-09-06 08:05:09,382 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:09,602 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:05:09,609 DEBUG: 			View 0 : 0.674033149171
-2016-09-06 08:05:09,617 DEBUG: 			View 1 : 0.602209944751
-2016-09-06 08:05:09,624 DEBUG: 			View 2 : 0.668508287293
-2016-09-06 08:05:09,632 DEBUG: 			View 3 : 0.718232044199
-2016-09-06 08:05:09,690 DEBUG: 			 Best view : 		View3
-2016-09-06 08:05:09,980 DEBUG: 		Start:	 Iteration 5
-2016-09-06 08:05:09,987 DEBUG: 			View 0 : 0.674033149171
-2016-09-06 08:05:09,995 DEBUG: 			View 1 : 0.602209944751
-2016-09-06 08:05:10,002 DEBUG: 			View 2 : 0.668508287293
-2016-09-06 08:05:10,009 DEBUG: 			View 3 : 0.718232044199
-2016-09-06 08:05:10,070 DEBUG: 			 Best view : 		View3
-2016-09-06 08:05:10,426 DEBUG: 		Start:	 Iteration 6
-2016-09-06 08:05:10,434 DEBUG: 			View 0 : 0.701657458564
-2016-09-06 08:05:10,441 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:10,448 DEBUG: 			View 2 : 0.67955801105
-2016-09-06 08:05:10,456 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:10,520 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:10,946 DEBUG: 		Start:	 Iteration 7
-2016-09-06 08:05:10,953 DEBUG: 			View 0 : 0.701657458564
-2016-09-06 08:05:10,961 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:10,968 DEBUG: 			View 2 : 0.71270718232
-2016-09-06 08:05:10,975 DEBUG: 			View 3 : 0.662983425414
-2016-09-06 08:05:11,043 DEBUG: 			 Best view : 		View2
-2016-09-06 08:05:11,537 DEBUG: 		Start:	 Iteration 8
-2016-09-06 08:05:11,545 DEBUG: 			View 0 : 0.701657458564
-2016-09-06 08:05:11,552 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:11,560 DEBUG: 			View 2 : 0.668508287293
-2016-09-06 08:05:11,567 DEBUG: 			View 3 : 0.662983425414
-2016-09-06 08:05:11,637 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:12,202 DEBUG: 		Start:	 Iteration 9
-2016-09-06 08:05:12,210 DEBUG: 			View 0 : 0.701657458564
-2016-09-06 08:05:12,217 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:12,225 DEBUG: 			View 2 : 0.629834254144
-2016-09-06 08:05:12,232 DEBUG: 			View 3 : 0.662983425414
-2016-09-06 08:05:12,304 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:12,937 DEBUG: 		Start:	 Iteration 10
-2016-09-06 08:05:12,944 DEBUG: 			View 0 : 0.67955801105
-2016-09-06 08:05:12,952 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:12,959 DEBUG: 			View 2 : 0.629834254144
-2016-09-06 08:05:12,966 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:13,042 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:13,745 DEBUG: 		Start:	 Iteration 11
-2016-09-06 08:05:13,753 DEBUG: 			View 0 : 0.67955801105
-2016-09-06 08:05:13,760 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:13,768 DEBUG: 			View 2 : 0.640883977901
-2016-09-06 08:05:13,775 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:13,853 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:14,626 DEBUG: 		Start:	 Iteration 12
-2016-09-06 08:05:14,634 DEBUG: 			View 0 : 0.67955801105
-2016-09-06 08:05:14,641 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:14,648 DEBUG: 			View 2 : 0.640883977901
-2016-09-06 08:05:14,656 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:14,737 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:15,578 DEBUG: 		Start:	 Iteration 13
-2016-09-06 08:05:15,586 DEBUG: 			View 0 : 0.651933701657
-2016-09-06 08:05:15,593 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:15,601 DEBUG: 			View 2 : 0.640883977901
-2016-09-06 08:05:15,608 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:15,692 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:16,601 DEBUG: 		Start:	 Iteration 14
-2016-09-06 08:05:16,609 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:16,616 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:16,623 DEBUG: 			View 2 : 0.640883977901
-2016-09-06 08:05:16,631 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:16,717 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:17,696 DEBUG: 		Start:	 Iteration 15
-2016-09-06 08:05:17,704 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:17,711 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:17,718 DEBUG: 			View 2 : 0.651933701657
-2016-09-06 08:05:17,726 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:17,815 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:18,860 DEBUG: 		Start:	 Iteration 16
-2016-09-06 08:05:18,867 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:18,874 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:18,881 DEBUG: 			View 2 : 0.651933701657
-2016-09-06 08:05:18,889 DEBUG: 			View 3 : 0.646408839779
-2016-09-06 08:05:18,979 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:20,107 DEBUG: 		Start:	 Iteration 17
-2016-09-06 08:05:20,116 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:20,125 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:20,134 DEBUG: 			View 2 : 0.651933701657
-2016-09-06 08:05:20,143 DEBUG: 			View 3 : 0.662983425414
-2016-09-06 08:05:20,262 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:21,920 DEBUG: 		Start:	 Iteration 18
-2016-09-06 08:05:21,936 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:21,951 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:21,965 DEBUG: 			View 2 : 0.640883977901
-2016-09-06 08:05:21,980 DEBUG: 			View 3 : 0.662983425414
-2016-09-06 08:05:22,140 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:23,627 DEBUG: 		Start:	 Iteration 19
-2016-09-06 08:05:23,634 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:23,642 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:23,649 DEBUG: 			View 2 : 0.640883977901
-2016-09-06 08:05:23,657 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:23,756 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:25,105 DEBUG: 		Start:	 Iteration 20
-2016-09-06 08:05:25,113 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:25,121 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:25,128 DEBUG: 			View 2 : 0.530386740331
-2016-09-06 08:05:25,136 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:25,240 DEBUG: 			 Best view : 		View1
-2016-09-06 08:05:26,677 DEBUG: 		Start:	 Iteration 21
-2016-09-06 08:05:26,684 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:26,691 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:26,698 DEBUG: 			View 2 : 0.629834254144
-2016-09-06 08:05:26,705 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:26,808 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:28,293 DEBUG: 		Start:	 Iteration 22
-2016-09-06 08:05:28,301 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:28,309 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:28,316 DEBUG: 			View 2 : 0.530386740331
-2016-09-06 08:05:28,324 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:28,431 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:29,978 DEBUG: 		Start:	 Iteration 23
-2016-09-06 08:05:29,986 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:29,993 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:30,000 DEBUG: 			View 2 : 0.530386740331
-2016-09-06 08:05:30,008 DEBUG: 			View 3 : 0.662983425414
-2016-09-06 08:05:30,116 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:32,159 DEBUG: 		Start:	 Iteration 24
-2016-09-06 08:05:32,169 DEBUG: 			View 0 : 0.696132596685
-2016-09-06 08:05:32,178 DEBUG: 			View 1 : 0.635359116022
-2016-09-06 08:05:32,187 DEBUG: 			View 2 : 0.563535911602
-2016-09-06 08:05:32,196 DEBUG: 			View 3 : 0.662983425414
-2016-09-06 08:05:32,326 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:34,196 DEBUG: 		Start:	 Iteration 25
-2016-09-06 08:05:34,203 DEBUG: 			View 0 : 0.701657458564
-2016-09-06 08:05:34,211 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:34,218 DEBUG: 			View 2 : 0.563535911602
-2016-09-06 08:05:34,225 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:34,373 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:36,562 DEBUG: 		Start:	 Iteration 26
-2016-09-06 08:05:36,571 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 08:05:36,579 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:36,587 DEBUG: 			View 2 : 0.563535911602
-2016-09-06 08:05:36,594 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:36,737 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:38,930 DEBUG: 		Start:	 Iteration 27
-2016-09-06 08:05:38,944 DEBUG: 			View 0 : 0.71270718232
-2016-09-06 08:05:38,956 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:38,966 DEBUG: 			View 2 : 0.640883977901
-2016-09-06 08:05:38,981 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:39,216 DEBUG: 			 Best view : 		View0
-2016-09-06 08:05:41,369 DEBUG: 		Start:	 Iteration 28
-2016-09-06 08:05:41,379 DEBUG: 			View 0 : 0.71270718232
-2016-09-06 08:05:41,388 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 08:05:41,395 DEBUG: 			View 2 : 0.640883977901
-2016-09-06 08:05:41,403 DEBUG: 			View 3 : 0.602209944751
-2016-09-06 08:05:41,538 DEBUG: 			 Best view : 		View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f30c8b56..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9a76fd81..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0e3255b0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 28e5d951..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5e4e696a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.428571428571
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.428571428571
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e413d0c9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 41d1525b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6464
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9bf62b3e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4ec46b39..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6d7aad0d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f0877a51..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 40bdc179..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 47b35c55..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b12bd9f2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ce93eca4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 35982db3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6464
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 565ed561..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5f125732..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 128ee537..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 40c44397..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 26b43d37..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 226def6e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 350738bc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2cbc0923..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 730d5642..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 154fff3f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a00ae59d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ab0b361c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2b7800f3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 821287ca..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6231
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080507Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-KNN-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080507Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-KNN-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index c500f78a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080507Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-KNN-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 45.2380952381
-	-On Validation : 83.8202247191
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 31
-		- Random Forest with num_esimators : 26, max_depth : 23
-		- K nearest Neighbors with  n_neighbors: 31
-		- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080555-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-080555-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 32d8fc69..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080555-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1550 +0,0 @@
-2016-09-06 08:05:55,156 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:05:55,156 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 9.821875e-05 Gbytes /!\ 
-2016-09-06 08:06:00,166 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 08:06:00,167 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 08:06:00,212 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:00,212 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:00,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:06:00,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:06:00,213 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:00,213 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:00,214 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:00,214 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:00,214 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:00,214 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:00,214 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:00,214 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:00,214 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:00,214 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:00,274 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:00,275 DEBUG: Start:	 Training
-2016-09-06 08:06:00,277 DEBUG: Info:	 Time for Training: 0.0646359920502[s]
-2016-09-06 08:06:00,277 DEBUG: Done:	 Training
-2016-09-06 08:06:00,277 DEBUG: Start:	 Predicting
-2016-09-06 08:06:00,279 DEBUG: Done:	 Predicting
-2016-09-06 08:06:00,280 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:00,281 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:00,281 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:06:00,281 INFO: Done:	 Result Analysis
-2016-09-06 08:06:00,302 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:00,302 DEBUG: Start:	 Training
-2016-09-06 08:06:00,306 DEBUG: Info:	 Time for Training: 0.0938808917999[s]
-2016-09-06 08:06:00,306 DEBUG: Done:	 Training
-2016-09-06 08:06:00,306 DEBUG: Start:	 Predicting
-2016-09-06 08:06:00,309 DEBUG: Done:	 Predicting
-2016-09-06 08:06:00,309 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:00,310 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:00,311 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:00,311 INFO: Done:	 Result Analysis
-2016-09-06 08:06:00,462 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:00,462 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:00,462 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:06:00,462 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:06:00,463 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:00,463 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:00,463 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:00,463 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:00,463 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:00,463 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:00,464 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:00,464 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:00,464 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:00,464 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:00,516 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:00,517 DEBUG: Start:	 Training
-2016-09-06 08:06:00,517 DEBUG: Info:	 Time for Training: 0.0554449558258[s]
-2016-09-06 08:06:00,517 DEBUG: Done:	 Training
-2016-09-06 08:06:00,517 DEBUG: Start:	 Predicting
-2016-09-06 08:06:00,523 DEBUG: Done:	 Predicting
-2016-09-06 08:06:00,524 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:00,525 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:00,525 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 24
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:06:00,525 INFO: Done:	 Result Analysis
-2016-09-06 08:06:00,980 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:00,980 DEBUG: Start:	 Training
-2016-09-06 08:06:01,004 DEBUG: Info:	 Time for Training: 0.541980981827[s]
-2016-09-06 08:06:01,004 DEBUG: Done:	 Training
-2016-09-06 08:06:01,004 DEBUG: Start:	 Predicting
-2016-09-06 08:06:01,008 DEBUG: Done:	 Predicting
-2016-09-06 08:06:01,008 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:01,009 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:01,009 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 9, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,010 INFO: Done:	 Result Analysis
-2016-09-06 08:06:01,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,112 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:06:01,112 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:01,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,113 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:06:01,113 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:01,113 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:01,113 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:01,113 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:01,114 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:01,114 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:01,114 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,114 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:01,114 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,192 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:01,192 DEBUG: Start:	 Training
-2016-09-06 08:06:01,193 DEBUG: Info:	 Time for Training: 0.0819571018219[s]
-2016-09-06 08:06:01,193 DEBUG: Done:	 Training
-2016-09-06 08:06:01,193 DEBUG: Start:	 Predicting
-2016-09-06 08:06:01,203 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:01,203 DEBUG: Start:	 Training
-2016-09-06 08:06:01,210 DEBUG: Done:	 Predicting
-2016-09-06 08:06:01,210 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:01,211 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:01,212 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,212 INFO: Done:	 Result Analysis
-2016-09-06 08:06:01,225 DEBUG: Info:	 Time for Training: 0.113878011703[s]
-2016-09-06 08:06:01,226 DEBUG: Done:	 Training
-2016-09-06 08:06:01,226 DEBUG: Start:	 Predicting
-2016-09-06 08:06:01,229 DEBUG: Done:	 Predicting
-2016-09-06 08:06:01,229 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:01,231 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:01,231 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,231 INFO: Done:	 Result Analysis
-2016-09-06 08:06:01,351 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,351 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,351 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:06:01,351 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:06:01,351 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:01,352 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:01,352 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:01,352 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:01,352 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:01,352 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:01,352 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:01,352 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:01,352 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,352 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,445 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:01,445 DEBUG: Start:	 Training
-2016-09-06 08:06:01,454 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:01,454 DEBUG: Start:	 Training
-2016-09-06 08:06:01,464 DEBUG: Info:	 Time for Training: 0.113697052002[s]
-2016-09-06 08:06:01,465 DEBUG: Done:	 Training
-2016-09-06 08:06:01,465 DEBUG: Start:	 Predicting
-2016-09-06 08:06:01,471 DEBUG: Done:	 Predicting
-2016-09-06 08:06:01,471 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:01,472 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:01,473 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,473 INFO: Done:	 Result Analysis
-2016-09-06 08:06:01,474 DEBUG: Info:	 Time for Training: 0.123625040054[s]
-2016-09-06 08:06:01,474 DEBUG: Done:	 Training
-2016-09-06 08:06:01,474 DEBUG: Start:	 Predicting
-2016-09-06 08:06:01,479 DEBUG: Done:	 Predicting
-2016-09-06 08:06:01,479 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:01,480 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:01,480 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4367
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,480 INFO: Done:	 Result Analysis
-2016-09-06 08:06:01,598 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,598 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,598 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:06:01,598 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:06:01,599 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:01,599 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:01,599 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:01,599 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:01,599 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:01,599 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:01,599 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:01,599 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:01,599 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,599 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,653 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:01,653 DEBUG: Start:	 Training
-2016-09-06 08:06:01,655 DEBUG: Info:	 Time for Training: 0.0572459697723[s]
-2016-09-06 08:06:01,655 DEBUG: Done:	 Training
-2016-09-06 08:06:01,655 DEBUG: Start:	 Predicting
-2016-09-06 08:06:01,658 DEBUG: Done:	 Predicting
-2016-09-06 08:06:01,658 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:01,659 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:01,659 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,659 INFO: Done:	 Result Analysis
-2016-09-06 08:06:01,679 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:01,679 DEBUG: Start:	 Training
-2016-09-06 08:06:01,682 DEBUG: Info:	 Time for Training: 0.084260225296[s]
-2016-09-06 08:06:01,682 DEBUG: Done:	 Training
-2016-09-06 08:06:01,682 DEBUG: Start:	 Predicting
-2016-09-06 08:06:01,685 DEBUG: Done:	 Predicting
-2016-09-06 08:06:01,685 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:01,687 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:01,687 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,687 INFO: Done:	 Result Analysis
-2016-09-06 08:06:01,749 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,749 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,749 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:06:01,749 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:06:01,749 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:01,749 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:01,750 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:01,750 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:01,750 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:01,750 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:01,750 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:01,750 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:01,750 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,750 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,825 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:01,825 DEBUG: Start:	 Training
-2016-09-06 08:06:01,826 DEBUG: Info:	 Time for Training: 0.0780990123749[s]
-2016-09-06 08:06:01,826 DEBUG: Done:	 Training
-2016-09-06 08:06:01,826 DEBUG: Start:	 Predicting
-2016-09-06 08:06:01,832 DEBUG: Done:	 Predicting
-2016-09-06 08:06:01,832 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:01,833 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:01,833 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 24
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,833 INFO: Done:	 Result Analysis
-2016-09-06 08:06:02,371 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:02,371 DEBUG: Start:	 Training
-2016-09-06 08:06:02,435 DEBUG: Info:	 Time for Training: 0.687300920486[s]
-2016-09-06 08:06:02,435 DEBUG: Done:	 Training
-2016-09-06 08:06:02,435 DEBUG: Start:	 Predicting
-2016-09-06 08:06:02,443 DEBUG: Done:	 Predicting
-2016-09-06 08:06:02,443 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:02,444 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:02,444 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 24, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:02,444 INFO: Done:	 Result Analysis
-2016-09-06 08:06:02,609 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:02,610 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:06:02,610 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:02,610 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:02,611 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:02,611 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:02,611 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:02,617 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:02,617 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:06:02,617 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:02,618 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:02,618 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:02,618 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:02,618 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:02,739 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:02,739 DEBUG: Start:	 Training
-2016-09-06 08:06:02,740 DEBUG: Info:	 Time for Training: 0.131755828857[s]
-2016-09-06 08:06:02,741 DEBUG: Done:	 Training
-2016-09-06 08:06:02,741 DEBUG: Start:	 Predicting
-2016-09-06 08:06:02,794 DEBUG: Done:	 Predicting
-2016-09-06 08:06:02,794 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:02,796 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:02,796 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:06:02,796 INFO: Done:	 Result Analysis
-2016-09-06 08:06:02,802 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:02,803 DEBUG: Start:	 Training
-2016-09-06 08:06:02,833 DEBUG: Info:	 Time for Training: 0.216584920883[s]
-2016-09-06 08:06:02,833 DEBUG: Done:	 Training
-2016-09-06 08:06:02,833 DEBUG: Start:	 Predicting
-2016-09-06 08:06:02,838 DEBUG: Done:	 Predicting
-2016-09-06 08:06:02,838 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:02,840 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:02,840 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:06:02,841 INFO: Done:	 Result Analysis
-2016-09-06 08:06:02,958 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:02,958 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:06:02,958 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:02,958 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:02,958 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:06:02,958 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:02,959 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:02,959 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:02,959 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:02,959 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:02,959 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:02,959 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:02,959 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:02,959 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:03,046 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:03,046 DEBUG: Start:	 Training
-2016-09-06 08:06:03,064 DEBUG: Info:	 Time for Training: 0.106878042221[s]
-2016-09-06 08:06:03,064 DEBUG: Done:	 Training
-2016-09-06 08:06:03,065 DEBUG: Start:	 Predicting
-2016-09-06 08:06:03,071 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:03,071 DEBUG: Start:	 Training
-2016-09-06 08:06:03,071 DEBUG: Done:	 Predicting
-2016-09-06 08:06:03,071 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:03,073 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:03,073 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:06:03,073 INFO: Done:	 Result Analysis
-2016-09-06 08:06:03,092 DEBUG: Info:	 Time for Training: 0.135026931763[s]
-2016-09-06 08:06:03,093 DEBUG: Done:	 Training
-2016-09-06 08:06:03,093 DEBUG: Start:	 Predicting
-2016-09-06 08:06:03,096 DEBUG: Done:	 Predicting
-2016-09-06 08:06:03,097 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:03,098 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:03,098 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:06:03,098 INFO: Done:	 Result Analysis
-2016-09-06 08:06:03,202 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:03,202 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:03,202 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:06:03,203 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:06:03,203 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:03,203 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:03,203 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:03,203 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:03,203 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:03,203 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:03,203 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:03,204 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:03,204 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:03,204 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:03,262 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:03,262 DEBUG: Start:	 Training
-2016-09-06 08:06:03,263 DEBUG: Info:	 Time for Training: 0.0617170333862[s]
-2016-09-06 08:06:03,264 DEBUG: Done:	 Training
-2016-09-06 08:06:03,264 DEBUG: Start:	 Predicting
-2016-09-06 08:06:03,267 DEBUG: Done:	 Predicting
-2016-09-06 08:06:03,267 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:03,268 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:03,268 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:06:03,268 INFO: Done:	 Result Analysis
-2016-09-06 08:06:03,306 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:03,306 DEBUG: Start:	 Training
-2016-09-06 08:06:03,310 DEBUG: Info:	 Time for Training: 0.108617067337[s]
-2016-09-06 08:06:03,311 DEBUG: Done:	 Training
-2016-09-06 08:06:03,311 DEBUG: Start:	 Predicting
-2016-09-06 08:06:03,314 DEBUG: Done:	 Predicting
-2016-09-06 08:06:03,314 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:03,316 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:03,316 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:06:03,316 INFO: Done:	 Result Analysis
-2016-09-06 08:06:03,447 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:03,447 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:03,447 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:06:03,447 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:06:03,447 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:03,447 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:03,448 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:03,448 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:03,448 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:03,448 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:03,448 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:03,448 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:03,448 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:03,448 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:03,502 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:03,502 DEBUG: Start:	 Training
-2016-09-06 08:06:03,502 DEBUG: Info:	 Time for Training: 0.0562369823456[s]
-2016-09-06 08:06:03,502 DEBUG: Done:	 Training
-2016-09-06 08:06:03,503 DEBUG: Start:	 Predicting
-2016-09-06 08:06:03,508 DEBUG: Done:	 Predicting
-2016-09-06 08:06:03,509 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:03,510 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:03,510 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 24
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:06:03,510 INFO: Done:	 Result Analysis
-2016-09-06 08:06:03,974 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:03,974 DEBUG: Start:	 Training
-2016-09-06 08:06:03,999 DEBUG: Info:	 Time for Training: 0.552613019943[s]
-2016-09-06 08:06:03,999 DEBUG: Done:	 Training
-2016-09-06 08:06:03,999 DEBUG: Start:	 Predicting
-2016-09-06 08:06:04,003 DEBUG: Done:	 Predicting
-2016-09-06 08:06:04,004 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:04,005 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:04,005 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 9, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,005 INFO: Done:	 Result Analysis
-2016-09-06 08:06:04,092 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,092 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,092 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:06:04,092 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:06:04,092 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:04,092 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:04,093 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:04,093 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:04,093 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:04,093 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:04,093 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:04,093 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:04,093 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,093 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,213 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:04,213 DEBUG: Start:	 Training
-2016-09-06 08:06:04,215 DEBUG: Info:	 Time for Training: 0.123574018478[s]
-2016-09-06 08:06:04,215 DEBUG: Done:	 Training
-2016-09-06 08:06:04,215 DEBUG: Start:	 Predicting
-2016-09-06 08:06:04,223 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:04,223 DEBUG: Start:	 Training
-2016-09-06 08:06:04,238 DEBUG: Done:	 Predicting
-2016-09-06 08:06:04,238 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:04,240 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:04,240 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,240 INFO: Done:	 Result Analysis
-2016-09-06 08:06:04,241 DEBUG: Info:	 Time for Training: 0.150360107422[s]
-2016-09-06 08:06:04,242 DEBUG: Done:	 Training
-2016-09-06 08:06:04,242 DEBUG: Start:	 Predicting
-2016-09-06 08:06:04,245 DEBUG: Done:	 Predicting
-2016-09-06 08:06:04,245 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:04,246 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:04,246 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,246 INFO: Done:	 Result Analysis
-2016-09-06 08:06:04,334 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,334 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,334 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:06:04,334 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:06:04,334 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:04,334 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:04,335 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:04,335 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:04,335 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:04,335 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:04,335 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:04,335 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:04,335 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,335 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,422 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:04,422 DEBUG: Start:	 Training
-2016-09-06 08:06:04,428 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:04,428 DEBUG: Start:	 Training
-2016-09-06 08:06:04,441 DEBUG: Info:	 Time for Training: 0.107517957687[s]
-2016-09-06 08:06:04,441 DEBUG: Done:	 Training
-2016-09-06 08:06:04,441 DEBUG: Start:	 Predicting
-2016-09-06 08:06:04,447 DEBUG: Done:	 Predicting
-2016-09-06 08:06:04,448 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:04,449 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:04,449 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,449 DEBUG: Info:	 Time for Training: 0.115609884262[s]
-2016-09-06 08:06:04,449 INFO: Done:	 Result Analysis
-2016-09-06 08:06:04,449 DEBUG: Done:	 Training
-2016-09-06 08:06:04,449 DEBUG: Start:	 Predicting
-2016-09-06 08:06:04,453 DEBUG: Done:	 Predicting
-2016-09-06 08:06:04,453 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:04,454 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:04,454 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.980952380952
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,454 INFO: Done:	 Result Analysis
-2016-09-06 08:06:04,581 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,581 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,581 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:06:04,581 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:06:04,581 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:04,581 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:04,582 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:04,582 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:04,582 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:04,582 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:04,582 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:04,582 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:04,582 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,582 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,665 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:04,665 DEBUG: Start:	 Training
-2016-09-06 08:06:04,667 DEBUG: Info:	 Time for Training: 0.0870370864868[s]
-2016-09-06 08:06:04,667 DEBUG: Done:	 Training
-2016-09-06 08:06:04,667 DEBUG: Start:	 Predicting
-2016-09-06 08:06:04,671 DEBUG: Done:	 Predicting
-2016-09-06 08:06:04,671 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:04,673 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:04,673 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,673 INFO: Done:	 Result Analysis
-2016-09-06 08:06:04,696 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:04,696 DEBUG: Start:	 Training
-2016-09-06 08:06:04,700 DEBUG: Info:	 Time for Training: 0.119526147842[s]
-2016-09-06 08:06:04,700 DEBUG: Done:	 Training
-2016-09-06 08:06:04,700 DEBUG: Start:	 Predicting
-2016-09-06 08:06:04,703 DEBUG: Done:	 Predicting
-2016-09-06 08:06:04,703 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:04,705 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:04,705 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,705 INFO: Done:	 Result Analysis
-2016-09-06 08:06:04,825 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,825 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,825 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:06:04,825 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:06:04,826 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:04,826 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:04,826 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:04,826 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:04,827 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:04,827 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:04,827 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:04,827 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:04,827 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,827 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,910 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:04,910 DEBUG: Start:	 Training
-2016-09-06 08:06:04,911 DEBUG: Info:	 Time for Training: 0.0860750675201[s]
-2016-09-06 08:06:04,911 DEBUG: Done:	 Training
-2016-09-06 08:06:04,911 DEBUG: Start:	 Predicting
-2016-09-06 08:06:04,920 DEBUG: Done:	 Predicting
-2016-09-06 08:06:04,920 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:04,921 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:04,922 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,922 INFO: Done:	 Result Analysis
-2016-09-06 08:06:05,414 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:05,414 DEBUG: Start:	 Training
-2016-09-06 08:06:05,476 DEBUG: Info:	 Time for Training: 0.651627063751[s]
-2016-09-06 08:06:05,476 DEBUG: Done:	 Training
-2016-09-06 08:06:05,476 DEBUG: Start:	 Predicting
-2016-09-06 08:06:05,484 DEBUG: Done:	 Predicting
-2016-09-06 08:06:05,484 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:05,485 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:05,485 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 24, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 08:06:05,485 INFO: Done:	 Result Analysis
-2016-09-06 08:06:05,580 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:05,580 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:05,581 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:06:05,581 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:06:05,581 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:05,581 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:06:05,582 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:05,582 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:05,582 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:05,582 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:05,583 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:05,583 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:06:05,583 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:05,583 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:06:05,697 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:05,697 DEBUG: Start:	 Training
-2016-09-06 08:06:05,698 DEBUG: Info:	 Time for Training: 0.118808031082[s]
-2016-09-06 08:06:05,698 DEBUG: Done:	 Training
-2016-09-06 08:06:05,698 DEBUG: Start:	 Predicting
-2016-09-06 08:06:05,706 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:06:05,706 DEBUG: Start:	 Training
-2016-09-06 08:06:05,725 DEBUG: Done:	 Predicting
-2016-09-06 08:06:05,725 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:05,726 DEBUG: Info:	 Time for Training: 0.146739006042[s]
-2016-09-06 08:06:05,726 DEBUG: Done:	 Training
-2016-09-06 08:06:05,726 DEBUG: Start:	 Predicting
-2016-09-06 08:06:05,727 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:05,727 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:06:05,727 INFO: Done:	 Result Analysis
-2016-09-06 08:06:05,729 DEBUG: Done:	 Predicting
-2016-09-06 08:06:05,729 DEBUG: Start:	 Getting Results
-2016-09-06 08:06:05,730 DEBUG: Done:	 Getting Results
-2016-09-06 08:06:05,731 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:06:05,731 INFO: Done:	 Result Analysis
-2016-09-06 08:06:05,968 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:06:05,968 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:06:05,969 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:06:05,969 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:06:05,969 INFO: Info:	 Shape of View1 :(300, 8)
-2016-09-06 08:06:05,969 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:06:05,970 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 08:06:05,970 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:06:05,970 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 08:06:05,970 INFO: Info:	 Shape of View1 :(300, 8)
-2016-09-06 08:06:05,970 INFO: Done:	 Read Database Files
-2016-09-06 08:06:05,970 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:06:05,971 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 08:06:05,971 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 08:06:05,971 INFO: Done:	 Read Database Files
-2016-09-06 08:06:05,971 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:06:05,974 INFO: Done:	 Determine validation split
-2016-09-06 08:06:05,974 INFO: Start:	 Determine 5 folds
-2016-09-06 08:06:05,975 INFO: Done:	 Determine validation split
-2016-09-06 08:06:05,975 INFO: Start:	 Determine 5 folds
-2016-09-06 08:06:05,982 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:06:05,983 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:06:05,983 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:06:05,983 INFO: Done:	 Determine folds
-2016-09-06 08:06:05,983 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 08:06:05,983 INFO: Start:	 Classification
-2016-09-06 08:06:05,983 INFO: 	Start:	 Fold number 1
-2016-09-06 08:06:05,985 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:06:05,985 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:06:05,985 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:06:05,985 INFO: Done:	 Determine folds
-2016-09-06 08:06:05,985 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:06:05,985 INFO: Start:	 Classification
-2016-09-06 08:06:05,985 INFO: 	Start:	 Fold number 1
-2016-09-06 08:06:06,069 INFO: 	Start: 	 Classification
-2016-09-06 08:06:06,144 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:06:06,144 INFO: 	Start:	 Fold number 2
-2016-09-06 08:06:06,223 INFO: 	Start: 	 Classification
-2016-09-06 08:06:06,299 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:06:06,299 INFO: 	Start:	 Fold number 3
-2016-09-06 08:06:06,379 INFO: 	Start: 	 Classification
-2016-09-06 08:06:06,454 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:06:06,454 INFO: 	Start:	 Fold number 4
-2016-09-06 08:06:06,531 INFO: 	Start: 	 Classification
-2016-09-06 08:06:06,601 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:06:06,601 INFO: 	Start:	 Fold number 5
-2016-09-06 08:06:06,676 INFO: 	Start: 	 Classification
-2016-09-06 08:06:06,745 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:06:06,745 INFO: Done:	 Classification
-2016-09-06 08:06:06,745 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:06:06,745 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:06:06,751 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 54.2857142857
-	-On Validation : 86.2921348315
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-		- SVM Linear with C : 9141
-		- SVM Linear with C : 9141
-		- SVM Linear with C : 9141
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:06:06,751 INFO: Done:	 Result Analysis
-2016-09-06 08:06:07,997 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:06:08,005 DEBUG: 			View 0 : 0.491803278689
-2016-09-06 08:06:08,012 DEBUG: 			View 1 : 0.48087431694
-2016-09-06 08:06:08,019 DEBUG: 			View 2 : 0.535519125683
-2016-09-06 08:06:08,026 DEBUG: 			View 3 : 0.497267759563
-2016-09-06 08:06:08,073 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:08,157 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:06:08,165 DEBUG: 			View 0 : 0.633879781421
-2016-09-06 08:06:08,173 DEBUG: 			View 1 : 0.655737704918
-2016-09-06 08:06:08,180 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:08,187 DEBUG: 			View 3 : 0.710382513661
-2016-09-06 08:06:08,246 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:08,399 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:06:08,407 DEBUG: 			View 0 : 0.633879781421
-2016-09-06 08:06:08,414 DEBUG: 			View 1 : 0.655737704918
-2016-09-06 08:06:08,422 DEBUG: 			View 2 : 0.72131147541
-2016-09-06 08:06:08,429 DEBUG: 			View 3 : 0.710382513661
-2016-09-06 08:06:08,487 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:08,711 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:06:08,719 DEBUG: 			View 0 : 0.650273224044
-2016-09-06 08:06:08,726 DEBUG: 			View 1 : 0.639344262295
-2016-09-06 08:06:08,733 DEBUG: 			View 2 : 0.72131147541
-2016-09-06 08:06:08,740 DEBUG: 			View 3 : 0.688524590164
-2016-09-06 08:06:08,801 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:09,093 DEBUG: 		Start:	 Iteration 5
-2016-09-06 08:06:09,100 DEBUG: 			View 0 : 0.650273224044
-2016-09-06 08:06:09,107 DEBUG: 			View 1 : 0.672131147541
-2016-09-06 08:06:09,114 DEBUG: 			View 2 : 0.644808743169
-2016-09-06 08:06:09,121 DEBUG: 			View 3 : 0.672131147541
-2016-09-06 08:06:09,185 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:09,547 DEBUG: 		Start:	 Iteration 6
-2016-09-06 08:06:09,555 DEBUG: 			View 0 : 0.644808743169
-2016-09-06 08:06:09,562 DEBUG: 			View 1 : 0.677595628415
-2016-09-06 08:06:09,569 DEBUG: 			View 2 : 0.644808743169
-2016-09-06 08:06:09,577 DEBUG: 			View 3 : 0.672131147541
-2016-09-06 08:06:09,649 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:10,082 DEBUG: 		Start:	 Iteration 7
-2016-09-06 08:06:10,089 DEBUG: 			View 0 : 0.644808743169
-2016-09-06 08:06:10,096 DEBUG: 			View 1 : 0.644808743169
-2016-09-06 08:06:10,103 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:10,110 DEBUG: 			View 3 : 0.650273224044
-2016-09-06 08:06:10,179 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:10,679 DEBUG: 		Start:	 Iteration 8
-2016-09-06 08:06:10,687 DEBUG: 			View 0 : 0.672131147541
-2016-09-06 08:06:10,694 DEBUG: 			View 1 : 0.644808743169
-2016-09-06 08:06:10,700 DEBUG: 			View 2 : 0.726775956284
-2016-09-06 08:06:10,708 DEBUG: 			View 3 : 0.68306010929
-2016-09-06 08:06:10,780 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:11,369 DEBUG: 		Start:	 Iteration 9
-2016-09-06 08:06:11,376 DEBUG: 			View 0 : 0.672131147541
-2016-09-06 08:06:11,384 DEBUG: 			View 1 : 0.655737704918
-2016-09-06 08:06:11,391 DEBUG: 			View 2 : 0.726775956284
-2016-09-06 08:06:11,397 DEBUG: 			View 3 : 0.710382513661
-2016-09-06 08:06:11,471 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:12,105 DEBUG: 		Start:	 Iteration 10
-2016-09-06 08:06:12,113 DEBUG: 			View 0 : 0.655737704918
-2016-09-06 08:06:12,120 DEBUG: 			View 1 : 0.655737704918
-2016-09-06 08:06:12,127 DEBUG: 			View 2 : 0.650273224044
-2016-09-06 08:06:12,134 DEBUG: 			View 3 : 0.710382513661
-2016-09-06 08:06:12,210 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:12,922 DEBUG: 		Start:	 Iteration 11
-2016-09-06 08:06:12,929 DEBUG: 			View 0 : 0.633879781421
-2016-09-06 08:06:12,936 DEBUG: 			View 1 : 0.655737704918
-2016-09-06 08:06:12,944 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:12,950 DEBUG: 			View 3 : 0.710382513661
-2016-09-06 08:06:13,034 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:13,809 DEBUG: 		Start:	 Iteration 12
-2016-09-06 08:06:13,816 DEBUG: 			View 0 : 0.633879781421
-2016-09-06 08:06:13,823 DEBUG: 			View 1 : 0.622950819672
-2016-09-06 08:06:13,830 DEBUG: 			View 2 : 0.661202185792
-2016-09-06 08:06:13,837 DEBUG: 			View 3 : 0.688524590164
-2016-09-06 08:06:13,918 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:14,758 DEBUG: 		Start:	 Iteration 13
-2016-09-06 08:06:14,765 DEBUG: 			View 0 : 0.633879781421
-2016-09-06 08:06:14,772 DEBUG: 			View 1 : 0.622950819672
-2016-09-06 08:06:14,780 DEBUG: 			View 2 : 0.661202185792
-2016-09-06 08:06:14,788 DEBUG: 			View 3 : 0.666666666667
-2016-09-06 08:06:14,872 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:15,801 DEBUG: 		Start:	 Iteration 14
-2016-09-06 08:06:15,808 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:06:15,815 DEBUG: 			View 1 : 0.639344262295
-2016-09-06 08:06:15,822 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:15,829 DEBUG: 			View 3 : 0.672131147541
-2016-09-06 08:06:15,916 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:16,895 DEBUG: 		Start:	 Iteration 15
-2016-09-06 08:06:16,902 DEBUG: 			View 0 : 0.639344262295
-2016-09-06 08:06:16,909 DEBUG: 			View 1 : 0.639344262295
-2016-09-06 08:06:16,917 DEBUG: 			View 2 : 0.644808743169
-2016-09-06 08:06:16,924 DEBUG: 			View 3 : 0.677595628415
-2016-09-06 08:06:17,013 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:18,061 DEBUG: 		Start:	 Iteration 16
-2016-09-06 08:06:18,069 DEBUG: 			View 0 : 0.639344262295
-2016-09-06 08:06:18,075 DEBUG: 			View 1 : 0.639344262295
-2016-09-06 08:06:18,083 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:18,089 DEBUG: 			View 3 : 0.693989071038
-2016-09-06 08:06:18,182 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:19,305 DEBUG: 		Start:	 Iteration 17
-2016-09-06 08:06:19,312 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:06:19,320 DEBUG: 			View 1 : 0.644808743169
-2016-09-06 08:06:19,326 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:19,333 DEBUG: 			View 3 : 0.710382513661
-2016-09-06 08:06:19,427 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:20,613 DEBUG: 		Start:	 Iteration 18
-2016-09-06 08:06:20,620 DEBUG: 			View 0 : 0.633879781421
-2016-09-06 08:06:20,627 DEBUG: 			View 1 : 0.644808743169
-2016-09-06 08:06:20,634 DEBUG: 			View 2 : 0.644808743169
-2016-09-06 08:06:20,641 DEBUG: 			View 3 : 0.68306010929
-2016-09-06 08:06:20,741 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:22,010 DEBUG: 		Start:	 Iteration 19
-2016-09-06 08:06:22,017 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:06:22,024 DEBUG: 			View 1 : 0.655737704918
-2016-09-06 08:06:22,031 DEBUG: 			View 2 : 0.644808743169
-2016-09-06 08:06:22,038 DEBUG: 			View 3 : 0.661202185792
-2016-09-06 08:06:22,142 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:23,499 DEBUG: 		Start:	 Iteration 20
-2016-09-06 08:06:23,508 DEBUG: 			View 0 : 0.590163934426
-2016-09-06 08:06:23,516 DEBUG: 			View 1 : 0.655737704918
-2016-09-06 08:06:23,527 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:23,536 DEBUG: 			View 3 : 0.688524590164
-2016-09-06 08:06:23,672 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:25,199 DEBUG: 		Start:	 Iteration 21
-2016-09-06 08:06:25,213 DEBUG: 			View 0 : 0.590163934426
-2016-09-06 08:06:25,227 DEBUG: 			View 1 : 0.655737704918
-2016-09-06 08:06:25,241 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:25,255 DEBUG: 			View 3 : 0.672131147541
-2016-09-06 08:06:25,398 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:27,290 DEBUG: 		Start:	 Iteration 22
-2016-09-06 08:06:27,298 DEBUG: 			View 0 : 0.628415300546
-2016-09-06 08:06:27,306 DEBUG: 			View 1 : 0.639344262295
-2016-09-06 08:06:27,314 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:27,321 DEBUG: 			View 3 : 0.672131147541
-2016-09-06 08:06:27,439 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:29,011 DEBUG: 		Start:	 Iteration 23
-2016-09-06 08:06:29,020 DEBUG: 			View 0 : 0.628415300546
-2016-09-06 08:06:29,027 DEBUG: 			View 1 : 0.644808743169
-2016-09-06 08:06:29,035 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:29,042 DEBUG: 			View 3 : 0.677595628415
-2016-09-06 08:06:29,157 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:30,789 DEBUG: 		Start:	 Iteration 24
-2016-09-06 08:06:30,797 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:06:30,805 DEBUG: 			View 1 : 0.633879781421
-2016-09-06 08:06:30,813 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:30,821 DEBUG: 			View 3 : 0.710382513661
-2016-09-06 08:06:30,938 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:32,645 DEBUG: 		Start:	 Iteration 25
-2016-09-06 08:06:32,653 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:06:32,661 DEBUG: 			View 1 : 0.633879781421
-2016-09-06 08:06:32,668 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:32,676 DEBUG: 			View 3 : 0.704918032787
-2016-09-06 08:06:32,792 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:34,560 DEBUG: 		Start:	 Iteration 26
-2016-09-06 08:06:34,568 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:06:34,576 DEBUG: 			View 1 : 0.661202185792
-2016-09-06 08:06:34,583 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:34,590 DEBUG: 			View 3 : 0.704918032787
-2016-09-06 08:06:34,708 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:36,580 DEBUG: 		Start:	 Iteration 27
-2016-09-06 08:06:36,588 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:06:36,596 DEBUG: 			View 1 : 0.661202185792
-2016-09-06 08:06:36,603 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:36,611 DEBUG: 			View 3 : 0.693989071038
-2016-09-06 08:06:36,738 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:38,662 DEBUG: 		Start:	 Iteration 28
-2016-09-06 08:06:38,670 DEBUG: 			View 0 : 0.590163934426
-2016-09-06 08:06:38,677 DEBUG: 			View 1 : 0.639344262295
-2016-09-06 08:06:38,684 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:38,691 DEBUG: 			View 3 : 0.650273224044
-2016-09-06 08:06:38,818 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:40,822 DEBUG: 		Start:	 Iteration 29
-2016-09-06 08:06:40,830 DEBUG: 			View 0 : 0.639344262295
-2016-09-06 08:06:40,837 DEBUG: 			View 1 : 0.633879781421
-2016-09-06 08:06:40,844 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:40,852 DEBUG: 			View 3 : 0.650273224044
-2016-09-06 08:06:40,982 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:43,053 DEBUG: 		Start:	 Iteration 30
-2016-09-06 08:06:43,061 DEBUG: 			View 0 : 0.639344262295
-2016-09-06 08:06:43,097 DEBUG: 			View 1 : 0.633879781421
-2016-09-06 08:06:43,105 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:43,112 DEBUG: 			View 3 : 0.693989071038
-2016-09-06 08:06:43,245 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:45,351 DEBUG: 		Start:	 Iteration 31
-2016-09-06 08:06:45,359 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:06:45,366 DEBUG: 			View 1 : 0.633879781421
-2016-09-06 08:06:45,373 DEBUG: 			View 2 : 0.650273224044
-2016-09-06 08:06:45,380 DEBUG: 			View 3 : 0.710382513661
-2016-09-06 08:06:45,563 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:47,829 DEBUG: 		Start:	 Iteration 32
-2016-09-06 08:06:47,836 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:06:47,843 DEBUG: 			View 1 : 0.661202185792
-2016-09-06 08:06:47,851 DEBUG: 			View 2 : 0.650273224044
-2016-09-06 08:06:47,858 DEBUG: 			View 3 : 0.704918032787
-2016-09-06 08:06:48,005 DEBUG: 			 Best view : 		View2
-2016-09-06 08:06:50,295 DEBUG: 		Start:	 Iteration 33
-2016-09-06 08:06:50,302 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:06:50,310 DEBUG: 			View 1 : 0.661202185792
-2016-09-06 08:06:50,317 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:50,324 DEBUG: 			View 3 : 0.688524590164
-2016-09-06 08:06:50,457 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:52,732 DEBUG: 		Start:	 Iteration 34
-2016-09-06 08:06:52,740 DEBUG: 			View 0 : 0.590163934426
-2016-09-06 08:06:52,747 DEBUG: 			View 1 : 0.644808743169
-2016-09-06 08:06:52,755 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:52,763 DEBUG: 			View 3 : 0.693989071038
-2016-09-06 08:06:52,916 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:55,509 DEBUG: 		Start:	 Iteration 35
-2016-09-06 08:06:55,516 DEBUG: 			View 0 : 0.590163934426
-2016-09-06 08:06:55,524 DEBUG: 			View 1 : 0.644808743169
-2016-09-06 08:06:55,531 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:55,538 DEBUG: 			View 3 : 0.672131147541
-2016-09-06 08:06:55,678 DEBUG: 			 Best view : 		View3
-2016-09-06 08:06:58,112 DEBUG: 		Start:	 Iteration 36
-2016-09-06 08:06:58,119 DEBUG: 			View 0 : 0.590163934426
-2016-09-06 08:06:58,127 DEBUG: 			View 1 : 0.633879781421
-2016-09-06 08:06:58,133 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:06:58,140 DEBUG: 			View 3 : 0.672131147541
-2016-09-06 08:06:58,287 DEBUG: 			 Best view : 		View3
-2016-09-06 08:07:00,814 DEBUG: 		Start:	 Iteration 37
-2016-09-06 08:07:00,822 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:07:00,829 DEBUG: 			View 1 : 0.639344262295
-2016-09-06 08:07:00,836 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:07:00,843 DEBUG: 			View 3 : 0.672131147541
-2016-09-06 08:07:01,100 DEBUG: 			 Best view : 		View3
-2016-09-06 08:07:04,473 DEBUG: 		Start:	 Iteration 38
-2016-09-06 08:07:04,485 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:07:04,498 DEBUG: 			View 1 : 0.661202185792
-2016-09-06 08:07:04,511 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:07:04,523 DEBUG: 			View 3 : 0.68306010929
-2016-09-06 08:07:04,691 DEBUG: 			 Best view : 		View3
-2016-09-06 08:07:07,716 DEBUG: 		Start:	 Iteration 39
-2016-09-06 08:07:07,723 DEBUG: 			View 0 : 0.628415300546
-2016-09-06 08:07:07,731 DEBUG: 			View 1 : 0.655737704918
-2016-09-06 08:07:07,738 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:07:07,745 DEBUG: 			View 3 : 0.677595628415
-2016-09-06 08:07:07,897 DEBUG: 			 Best view : 		View3
-2016-09-06 08:07:10,829 DEBUG: 		Start:	 Iteration 40
-2016-09-06 08:07:10,836 DEBUG: 			View 0 : 0.628415300546
-2016-09-06 08:07:10,843 DEBUG: 			View 1 : 0.633879781421
-2016-09-06 08:07:10,851 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:07:10,858 DEBUG: 			View 3 : 0.704918032787
-2016-09-06 08:07:11,018 DEBUG: 			 Best view : 		View3
-2016-09-06 08:07:14,057 DEBUG: 		Start:	 Iteration 41
-2016-09-06 08:07:14,066 DEBUG: 			View 0 : 0.622950819672
-2016-09-06 08:07:14,074 DEBUG: 			View 1 : 0.633879781421
-2016-09-06 08:07:14,081 DEBUG: 			View 2 : 0.655737704918
-2016-09-06 08:07:14,088 DEBUG: 			View 3 : 0.677595628415
-2016-09-06 08:07:14,253 DEBUG: 			 Best view : 		View3
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8e044ac9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3c3ec1a1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5344b790..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 24
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d80134df..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b3a3a6b2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fd0eb02b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 24
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1aecca62..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 9, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bace8efd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0ac6d6d2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4502c840..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4367
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2af377a8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index da1e51db..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 24, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4e9bf54f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 44dcaf4d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 14e81ebc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1648788e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f3a622e2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 24
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index aba772dc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 9, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 19891ede..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index eba1f504..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5138179a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d42d8911..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 57f49301..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 68ccb89b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bd049f03..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index efb8261d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.980952380952
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b069286b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cc9e53b0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 24, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5c02b6e0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d70b02cb..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9141
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080606Results-Fusion-LateFusion-BayesianInference-Adaboost-SVMLinear-SVMLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080606Results-Fusion-LateFusion-BayesianInference-Adaboost-SVMLinear-SVMLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index e95028d1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080606Results-Fusion-LateFusion-BayesianInference-Adaboost-SVMLinear-SVMLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 54.2857142857
-	-On Validation : 86.2921348315
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-		- SVM Linear with C : 9141
-		- SVM Linear with C : 9141
-		- SVM Linear with C : 9141
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080717-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-080717-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index a4e3af56..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080717-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1762 +0,0 @@
-2016-09-06 08:07:17,026 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:07:17,027 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00012634375 Gbytes /!\ 
-2016-09-06 08:07:22,035 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 08:07:22,037 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 08:07:22,084 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,084 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,084 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:07:22,084 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:07:22,084 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:22,084 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:22,085 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,085 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,085 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,085 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:22,085 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,085 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,085 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:22,085 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,145 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:22,145 DEBUG: Start:	 Training
-2016-09-06 08:07:22,147 DEBUG: Info:	 Time for Training: 0.0636520385742[s]
-2016-09-06 08:07:22,147 DEBUG: Done:	 Training
-2016-09-06 08:07:22,147 DEBUG: Start:	 Predicting
-2016-09-06 08:07:22,150 DEBUG: Done:	 Predicting
-2016-09-06 08:07:22,150 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:22,151 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:22,151 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,151 INFO: Done:	 Result Analysis
-2016-09-06 08:07:22,172 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:22,172 DEBUG: Start:	 Training
-2016-09-06 08:07:22,176 DEBUG: Info:	 Time for Training: 0.092915058136[s]
-2016-09-06 08:07:22,176 DEBUG: Done:	 Training
-2016-09-06 08:07:22,177 DEBUG: Start:	 Predicting
-2016-09-06 08:07:22,179 DEBUG: Done:	 Predicting
-2016-09-06 08:07:22,179 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:22,181 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:22,181 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,181 INFO: Done:	 Result Analysis
-2016-09-06 08:07:22,335 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,335 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,335 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:07:22,335 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:07:22,336 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:22,336 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:22,336 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,336 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,336 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,336 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,336 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:22,336 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:22,337 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,337 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,393 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:22,393 DEBUG: Start:	 Training
-2016-09-06 08:07:22,394 DEBUG: Info:	 Time for Training: 0.0591959953308[s]
-2016-09-06 08:07:22,394 DEBUG: Done:	 Training
-2016-09-06 08:07:22,394 DEBUG: Start:	 Predicting
-2016-09-06 08:07:22,402 DEBUG: Done:	 Predicting
-2016-09-06 08:07:22,402 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:22,403 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:22,403 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.661904761905
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,403 INFO: Done:	 Result Analysis
-2016-09-06 08:07:22,641 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:22,641 DEBUG: Start:	 Training
-2016-09-06 08:07:22,660 DEBUG: Info:	 Time for Training: 0.32483792305[s]
-2016-09-06 08:07:22,660 DEBUG: Done:	 Training
-2016-09-06 08:07:22,660 DEBUG: Start:	 Predicting
-2016-09-06 08:07:22,664 DEBUG: Done:	 Predicting
-2016-09-06 08:07:22,664 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:22,665 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:22,665 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.952380952381
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,665 INFO: Done:	 Result Analysis
-2016-09-06 08:07:22,783 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,783 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,783 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:07:22,783 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:07:22,783 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:22,783 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:22,784 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,784 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,784 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,784 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,784 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:22,784 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:22,784 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,784 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,905 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:22,905 DEBUG: Start:	 Training
-2016-09-06 08:07:22,907 DEBUG: Info:	 Time for Training: 0.12481212616[s]
-2016-09-06 08:07:22,907 DEBUG: Done:	 Training
-2016-09-06 08:07:22,907 DEBUG: Start:	 Predicting
-2016-09-06 08:07:22,916 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:22,917 DEBUG: Start:	 Training
-2016-09-06 08:07:22,918 DEBUG: Done:	 Predicting
-2016-09-06 08:07:22,918 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:22,919 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:22,919 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,919 INFO: Done:	 Result Analysis
-2016-09-06 08:07:22,936 DEBUG: Info:	 Time for Training: 0.154469013214[s]
-2016-09-06 08:07:22,937 DEBUG: Done:	 Training
-2016-09-06 08:07:22,937 DEBUG: Start:	 Predicting
-2016-09-06 08:07:22,940 DEBUG: Done:	 Predicting
-2016-09-06 08:07:22,940 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:22,941 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:22,941 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.438095238095
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.438095238095
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,941 INFO: Done:	 Result Analysis
-2016-09-06 08:07:23,022 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,022 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,022 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:07:23,022 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:07:23,022 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:23,022 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:23,023 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:23,023 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:23,023 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:23,023 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:23,023 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:23,023 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:23,023 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,023 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,106 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:23,107 DEBUG: Start:	 Training
-2016-09-06 08:07:23,112 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:23,112 DEBUG: Start:	 Training
-2016-09-06 08:07:23,126 DEBUG: Info:	 Time for Training: 0.104226112366[s]
-2016-09-06 08:07:23,126 DEBUG: Done:	 Training
-2016-09-06 08:07:23,126 DEBUG: Start:	 Predicting
-2016-09-06 08:07:23,132 DEBUG: Done:	 Predicting
-2016-09-06 08:07:23,132 DEBUG: Info:	 Time for Training: 0.110702037811[s]
-2016-09-06 08:07:23,132 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:23,132 DEBUG: Done:	 Training
-2016-09-06 08:07:23,132 DEBUG: Start:	 Predicting
-2016-09-06 08:07:23,133 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:23,133 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,133 INFO: Done:	 Result Analysis
-2016-09-06 08:07:23,136 DEBUG: Done:	 Predicting
-2016-09-06 08:07:23,137 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:23,138 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:23,138 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,138 INFO: Done:	 Result Analysis
-2016-09-06 08:07:23,270 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,270 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,270 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:07:23,270 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:07:23,271 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:23,271 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:23,271 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,271 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,271 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,271 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,271 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:23,271 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:23,271 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,272 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,329 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:23,329 DEBUG: Start:	 Training
-2016-09-06 08:07:23,331 DEBUG: Info:	 Time for Training: 0.0611419677734[s]
-2016-09-06 08:07:23,331 DEBUG: Done:	 Training
-2016-09-06 08:07:23,331 DEBUG: Start:	 Predicting
-2016-09-06 08:07:23,333 DEBUG: Done:	 Predicting
-2016-09-06 08:07:23,333 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:23,334 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:23,334 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,335 INFO: Done:	 Result Analysis
-2016-09-06 08:07:23,365 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:23,365 DEBUG: Start:	 Training
-2016-09-06 08:07:23,369 DEBUG: Info:	 Time for Training: 0.0994069576263[s]
-2016-09-06 08:07:23,369 DEBUG: Done:	 Training
-2016-09-06 08:07:23,369 DEBUG: Start:	 Predicting
-2016-09-06 08:07:23,372 DEBUG: Done:	 Predicting
-2016-09-06 08:07:23,373 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:23,374 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:23,374 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,374 INFO: Done:	 Result Analysis
-2016-09-06 08:07:23,524 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,524 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,524 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:07:23,524 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:07:23,524 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:23,524 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:23,525 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,525 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,526 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,526 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,526 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:23,526 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:23,526 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,526 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,587 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:23,587 DEBUG: Start:	 Training
-2016-09-06 08:07:23,587 DEBUG: Info:	 Time for Training: 0.0643999576569[s]
-2016-09-06 08:07:23,588 DEBUG: Done:	 Training
-2016-09-06 08:07:23,588 DEBUG: Start:	 Predicting
-2016-09-06 08:07:23,594 DEBUG: Done:	 Predicting
-2016-09-06 08:07:23,594 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:23,595 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:23,595 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,595 INFO: Done:	 Result Analysis
-2016-09-06 08:07:23,872 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:23,872 DEBUG: Start:	 Training
-2016-09-06 08:07:23,901 DEBUG: Info:	 Time for Training: 0.377696990967[s]
-2016-09-06 08:07:23,901 DEBUG: Done:	 Training
-2016-09-06 08:07:23,901 DEBUG: Start:	 Predicting
-2016-09-06 08:07:23,906 DEBUG: Done:	 Predicting
-2016-09-06 08:07:23,906 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:23,907 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:23,907 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,908 INFO: Done:	 Result Analysis
-2016-09-06 08:07:23,970 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,970 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,970 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:07:23,970 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:07:23,970 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:23,970 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:23,971 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,971 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,971 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,971 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,971 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:23,971 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:23,971 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,971 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,054 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:24,055 DEBUG: Start:	 Training
-2016-09-06 08:07:24,055 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:24,055 DEBUG: Start:	 Training
-2016-09-06 08:07:24,055 DEBUG: Info:	 Time for Training: 0.0859999656677[s]
-2016-09-06 08:07:24,055 DEBUG: Done:	 Training
-2016-09-06 08:07:24,056 DEBUG: Start:	 Predicting
-2016-09-06 08:07:24,070 DEBUG: Done:	 Predicting
-2016-09-06 08:07:24,070 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:24,071 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:24,071 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,072 INFO: Done:	 Result Analysis
-2016-09-06 08:07:24,074 DEBUG: Info:	 Time for Training: 0.104997873306[s]
-2016-09-06 08:07:24,074 DEBUG: Done:	 Training
-2016-09-06 08:07:24,075 DEBUG: Start:	 Predicting
-2016-09-06 08:07:24,078 DEBUG: Done:	 Predicting
-2016-09-06 08:07:24,078 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:24,079 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:24,079 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,079 INFO: Done:	 Result Analysis
-2016-09-06 08:07:24,230 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,230 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,230 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:07:24,230 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:07:24,230 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:24,230 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:24,231 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:24,231 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:24,231 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:24,231 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:24,232 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:24,232 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:24,232 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,232 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,329 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:24,329 DEBUG: Start:	 Training
-2016-09-06 08:07:24,335 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:24,335 DEBUG: Start:	 Training
-2016-09-06 08:07:24,346 DEBUG: Info:	 Time for Training: 0.11728310585[s]
-2016-09-06 08:07:24,346 DEBUG: Done:	 Training
-2016-09-06 08:07:24,346 DEBUG: Start:	 Predicting
-2016-09-06 08:07:24,352 DEBUG: Done:	 Predicting
-2016-09-06 08:07:24,352 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:24,353 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:24,353 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,353 INFO: Done:	 Result Analysis
-2016-09-06 08:07:24,354 DEBUG: Info:	 Time for Training: 0.124927043915[s]
-2016-09-06 08:07:24,354 DEBUG: Done:	 Training
-2016-09-06 08:07:24,354 DEBUG: Start:	 Predicting
-2016-09-06 08:07:24,359 DEBUG: Done:	 Predicting
-2016-09-06 08:07:24,360 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:24,361 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:24,361 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.644444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.644444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,361 INFO: Done:	 Result Analysis
-2016-09-06 08:07:24,480 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,480 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:07:24,480 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:24,481 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,481 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:07:24,481 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:24,481 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:24,481 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:24,481 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:24,482 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,482 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:24,482 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:24,482 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:24,482 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,552 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:24,552 DEBUG: Start:	 Training
-2016-09-06 08:07:24,554 DEBUG: Info:	 Time for Training: 0.0741329193115[s]
-2016-09-06 08:07:24,555 DEBUG: Done:	 Training
-2016-09-06 08:07:24,555 DEBUG: Start:	 Predicting
-2016-09-06 08:07:24,557 DEBUG: Done:	 Predicting
-2016-09-06 08:07:24,557 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:24,558 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:24,559 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,559 INFO: Done:	 Result Analysis
-2016-09-06 08:07:24,569 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:24,570 DEBUG: Start:	 Training
-2016-09-06 08:07:24,573 DEBUG: Info:	 Time for Training: 0.0938920974731[s]
-2016-09-06 08:07:24,574 DEBUG: Done:	 Training
-2016-09-06 08:07:24,574 DEBUG: Start:	 Predicting
-2016-09-06 08:07:24,576 DEBUG: Done:	 Predicting
-2016-09-06 08:07:24,577 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:24,578 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:24,578 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,578 INFO: Done:	 Result Analysis
-2016-09-06 08:07:24,728 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,728 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,729 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:07:24,729 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:07:24,729 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:24,729 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:24,730 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:24,730 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:24,730 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:24,730 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:24,730 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:24,730 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:24,730 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,730 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,793 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:24,793 DEBUG: Start:	 Training
-2016-09-06 08:07:24,794 DEBUG: Info:	 Time for Training: 0.0663559436798[s]
-2016-09-06 08:07:24,794 DEBUG: Done:	 Training
-2016-09-06 08:07:24,794 DEBUG: Start:	 Predicting
-2016-09-06 08:07:24,801 DEBUG: Done:	 Predicting
-2016-09-06 08:07:24,801 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:24,803 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:24,803 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.652380952381
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,803 INFO: Done:	 Result Analysis
-2016-09-06 08:07:25,043 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:25,044 DEBUG: Start:	 Training
-2016-09-06 08:07:25,083 DEBUG: Info:	 Time for Training: 0.35576415062[s]
-2016-09-06 08:07:25,083 DEBUG: Done:	 Training
-2016-09-06 08:07:25,083 DEBUG: Start:	 Predicting
-2016-09-06 08:07:25,088 DEBUG: Done:	 Predicting
-2016-09-06 08:07:25,088 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:25,089 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:25,089 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 10, max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.957142857143
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,089 INFO: Done:	 Result Analysis
-2016-09-06 08:07:25,174 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,174 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,174 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:07:25,174 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:07:25,174 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:25,174 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:25,175 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:25,175 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:25,175 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:25,175 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:25,175 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:25,175 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:25,176 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,176 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,253 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:25,253 DEBUG: Start:	 Training
-2016-09-06 08:07:25,254 DEBUG: Info:	 Time for Training: 0.0804960727692[s]
-2016-09-06 08:07:25,254 DEBUG: Done:	 Training
-2016-09-06 08:07:25,254 DEBUG: Start:	 Predicting
-2016-09-06 08:07:25,273 DEBUG: Done:	 Predicting
-2016-09-06 08:07:25,273 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:25,274 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:25,274 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,275 INFO: Done:	 Result Analysis
-2016-09-06 08:07:25,277 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:25,277 DEBUG: Start:	 Training
-2016-09-06 08:07:25,297 DEBUG: Info:	 Time for Training: 0.123291015625[s]
-2016-09-06 08:07:25,297 DEBUG: Done:	 Training
-2016-09-06 08:07:25,297 DEBUG: Start:	 Predicting
-2016-09-06 08:07:25,300 DEBUG: Done:	 Predicting
-2016-09-06 08:07:25,300 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:25,301 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:25,301 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,302 INFO: Done:	 Result Analysis
-2016-09-06 08:07:25,422 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,422 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,423 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:07:25,423 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:07:25,423 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:25,423 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:25,423 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:25,423 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:25,423 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:25,423 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:25,424 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:25,424 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,424 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:25,424 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,509 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:25,510 DEBUG: Start:	 Training
-2016-09-06 08:07:25,513 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:25,514 DEBUG: Start:	 Training
-2016-09-06 08:07:25,528 DEBUG: Info:	 Time for Training: 0.106248855591[s]
-2016-09-06 08:07:25,528 DEBUG: Done:	 Training
-2016-09-06 08:07:25,528 DEBUG: Start:	 Predicting
-2016-09-06 08:07:25,531 DEBUG: Info:	 Time for Training: 0.109098911285[s]
-2016-09-06 08:07:25,531 DEBUG: Done:	 Training
-2016-09-06 08:07:25,531 DEBUG: Start:	 Predicting
-2016-09-06 08:07:25,535 DEBUG: Done:	 Predicting
-2016-09-06 08:07:25,535 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:25,535 DEBUG: Done:	 Predicting
-2016-09-06 08:07:25,535 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:25,536 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:25,536 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,536 INFO: Done:	 Result Analysis
-2016-09-06 08:07:25,537 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:25,537 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,537 INFO: Done:	 Result Analysis
-2016-09-06 08:07:25,671 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,671 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:07:25,672 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:25,672 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,672 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:07:25,672 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:25,673 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:25,673 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:25,673 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:25,673 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:25,673 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:25,673 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:25,673 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,673 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,732 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:25,732 DEBUG: Start:	 Training
-2016-09-06 08:07:25,734 DEBUG: Info:	 Time for Training: 0.0624670982361[s]
-2016-09-06 08:07:25,734 DEBUG: Done:	 Training
-2016-09-06 08:07:25,734 DEBUG: Start:	 Predicting
-2016-09-06 08:07:25,736 DEBUG: Done:	 Predicting
-2016-09-06 08:07:25,737 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:25,738 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:25,738 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,738 INFO: Done:	 Result Analysis
-2016-09-06 08:07:25,759 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:25,759 DEBUG: Start:	 Training
-2016-09-06 08:07:25,763 DEBUG: Info:	 Time for Training: 0.0922710895538[s]
-2016-09-06 08:07:25,763 DEBUG: Done:	 Training
-2016-09-06 08:07:25,763 DEBUG: Start:	 Predicting
-2016-09-06 08:07:25,766 DEBUG: Done:	 Predicting
-2016-09-06 08:07:25,766 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:25,768 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:25,768 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,768 INFO: Done:	 Result Analysis
-2016-09-06 08:07:25,919 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,919 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,919 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:07:25,919 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:07:25,919 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:25,919 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:25,920 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:25,920 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:25,920 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:25,920 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:25,920 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:25,920 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:25,920 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,920 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,974 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:25,974 DEBUG: Start:	 Training
-2016-09-06 08:07:25,975 DEBUG: Info:	 Time for Training: 0.0564980506897[s]
-2016-09-06 08:07:25,975 DEBUG: Done:	 Training
-2016-09-06 08:07:25,975 DEBUG: Start:	 Predicting
-2016-09-06 08:07:25,981 DEBUG: Done:	 Predicting
-2016-09-06 08:07:25,981 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:25,982 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:25,983 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,983 INFO: Done:	 Result Analysis
-2016-09-06 08:07:26,231 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:26,231 DEBUG: Start:	 Training
-2016-09-06 08:07:26,249 DEBUG: Info:	 Time for Training: 0.331297874451[s]
-2016-09-06 08:07:26,250 DEBUG: Done:	 Training
-2016-09-06 08:07:26,250 DEBUG: Start:	 Predicting
-2016-09-06 08:07:26,254 DEBUG: Done:	 Predicting
-2016-09-06 08:07:26,254 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:26,255 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:26,255 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:07:26,255 INFO: Done:	 Result Analysis
-2016-09-06 08:07:26,364 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:26,364 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:26,364 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:07:26,364 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:07:26,364 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:26,364 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:07:26,365 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:26,365 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:26,365 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:26,365 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:26,366 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:26,366 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:07:26,366 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:26,366 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:07:26,446 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:26,446 DEBUG: Start:	 Training
-2016-09-06 08:07:26,447 DEBUG: Info:	 Time for Training: 0.0832591056824[s]
-2016-09-06 08:07:26,447 DEBUG: Done:	 Training
-2016-09-06 08:07:26,447 DEBUG: Start:	 Predicting
-2016-09-06 08:07:26,454 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:07:26,454 DEBUG: Start:	 Training
-2016-09-06 08:07:26,466 DEBUG: Done:	 Predicting
-2016-09-06 08:07:26,466 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:26,467 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:26,467 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:07:26,467 INFO: Done:	 Result Analysis
-2016-09-06 08:07:26,472 DEBUG: Info:	 Time for Training: 0.108740091324[s]
-2016-09-06 08:07:26,472 DEBUG: Done:	 Training
-2016-09-06 08:07:26,473 DEBUG: Start:	 Predicting
-2016-09-06 08:07:26,478 DEBUG: Done:	 Predicting
-2016-09-06 08:07:26,479 DEBUG: Start:	 Getting Results
-2016-09-06 08:07:26,480 DEBUG: Done:	 Getting Results
-2016-09-06 08:07:26,480 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:07:26,480 INFO: Done:	 Result Analysis
-2016-09-06 08:07:26,764 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:07:26,764 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:07:26,765 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:07:26,765 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:07:26,765 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:07:26,765 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:07:26,766 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:07:26,766 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:07:26,767 INFO: Info:	 Shape of View2 :(300, 13)
-2016-09-06 08:07:26,767 INFO: Info:	 Shape of View2 :(300, 13)
-2016-09-06 08:07:26,768 INFO: Info:	 Shape of View3 :(300, 9)
-2016-09-06 08:07:26,768 INFO: Info:	 Shape of View3 :(300, 9)
-2016-09-06 08:07:26,768 INFO: Done:	 Read Database Files
-2016-09-06 08:07:26,768 INFO: Done:	 Read Database Files
-2016-09-06 08:07:26,768 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:07:26,768 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:07:26,776 INFO: Done:	 Determine validation split
-2016-09-06 08:07:26,776 INFO: Done:	 Determine validation split
-2016-09-06 08:07:26,776 INFO: Start:	 Determine 5 folds
-2016-09-06 08:07:26,776 INFO: Start:	 Determine 5 folds
-2016-09-06 08:07:26,786 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:07:26,786 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:07:26,787 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:07:26,787 INFO: Done:	 Determine folds
-2016-09-06 08:07:26,787 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 08:07:26,787 INFO: Start:	 Classification
-2016-09-06 08:07:26,787 INFO: 	Start:	 Fold number 1
-2016-09-06 08:07:26,788 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:07:26,788 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:07:26,789 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:07:26,789 INFO: Done:	 Determine folds
-2016-09-06 08:07:26,789 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:07:26,789 INFO: Start:	 Classification
-2016-09-06 08:07:26,789 INFO: 	Start:	 Fold number 1
-2016-09-06 08:07:26,842 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:07:26,855 DEBUG: 			View 0 : 0.412087912088
-2016-09-06 08:07:26,867 DEBUG: 			View 1 : 0.412087912088
-2016-09-06 08:07:26,879 DEBUG: 			View 2 : 0.412087912088
-2016-09-06 08:07:26,889 DEBUG: 			View 3 : 0.412087912088
-2016-09-06 08:07:26,889 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 08:07:26,891 INFO: 	Start: 	 Classification
-2016-09-06 08:07:26,952 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:07:26,953 INFO: 	Start:	 Fold number 2
-2016-09-06 08:07:26,954 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:27,056 INFO: 	Start: 	 Classification
-2016-09-06 08:07:27,077 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:07:27,091 DEBUG: 			View 0 : 0.725274725275
-2016-09-06 08:07:27,104 DEBUG: 			View 1 : 0.703296703297
-2016-09-06 08:07:27,117 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:07:27,117 INFO: 	Start:	 Fold number 3
-2016-09-06 08:07:27,117 DEBUG: 			View 2 : 0.725274725275
-2016-09-06 08:07:27,131 DEBUG: 			View 3 : 0.653846153846
-2016-09-06 08:07:27,203 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:27,218 INFO: 	Start: 	 Classification
-2016-09-06 08:07:27,255 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:07:27,255 INFO: 	Start:	 Fold number 4
-2016-09-06 08:07:27,320 INFO: 	Start: 	 Classification
-2016-09-06 08:07:27,355 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:07:27,355 INFO: 	Start:	 Fold number 5
-2016-09-06 08:07:27,420 INFO: 	Start: 	 Classification
-2016-09-06 08:07:27,454 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:07:27,454 INFO: Done:	 Classification
-2016-09-06 08:07:27,454 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:07:27,454 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:07:27,458 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 59.262305365
-	-On Test : 55.6097560976
-	-On Validation : 57.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- SVM Linear with C : 5372
-		- SVM Linear with C : 5372
-		- K nearest Neighbors with  n_neighbors: 31
-		- SVM Linear with C : 5372
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:07:27,459 INFO: Done:	 Result Analysis
-2016-09-06 08:07:27,568 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:07:27,578 DEBUG: 			View 0 : 0.725274725275
-2016-09-06 08:07:27,587 DEBUG: 			View 1 : 0.703296703297
-2016-09-06 08:07:27,594 DEBUG: 			View 2 : 0.725274725275
-2016-09-06 08:07:27,601 DEBUG: 			View 3 : 0.653846153846
-2016-09-06 08:07:27,643 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:27,903 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:07:27,910 DEBUG: 			View 0 : 0.708791208791
-2016-09-06 08:07:27,917 DEBUG: 			View 1 : 0.642857142857
-2016-09-06 08:07:27,925 DEBUG: 			View 2 : 0.714285714286
-2016-09-06 08:07:27,932 DEBUG: 			View 3 : 0.642857142857
-2016-09-06 08:07:27,977 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:28,265 INFO: 	Start: 	 Classification
-2016-09-06 08:07:28,735 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:07:28,735 INFO: 	Start:	 Fold number 2
-2016-09-06 08:07:28,764 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:07:28,771 DEBUG: 			View 0 : 0.576923076923
-2016-09-06 08:07:28,778 DEBUG: 			View 1 : 0.576923076923
-2016-09-06 08:07:28,784 DEBUG: 			View 2 : 0.576923076923
-2016-09-06 08:07:28,791 DEBUG: 			View 3 : 0.576923076923
-2016-09-06 08:07:28,823 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:28,902 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:07:28,910 DEBUG: 			View 0 : 0.736263736264
-2016-09-06 08:07:28,917 DEBUG: 			View 1 : 0.692307692308
-2016-09-06 08:07:28,924 DEBUG: 			View 2 : 0.681318681319
-2016-09-06 08:07:28,938 DEBUG: 			View 3 : 0.642857142857
-2016-09-06 08:07:28,979 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:29,128 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:07:29,135 DEBUG: 			View 0 : 0.736263736264
-2016-09-06 08:07:29,143 DEBUG: 			View 1 : 0.692307692308
-2016-09-06 08:07:29,150 DEBUG: 			View 2 : 0.681318681319
-2016-09-06 08:07:29,157 DEBUG: 			View 3 : 0.642857142857
-2016-09-06 08:07:29,198 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:29,430 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:07:29,438 DEBUG: 			View 0 : 0.708791208791
-2016-09-06 08:07:29,445 DEBUG: 			View 1 : 0.675824175824
-2016-09-06 08:07:29,453 DEBUG: 			View 2 : 0.659340659341
-2016-09-06 08:07:29,460 DEBUG: 			View 3 : 0.571428571429
-2016-09-06 08:07:29,504 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:29,794 INFO: 	Start: 	 Classification
-2016-09-06 08:07:30,272 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:07:30,272 INFO: 	Start:	 Fold number 3
-2016-09-06 08:07:30,304 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:07:30,311 DEBUG: 			View 0 : 0.582857142857
-2016-09-06 08:07:30,318 DEBUG: 			View 1 : 0.582857142857
-2016-09-06 08:07:30,326 DEBUG: 			View 2 : 0.582857142857
-2016-09-06 08:07:30,333 DEBUG: 			View 3 : 0.582857142857
-2016-09-06 08:07:30,367 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:30,447 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:07:30,455 DEBUG: 			View 0 : 0.702857142857
-2016-09-06 08:07:30,463 DEBUG: 			View 1 : 0.691428571429
-2016-09-06 08:07:30,471 DEBUG: 			View 2 : 0.737142857143
-2016-09-06 08:07:30,479 DEBUG: 			View 3 : 0.725714285714
-2016-09-06 08:07:30,520 DEBUG: 			 Best view : 		View2
-2016-09-06 08:07:30,672 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:07:30,680 DEBUG: 			View 0 : 0.702857142857
-2016-09-06 08:07:30,688 DEBUG: 			View 1 : 0.691428571429
-2016-09-06 08:07:30,695 DEBUG: 			View 2 : 0.737142857143
-2016-09-06 08:07:30,703 DEBUG: 			View 3 : 0.725714285714
-2016-09-06 08:07:30,744 DEBUG: 			 Best view : 		View2
-2016-09-06 08:07:30,957 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:07:30,964 DEBUG: 			View 0 : 0.742857142857
-2016-09-06 08:07:30,971 DEBUG: 			View 1 : 0.725714285714
-2016-09-06 08:07:30,978 DEBUG: 			View 2 : 0.708571428571
-2016-09-06 08:07:30,985 DEBUG: 			View 3 : 0.657142857143
-2016-09-06 08:07:31,027 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:31,306 INFO: 	Start: 	 Classification
-2016-09-06 08:07:31,772 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:07:31,773 INFO: 	Start:	 Fold number 4
-2016-09-06 08:07:31,804 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:07:31,811 DEBUG: 			View 0 : 0.41847826087
-2016-09-06 08:07:31,817 DEBUG: 			View 1 : 0.41847826087
-2016-09-06 08:07:31,824 DEBUG: 			View 2 : 0.41847826087
-2016-09-06 08:07:31,831 DEBUG: 			View 3 : 0.41847826087
-2016-09-06 08:07:31,831 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 08:07:31,863 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:31,945 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:07:31,952 DEBUG: 			View 0 : 0.717391304348
-2016-09-06 08:07:31,960 DEBUG: 			View 1 : 0.739130434783
-2016-09-06 08:07:31,967 DEBUG: 			View 2 : 0.66847826087
-2016-09-06 08:07:31,974 DEBUG: 			View 3 : 0.673913043478
-2016-09-06 08:07:32,013 DEBUG: 			 Best view : 		View1
-2016-09-06 08:07:32,165 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:07:32,173 DEBUG: 			View 0 : 0.717391304348
-2016-09-06 08:07:32,180 DEBUG: 			View 1 : 0.739130434783
-2016-09-06 08:07:32,187 DEBUG: 			View 2 : 0.66847826087
-2016-09-06 08:07:32,194 DEBUG: 			View 3 : 0.673913043478
-2016-09-06 08:07:32,238 DEBUG: 			 Best view : 		View1
-2016-09-06 08:07:32,460 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:07:32,467 DEBUG: 			View 0 : 0.760869565217
-2016-09-06 08:07:32,474 DEBUG: 			View 1 : 0.701086956522
-2016-09-06 08:07:32,482 DEBUG: 			View 2 : 0.695652173913
-2016-09-06 08:07:32,489 DEBUG: 			View 3 : 0.641304347826
-2016-09-06 08:07:32,533 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:32,825 INFO: 	Start: 	 Classification
-2016-09-06 08:07:33,307 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:07:33,307 INFO: 	Start:	 Fold number 5
-2016-09-06 08:07:33,337 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:07:33,344 DEBUG: 			View 0 : 0.579787234043
-2016-09-06 08:07:33,351 DEBUG: 			View 1 : 0.579787234043
-2016-09-06 08:07:33,358 DEBUG: 			View 2 : 0.579787234043
-2016-09-06 08:07:33,364 DEBUG: 			View 3 : 0.579787234043
-2016-09-06 08:07:33,398 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:33,484 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:07:33,492 DEBUG: 			View 0 : 0.728723404255
-2016-09-06 08:07:33,499 DEBUG: 			View 1 : 0.696808510638
-2016-09-06 08:07:33,506 DEBUG: 			View 2 : 0.632978723404
-2016-09-06 08:07:33,513 DEBUG: 			View 3 : 0.696808510638
-2016-09-06 08:07:33,553 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:33,709 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:07:33,717 DEBUG: 			View 0 : 0.728723404255
-2016-09-06 08:07:33,725 DEBUG: 			View 1 : 0.696808510638
-2016-09-06 08:07:33,733 DEBUG: 			View 2 : 0.632978723404
-2016-09-06 08:07:33,740 DEBUG: 			View 3 : 0.696808510638
-2016-09-06 08:07:33,783 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:34,011 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:07:34,019 DEBUG: 			View 0 : 0.755319148936
-2016-09-06 08:07:34,026 DEBUG: 			View 1 : 0.664893617021
-2016-09-06 08:07:34,033 DEBUG: 			View 2 : 0.664893617021
-2016-09-06 08:07:34,040 DEBUG: 			View 3 : 0.617021276596
-2016-09-06 08:07:34,085 DEBUG: 			 Best view : 		View0
-2016-09-06 08:07:34,384 INFO: 	Start: 	 Classification
-2016-09-06 08:07:34,871 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:07:34,871 INFO: Done:	 Classification
-2016-09-06 08:07:34,871 INFO: Info:	 Time for Classification: 8[s]
-2016-09-06 08:07:34,871 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 08:07:37,434 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 73.3307031544
-	-On Test : 49.756097561
-	-On Validation : 67.5555555556Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 14), View1 of shape (300, 10), View2 of shape (300, 13), View3 of shape (300, 9)
-	-5 folds
-	- Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:03        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:06        0:00:00
-	         Fold 5        0:00:07        0:00:00
-	          Total        0:00:22        0:00:02
-	So a total classification time of 0:00:08.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.257142857143
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.246153846154
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.257692307692
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.236263736264
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.275824175824
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.263736263736
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.25989010989
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.243406593407
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.273142857143
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.269142857143
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.276571428571
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.269142857143
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.261413043478
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.259782608696
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.245108695652
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.240760869565
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.279255319149
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.263829787234
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.251063829787
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.259042553191
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 41.2087912088
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 42.3076923077
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 41.7142857143
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 41.847826087
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 42.0212765957
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 72.5274725275
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.2222222222
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.6263736264
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.5555555556
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 73.7142857143
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 73.9130434783
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 72.8723404255
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 72.5274725275
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.2222222222
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.6263736264
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.5555555556
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 73.7142857143
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 73.9130434783
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 72.8723404255
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 72.5274725275
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.2222222222
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.6263736264
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.5555555556
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 73.7142857143
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 73.9130434783
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 72.8723404255
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 41.2087912088
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 42.3076923077
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 41.7142857143
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 41.847826087
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 42.0212765957
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-2016-09-06 08:07:37,700 INFO: Done:	 Result Analysis
-2016-09-06 08:07:37,830 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:07:37,831 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:07:37,831 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:07:37,831 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:07:37,831 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:07:37,831 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:07:37,832 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:07:37,832 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:07:37,832 INFO: Info:	 Shape of View2 :(300, 13)
-2016-09-06 08:07:37,832 INFO: Info:	 Shape of View2 :(300, 13)
-2016-09-06 08:07:37,833 INFO: Info:	 Shape of View3 :(300, 9)
-2016-09-06 08:07:37,833 INFO: Info:	 Shape of View3 :(300, 9)
-2016-09-06 08:07:37,833 INFO: Done:	 Read Database Files
-2016-09-06 08:07:37,833 INFO: Done:	 Read Database Files
-2016-09-06 08:07:37,833 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:07:37,833 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:07:37,837 INFO: Done:	 Determine validation split
-2016-09-06 08:07:37,837 INFO: Start:	 Determine 5 folds
-2016-09-06 08:07:37,839 INFO: Done:	 Determine validation split
-2016-09-06 08:07:37,839 INFO: Start:	 Determine 5 folds
-2016-09-06 08:07:37,845 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:07:37,845 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:07:37,845 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:07:37,846 INFO: Done:	 Determine folds
-2016-09-06 08:07:37,846 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:07:37,846 INFO: Start:	 Classification
-2016-09-06 08:07:37,846 INFO: 	Start:	 Fold number 1
-2016-09-06 08:07:37,846 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:07:37,846 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:07:37,846 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:07:37,846 INFO: Done:	 Determine folds
-2016-09-06 08:07:37,847 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:07:37,847 INFO: Start:	 Classification
-2016-09-06 08:07:37,847 INFO: 	Start:	 Fold number 1
-2016-09-06 08:07:37,913 INFO: 	Start: 	 Classification
-2016-09-06 08:07:37,948 INFO: 	Start: 	 Classification
-2016-09-06 08:07:37,996 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:07:37,996 INFO: 	Start:	 Fold number 2
-2016-09-06 08:07:38,059 INFO: 	Start: 	 Classification
-2016-09-06 08:07:38,143 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:07:38,143 INFO: 	Start:	 Fold number 3
-2016-09-06 08:07:38,210 INFO: 	Start: 	 Classification
-2016-09-06 08:07:38,291 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:07:38,292 INFO: 	Start:	 Fold number 4
-2016-09-06 08:07:38,354 INFO: 	Start: 	 Classification
-2016-09-06 08:07:38,433 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:07:38,433 INFO: 	Start:	 Fold number 5
-2016-09-06 08:07:38,497 INFO: 	Start: 	 Classification
-2016-09-06 08:07:38,576 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:07:38,576 INFO: Done:	 Classification
-2016-09-06 08:07:38,576 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:07:38,576 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:07:38,581 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 96.1190234432
-	-On Test : 51.2195121951
-	-On Validation : 81.5555555556
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- SVM Linear with C : 5372
-		- SVM Linear with C : 5372
-		- K nearest Neighbors with  n_neighbors: 31
-		- SVM Linear with C : 5372
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:07:38,581 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5d72613c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 07182458..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b21aab1d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.661904761905
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a62664a6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.952380952381
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a776fa3a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 90ec5cdd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.438095238095
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.438095238095
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8b12b25b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 86262c36..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4acfa749..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75cb380f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6fa7b0fa..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6f07c75d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b781bf5f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8152c45e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ec646fc8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.652380952381
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c70d6097..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e3194ef9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ceeff8e1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.644444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.644444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 106a3d98..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 308bfe63..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2757a0f8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9cd6b780..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 31
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b28792ca..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 10, max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.957142857143
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cf748fb9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7f27478a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c388e77d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 36c54288..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bbf9b155..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f2ce7971..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 91bd3e56..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5372
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080727Results-Fusion-LateFusion-BayesianInference-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080727Results-Fusion-LateFusion-BayesianInference-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5bdf5b85..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080727Results-Fusion-LateFusion-BayesianInference-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 59.262305365
-	-On Test : 55.6097560976
-	-On Validation : 57.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- SVM Linear with C : 5372
-		- SVM Linear with C : 5372
-		- K nearest Neighbors with  n_neighbors: 31
-		- SVM Linear with C : 5372
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080737Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-080737Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index c0a61fb8d4ae160471d9076e8e3b16fc4e5ff5af..0000000000000000000000000000000000000000
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080737Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080737Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 9361b2c8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080737Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,235 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 73.3307031544
-	-On Test : 49.756097561
-	-On Validation : 67.5555555556Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 14), View1 of shape (300, 10), View2 of shape (300, 13), View3 of shape (300, 9)
-	-5 folds
-	- Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:03        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:06        0:00:00
-	         Fold 5        0:00:07        0:00:00
-	          Total        0:00:22        0:00:02
-	So a total classification time of 0:00:08.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.257142857143
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.246153846154
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.257692307692
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.236263736264
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.275824175824
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.263736263736
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.25989010989
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.243406593407
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.273142857143
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.269142857143
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.276571428571
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.269142857143
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.261413043478
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.259782608696
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.245108695652
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.240760869565
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.279255319149
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.263829787234
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.251063829787
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.259042553191
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 41.2087912088
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 42.3076923077
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 41.7142857143
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 41.847826087
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 42.0212765957
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 72.5274725275
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.2222222222
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.6263736264
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.5555555556
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 73.7142857143
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 73.9130434783
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 72.8723404255
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 72.5274725275
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.2222222222
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.6263736264
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.5555555556
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 73.7142857143
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 73.9130434783
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 72.8723404255
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 72.5274725275
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.2222222222
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.6263736264
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.5555555556
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 73.7142857143
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 73.9130434783
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 72.8723404255
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 41.2087912088
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 42.3076923077
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 41.7142857143
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 41.847826087
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 42.0212765957
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.3333333333
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080738Results-Fusion-LateFusion-MajorityVoting-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080738Results-Fusion-LateFusion-MajorityVoting-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index fec3e54a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080738Results-Fusion-LateFusion-MajorityVoting-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 96.1190234432
-	-On Test : 51.2195121951
-	-On Validation : 81.5555555556
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- SVM Linear with C : 5372
-		- SVM Linear with C : 5372
-		- K nearest Neighbors with  n_neighbors: 31
-		- SVM Linear with C : 5372
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080839-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-080839-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index f9eb7925..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080839-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1840 +0,0 @@
-2016-09-06 08:08:39,145 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:08:39,145 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00014040625 Gbytes /!\ 
-2016-09-06 08:08:44,159 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 08:08:44,161 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 08:08:44,213 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:44,213 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:44,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:08:44,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:08:44,213 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:44,213 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:44,214 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:44,214 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:44,214 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:44,214 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:44,214 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:44,214 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:44,214 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:44,214 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:44,272 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:44,272 DEBUG: Start:	 Training
-2016-09-06 08:08:44,274 DEBUG: Info:	 Time for Training: 0.0613279342651[s]
-2016-09-06 08:08:44,274 DEBUG: Done:	 Training
-2016-09-06 08:08:44,274 DEBUG: Start:	 Predicting
-2016-09-06 08:08:44,276 DEBUG: Done:	 Predicting
-2016-09-06 08:08:44,276 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:44,277 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:44,278 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:08:44,278 INFO: Done:	 Result Analysis
-2016-09-06 08:08:44,308 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:44,309 DEBUG: Start:	 Training
-2016-09-06 08:08:44,313 DEBUG: Info:	 Time for Training: 0.100487947464[s]
-2016-09-06 08:08:44,313 DEBUG: Done:	 Training
-2016-09-06 08:08:44,313 DEBUG: Start:	 Predicting
-2016-09-06 08:08:44,315 DEBUG: Done:	 Predicting
-2016-09-06 08:08:44,316 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:44,317 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:44,317 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:08:44,317 INFO: Done:	 Result Analysis
-2016-09-06 08:08:44,464 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:44,464 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:08:44,464 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:44,464 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:44,465 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:08:44,465 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:44,465 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:44,466 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:44,466 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:44,466 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:44,466 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:44,466 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:44,466 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:44,466 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:44,519 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:44,519 DEBUG: Start:	 Training
-2016-09-06 08:08:44,520 DEBUG: Info:	 Time for Training: 0.0568120479584[s]
-2016-09-06 08:08:44,520 DEBUG: Done:	 Training
-2016-09-06 08:08:44,520 DEBUG: Start:	 Predicting
-2016-09-06 08:08:44,527 DEBUG: Done:	 Predicting
-2016-09-06 08:08:44,528 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:44,529 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:44,529 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 49
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:44,529 INFO: Done:	 Result Analysis
-2016-09-06 08:08:45,064 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:45,064 DEBUG: Start:	 Training
-2016-09-06 08:08:45,119 DEBUG: Info:	 Time for Training: 0.655100107193[s]
-2016-09-06 08:08:45,119 DEBUG: Done:	 Training
-2016-09-06 08:08:45,119 DEBUG: Start:	 Predicting
-2016-09-06 08:08:45,126 DEBUG: Done:	 Predicting
-2016-09-06 08:08:45,126 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:45,127 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:45,127 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 21, max_depth : 29
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,127 INFO: Done:	 Result Analysis
-2016-09-06 08:08:45,213 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,213 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:08:45,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:08:45,214 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:45,214 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:45,214 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:45,214 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:45,214 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:45,214 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:45,215 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:45,215 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:45,215 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,215 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,299 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:45,299 DEBUG: Start:	 Training
-2016-09-06 08:08:45,300 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:45,300 DEBUG: Start:	 Training
-2016-09-06 08:08:45,301 DEBUG: Info:	 Time for Training: 0.0881788730621[s]
-2016-09-06 08:08:45,301 DEBUG: Done:	 Training
-2016-09-06 08:08:45,301 DEBUG: Start:	 Predicting
-2016-09-06 08:08:45,310 DEBUG: Done:	 Predicting
-2016-09-06 08:08:45,310 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:45,311 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:45,311 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,312 INFO: Done:	 Result Analysis
-2016-09-06 08:08:45,318 DEBUG: Info:	 Time for Training: 0.105075836182[s]
-2016-09-06 08:08:45,318 DEBUG: Done:	 Training
-2016-09-06 08:08:45,318 DEBUG: Start:	 Predicting
-2016-09-06 08:08:45,321 DEBUG: Done:	 Predicting
-2016-09-06 08:08:45,321 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:45,322 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:45,322 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,322 INFO: Done:	 Result Analysis
-2016-09-06 08:08:45,460 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,460 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,460 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:08:45,460 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:08:45,460 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:45,460 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:45,461 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:45,461 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:45,461 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:45,461 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:45,461 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:45,461 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,461 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:45,461 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,550 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:45,550 DEBUG: Start:	 Training
-2016-09-06 08:08:45,561 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:45,561 DEBUG: Start:	 Training
-2016-09-06 08:08:45,567 DEBUG: Info:	 Time for Training: 0.108205080032[s]
-2016-09-06 08:08:45,567 DEBUG: Done:	 Training
-2016-09-06 08:08:45,568 DEBUG: Start:	 Predicting
-2016-09-06 08:08:45,573 DEBUG: Done:	 Predicting
-2016-09-06 08:08:45,573 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:45,574 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:45,574 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,574 INFO: Done:	 Result Analysis
-2016-09-06 08:08:45,578 DEBUG: Info:	 Time for Training: 0.118767976761[s]
-2016-09-06 08:08:45,578 DEBUG: Done:	 Training
-2016-09-06 08:08:45,578 DEBUG: Start:	 Predicting
-2016-09-06 08:08:45,582 DEBUG: Done:	 Predicting
-2016-09-06 08:08:45,582 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:45,583 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:45,583 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,583 INFO: Done:	 Result Analysis
-2016-09-06 08:08:45,709 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,709 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:08:45,709 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,709 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:45,709 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:08:45,709 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:45,710 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:45,710 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:45,710 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:45,710 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:45,710 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:45,710 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,710 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:45,710 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,775 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:45,775 DEBUG: Start:	 Training
-2016-09-06 08:08:45,778 DEBUG: Info:	 Time for Training: 0.0693180561066[s]
-2016-09-06 08:08:45,778 DEBUG: Done:	 Training
-2016-09-06 08:08:45,778 DEBUG: Start:	 Predicting
-2016-09-06 08:08:45,780 DEBUG: Done:	 Predicting
-2016-09-06 08:08:45,780 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:45,781 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:45,781 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,782 INFO: Done:	 Result Analysis
-2016-09-06 08:08:45,801 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:45,801 DEBUG: Start:	 Training
-2016-09-06 08:08:45,806 DEBUG: Info:	 Time for Training: 0.0974469184875[s]
-2016-09-06 08:08:45,806 DEBUG: Done:	 Training
-2016-09-06 08:08:45,806 DEBUG: Start:	 Predicting
-2016-09-06 08:08:45,809 DEBUG: Done:	 Predicting
-2016-09-06 08:08:45,809 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:45,811 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:45,811 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,811 INFO: Done:	 Result Analysis
-2016-09-06 08:08:45,957 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,957 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,957 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:08:45,957 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:08:45,957 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:45,957 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:45,958 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:45,958 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:45,958 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:45,958 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:45,958 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:45,958 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:45,958 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,958 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:46,020 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:46,020 DEBUG: Start:	 Training
-2016-09-06 08:08:46,021 DEBUG: Info:	 Time for Training: 0.0644130706787[s]
-2016-09-06 08:08:46,021 DEBUG: Done:	 Training
-2016-09-06 08:08:46,021 DEBUG: Start:	 Predicting
-2016-09-06 08:08:46,029 DEBUG: Done:	 Predicting
-2016-09-06 08:08:46,029 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:46,031 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:46,031 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:08:46,031 INFO: Done:	 Result Analysis
-2016-09-06 08:08:46,554 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:46,554 DEBUG: Start:	 Training
-2016-09-06 08:08:46,597 DEBUG: Info:	 Time for Training: 0.641263008118[s]
-2016-09-06 08:08:46,598 DEBUG: Done:	 Training
-2016-09-06 08:08:46,598 DEBUG: Start:	 Predicting
-2016-09-06 08:08:46,604 DEBUG: Done:	 Predicting
-2016-09-06 08:08:46,604 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:46,605 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:46,605 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 16, max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:46,605 INFO: Done:	 Result Analysis
-2016-09-06 08:08:46,711 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:46,711 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:46,711 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:08:46,711 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:08:46,711 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:46,711 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:46,712 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:46,712 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:46,712 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:46,712 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:46,712 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:46,712 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:46,712 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:46,712 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:46,788 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:46,788 DEBUG: Start:	 Training
-2016-09-06 08:08:46,789 DEBUG: Info:	 Time for Training: 0.0784420967102[s]
-2016-09-06 08:08:46,789 DEBUG: Done:	 Training
-2016-09-06 08:08:46,789 DEBUG: Start:	 Predicting
-2016-09-06 08:08:46,800 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:46,800 DEBUG: Start:	 Training
-2016-09-06 08:08:46,812 DEBUG: Done:	 Predicting
-2016-09-06 08:08:46,812 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:46,815 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:46,815 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:46,816 INFO: Done:	 Result Analysis
-2016-09-06 08:08:46,831 DEBUG: Info:	 Time for Training: 0.120301008224[s]
-2016-09-06 08:08:46,831 DEBUG: Done:	 Training
-2016-09-06 08:08:46,831 DEBUG: Start:	 Predicting
-2016-09-06 08:08:46,834 DEBUG: Done:	 Predicting
-2016-09-06 08:08:46,835 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:46,835 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:46,836 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:08:46,836 INFO: Done:	 Result Analysis
-2016-09-06 08:08:46,957 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:46,957 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:46,957 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:08:46,957 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:08:46,957 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:46,957 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:46,958 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:46,958 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:46,958 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:46,958 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:46,958 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:46,958 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:46,959 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:46,959 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:47,044 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:47,044 DEBUG: Start:	 Training
-2016-09-06 08:08:47,054 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:47,055 DEBUG: Start:	 Training
-2016-09-06 08:08:47,064 DEBUG: Info:	 Time for Training: 0.107657909393[s]
-2016-09-06 08:08:47,064 DEBUG: Done:	 Training
-2016-09-06 08:08:47,064 DEBUG: Start:	 Predicting
-2016-09-06 08:08:47,070 DEBUG: Done:	 Predicting
-2016-09-06 08:08:47,070 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:47,072 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:47,072 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:08:47,072 INFO: Done:	 Result Analysis
-2016-09-06 08:08:47,073 DEBUG: Info:	 Time for Training: 0.116585016251[s]
-2016-09-06 08:08:47,073 DEBUG: Done:	 Training
-2016-09-06 08:08:47,073 DEBUG: Start:	 Predicting
-2016-09-06 08:08:47,077 DEBUG: Done:	 Predicting
-2016-09-06 08:08:47,077 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:47,078 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:47,078 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9418
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:47,078 INFO: Done:	 Result Analysis
-2016-09-06 08:08:47,207 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:47,207 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:08:47,207 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:47,208 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:47,208 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:47,208 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:08:47,208 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:47,208 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:47,208 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:47,209 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:47,209 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:47,210 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:47,210 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:47,210 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:47,283 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:47,283 DEBUG: Start:	 Training
-2016-09-06 08:08:47,286 DEBUG: Info:	 Time for Training: 0.0787489414215[s]
-2016-09-06 08:08:47,286 DEBUG: Done:	 Training
-2016-09-06 08:08:47,286 DEBUG: Start:	 Predicting
-2016-09-06 08:08:47,289 DEBUG: Done:	 Predicting
-2016-09-06 08:08:47,290 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:47,291 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:47,291 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:47,291 INFO: Done:	 Result Analysis
-2016-09-06 08:08:47,302 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:47,302 DEBUG: Start:	 Training
-2016-09-06 08:08:47,307 DEBUG: Info:	 Time for Training: 0.101135015488[s]
-2016-09-06 08:08:47,307 DEBUG: Done:	 Training
-2016-09-06 08:08:47,307 DEBUG: Start:	 Predicting
-2016-09-06 08:08:47,310 DEBUG: Done:	 Predicting
-2016-09-06 08:08:47,310 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:47,312 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:47,312 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:08:47,312 INFO: Done:	 Result Analysis
-2016-09-06 08:08:47,457 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:47,457 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:47,457 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:08:47,457 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:08:47,458 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:47,458 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:47,459 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:47,459 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:47,459 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:47,459 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:47,459 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:47,459 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:47,459 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:47,459 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:47,513 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:47,514 DEBUG: Start:	 Training
-2016-09-06 08:08:47,514 DEBUG: Info:	 Time for Training: 0.0577688217163[s]
-2016-09-06 08:08:47,514 DEBUG: Done:	 Training
-2016-09-06 08:08:47,514 DEBUG: Start:	 Predicting
-2016-09-06 08:08:47,523 DEBUG: Done:	 Predicting
-2016-09-06 08:08:47,523 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:47,524 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:47,524 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:47,524 INFO: Done:	 Result Analysis
-2016-09-06 08:08:48,130 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:48,130 DEBUG: Start:	 Training
-2016-09-06 08:08:48,193 DEBUG: Info:	 Time for Training: 0.736048936844[s]
-2016-09-06 08:08:48,193 DEBUG: Done:	 Training
-2016-09-06 08:08:48,193 DEBUG: Start:	 Predicting
-2016-09-06 08:08:48,201 DEBUG: Done:	 Predicting
-2016-09-06 08:08:48,201 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:48,202 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:48,203 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 21, max_depth : 29
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,203 INFO: Done:	 Result Analysis
-2016-09-06 08:08:48,315 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,315 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,316 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:08:48,316 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:08:48,316 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:48,316 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:48,316 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:48,316 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:48,317 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:48,317 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:48,317 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:48,317 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:48,317 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,317 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,391 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:48,391 DEBUG: Start:	 Training
-2016-09-06 08:08:48,391 DEBUG: Info:	 Time for Training: 0.0768928527832[s]
-2016-09-06 08:08:48,392 DEBUG: Done:	 Training
-2016-09-06 08:08:48,392 DEBUG: Start:	 Predicting
-2016-09-06 08:08:48,403 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:48,404 DEBUG: Start:	 Training
-2016-09-06 08:08:48,423 DEBUG: Done:	 Predicting
-2016-09-06 08:08:48,423 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:48,424 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:48,424 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,425 INFO: Done:	 Result Analysis
-2016-09-06 08:08:48,425 DEBUG: Info:	 Time for Training: 0.110107898712[s]
-2016-09-06 08:08:48,425 DEBUG: Done:	 Training
-2016-09-06 08:08:48,425 DEBUG: Start:	 Predicting
-2016-09-06 08:08:48,429 DEBUG: Done:	 Predicting
-2016-09-06 08:08:48,429 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:48,430 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:48,430 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,430 INFO: Done:	 Result Analysis
-2016-09-06 08:08:48,564 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:08:48,564 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:48,564 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:08:48,565 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:48,565 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:48,565 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:48,565 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:48,565 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,565 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:48,565 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:48,565 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:48,566 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,648 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:48,649 DEBUG: Start:	 Training
-2016-09-06 08:08:48,668 DEBUG: Info:	 Time for Training: 0.104422092438[s]
-2016-09-06 08:08:48,668 DEBUG: Done:	 Training
-2016-09-06 08:08:48,668 DEBUG: Start:	 Predicting
-2016-09-06 08:08:48,670 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:48,670 DEBUG: Start:	 Training
-2016-09-06 08:08:48,674 DEBUG: Done:	 Predicting
-2016-09-06 08:08:48,674 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:48,675 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:48,675 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,676 INFO: Done:	 Result Analysis
-2016-09-06 08:08:48,690 DEBUG: Info:	 Time for Training: 0.126782894135[s]
-2016-09-06 08:08:48,690 DEBUG: Done:	 Training
-2016-09-06 08:08:48,690 DEBUG: Start:	 Predicting
-2016-09-06 08:08:48,695 DEBUG: Done:	 Predicting
-2016-09-06 08:08:48,695 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:48,697 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:48,697 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9418
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,697 INFO: Done:	 Result Analysis
-2016-09-06 08:08:48,814 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,815 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:08:48,814 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,815 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:48,815 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:08:48,815 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:48,815 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:48,816 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:48,816 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:48,816 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,816 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:48,816 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:48,816 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:48,817 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,874 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:48,875 DEBUG: Start:	 Training
-2016-09-06 08:08:48,876 DEBUG: Info:	 Time for Training: 0.0622010231018[s]
-2016-09-06 08:08:48,876 DEBUG: Done:	 Training
-2016-09-06 08:08:48,876 DEBUG: Start:	 Predicting
-2016-09-06 08:08:48,878 DEBUG: Done:	 Predicting
-2016-09-06 08:08:48,878 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:48,879 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:48,880 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,880 INFO: Done:	 Result Analysis
-2016-09-06 08:08:48,902 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:48,903 DEBUG: Start:	 Training
-2016-09-06 08:08:48,906 DEBUG: Info:	 Time for Training: 0.092826128006[s]
-2016-09-06 08:08:48,907 DEBUG: Done:	 Training
-2016-09-06 08:08:48,907 DEBUG: Start:	 Predicting
-2016-09-06 08:08:48,909 DEBUG: Done:	 Predicting
-2016-09-06 08:08:48,909 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:48,911 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:48,911 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,911 INFO: Done:	 Result Analysis
-2016-09-06 08:08:49,061 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:49,061 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:08:49,061 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:49,062 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:49,062 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:49,062 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:49,062 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:08:49,062 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:49,062 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:49,062 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:49,063 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:49,063 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:49,063 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:49,063 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:49,113 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:49,113 DEBUG: Start:	 Training
-2016-09-06 08:08:49,114 DEBUG: Info:	 Time for Training: 0.0540471076965[s]
-2016-09-06 08:08:49,114 DEBUG: Done:	 Training
-2016-09-06 08:08:49,114 DEBUG: Start:	 Predicting
-2016-09-06 08:08:49,121 DEBUG: Done:	 Predicting
-2016-09-06 08:08:49,121 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:49,122 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:49,122 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 08:08:49,122 INFO: Done:	 Result Analysis
-2016-09-06 08:08:49,693 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:49,693 DEBUG: Start:	 Training
-2016-09-06 08:08:49,747 DEBUG: Info:	 Time for Training: 0.685566186905[s]
-2016-09-06 08:08:49,747 DEBUG: Done:	 Training
-2016-09-06 08:08:49,747 DEBUG: Start:	 Predicting
-2016-09-06 08:08:49,754 DEBUG: Done:	 Predicting
-2016-09-06 08:08:49,754 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:49,755 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:49,755 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 21, max_depth : 29
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:08:49,755 INFO: Done:	 Result Analysis
-2016-09-06 08:08:49,919 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:49,919 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:49,919 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:08:49,919 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:08:49,920 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:49,920 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:08:49,921 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:49,921 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:49,921 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:49,921 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:49,921 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:49,921 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:08:49,921 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:49,921 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:08:50,039 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:50,039 DEBUG: Start:	 Training
-2016-09-06 08:08:50,040 DEBUG: Info:	 Time for Training: 0.121908903122[s]
-2016-09-06 08:08:50,040 DEBUG: Done:	 Training
-2016-09-06 08:08:50,040 DEBUG: Start:	 Predicting
-2016-09-06 08:08:50,042 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:08:50,042 DEBUG: Start:	 Training
-2016-09-06 08:08:50,059 DEBUG: Info:	 Time for Training: 0.140678882599[s]
-2016-09-06 08:08:50,059 DEBUG: Done:	 Training
-2016-09-06 08:08:50,059 DEBUG: Start:	 Predicting
-2016-09-06 08:08:50,061 DEBUG: Done:	 Predicting
-2016-09-06 08:08:50,061 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:50,061 DEBUG: Done:	 Predicting
-2016-09-06 08:08:50,061 DEBUG: Start:	 Getting Results
-2016-09-06 08:08:50,062 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:50,062 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:50,062 INFO: Done:	 Result Analysis
-2016-09-06 08:08:50,063 DEBUG: Done:	 Getting Results
-2016-09-06 08:08:50,063 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:50,063 INFO: Done:	 Result Analysis
-2016-09-06 08:08:50,318 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:50,319 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:08:50,319 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:50,320 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:08:50,321 INFO: Info:	 Shape of View0 :(300, 10)
-2016-09-06 08:08:50,321 INFO: Info:	 Shape of View0 :(300, 10)
-2016-09-06 08:08:50,322 INFO: Info:	 Shape of View1 :(300, 18)
-2016-09-06 08:08:50,322 INFO: Info:	 Shape of View1 :(300, 18)
-2016-09-06 08:08:50,323 INFO: Info:	 Shape of View2 :(300, 19)
-2016-09-06 08:08:50,323 INFO: Info:	 Shape of View2 :(300, 19)
-2016-09-06 08:08:50,323 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 08:08:50,324 INFO: Done:	 Read Database Files
-2016-09-06 08:08:50,324 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 08:08:50,324 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:08:50,324 INFO: Done:	 Read Database Files
-2016-09-06 08:08:50,324 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:08:50,330 INFO: Done:	 Determine validation split
-2016-09-06 08:08:50,330 INFO: Done:	 Determine validation split
-2016-09-06 08:08:50,330 INFO: Start:	 Determine 5 folds
-2016-09-06 08:08:50,330 INFO: Start:	 Determine 5 folds
-2016-09-06 08:08:50,340 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:08:50,340 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:08:50,340 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:08:50,340 INFO: Done:	 Determine folds
-2016-09-06 08:08:50,340 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:08:50,340 INFO: Start:	 Classification
-2016-09-06 08:08:50,341 INFO: 	Start:	 Fold number 1
-2016-09-06 08:08:50,341 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:08:50,341 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:08:50,341 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:08:50,342 INFO: Done:	 Determine folds
-2016-09-06 08:08:50,342 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 08:08:50,342 INFO: Start:	 Classification
-2016-09-06 08:08:50,342 INFO: 	Start:	 Fold number 1
-2016-09-06 08:08:50,397 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:08:50,407 INFO: 	Start: 	 Classification
-2016-09-06 08:08:50,410 DEBUG: 			View 0 : 0.418079096045
-2016-09-06 08:08:50,422 DEBUG: 			View 1 : 0.418079096045
-2016-09-06 08:08:50,433 DEBUG: 			View 2 : 0.418079096045
-2016-09-06 08:08:50,444 DEBUG: 			View 3 : 0.418079096045
-2016-09-06 08:08:50,444 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 08:08:50,481 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:08:50,481 INFO: 	Start:	 Fold number 2
-2016-09-06 08:08:50,508 DEBUG: 			 Best view : 		View0
-2016-09-06 08:08:50,550 INFO: 	Start: 	 Classification
-2016-09-06 08:08:50,626 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:08:50,626 INFO: 	Start:	 Fold number 3
-2016-09-06 08:08:50,627 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:08:50,640 DEBUG: 			View 0 : 0.723163841808
-2016-09-06 08:08:50,653 DEBUG: 			View 1 : 0.649717514124
-2016-09-06 08:08:50,665 DEBUG: 			View 2 : 0.683615819209
-2016-09-06 08:08:50,675 DEBUG: 			View 3 : 0.666666666667
-2016-09-06 08:08:50,691 INFO: 	Start: 	 Classification
-2016-09-06 08:08:50,749 DEBUG: 			 Best view : 		View0
-2016-09-06 08:08:50,767 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:08:50,768 INFO: 	Start:	 Fold number 4
-2016-09-06 08:08:50,831 INFO: 	Start: 	 Classification
-2016-09-06 08:08:50,906 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:08:50,906 INFO: 	Start:	 Fold number 5
-2016-09-06 08:08:50,953 INFO: 	Start: 	 Classification
-2016-09-06 08:08:50,965 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:08:50,972 DEBUG: 			View 0 : 0.723163841808
-2016-09-06 08:08:50,980 DEBUG: 			View 1 : 0.649717514124
-2016-09-06 08:08:50,988 DEBUG: 			View 2 : 0.683615819209
-2016-09-06 08:08:50,996 DEBUG: 			View 3 : 0.666666666667
-2016-09-06 08:08:50,999 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:08:50,999 INFO: Done:	 Classification
-2016-09-06 08:08:50,999 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:08:50,999 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:08:51,004 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 58.6889730599
-	-On Test : 58.5365853659
-	-On Validation : 58.8764044944
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 49
-		- K nearest Neighbors with  n_neighbors: 48
-		- SVM Linear with C : 9418
-		- K nearest Neighbors with  n_neighbors: 48
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:08:51,004 INFO: Done:	 Result Analysis
-2016-09-06 08:08:51,042 DEBUG: 			 Best view : 		View0
-2016-09-06 08:08:51,279 INFO: 	Start: 	 Classification
-2016-09-06 08:08:51,627 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:08:51,627 INFO: 	Start:	 Fold number 2
-2016-09-06 08:08:51,658 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:08:51,664 DEBUG: 			View 0 : 0.424731182796
-2016-09-06 08:08:51,671 DEBUG: 			View 1 : 0.424731182796
-2016-09-06 08:08:51,678 DEBUG: 			View 2 : 0.424731182796
-2016-09-06 08:08:51,685 DEBUG: 			View 3 : 0.424731182796
-2016-09-06 08:08:51,685 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 08:08:51,718 DEBUG: 			 Best view : 		View0
-2016-09-06 08:08:51,800 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:08:51,807 DEBUG: 			View 0 : 0.682795698925
-2016-09-06 08:08:51,815 DEBUG: 			View 1 : 0.731182795699
-2016-09-06 08:08:51,822 DEBUG: 			View 2 : 0.709677419355
-2016-09-06 08:08:51,829 DEBUG: 			View 3 : 0.666666666667
-2016-09-06 08:08:51,868 DEBUG: 			 Best view : 		View1
-2016-09-06 08:08:52,019 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:08:52,026 DEBUG: 			View 0 : 0.682795698925
-2016-09-06 08:08:52,034 DEBUG: 			View 1 : 0.731182795699
-2016-09-06 08:08:52,041 DEBUG: 			View 2 : 0.709677419355
-2016-09-06 08:08:52,048 DEBUG: 			View 3 : 0.666666666667
-2016-09-06 08:08:52,090 DEBUG: 			 Best view : 		View1
-2016-09-06 08:08:52,311 INFO: 	Start: 	 Classification
-2016-09-06 08:08:52,667 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:08:52,667 INFO: 	Start:	 Fold number 3
-2016-09-06 08:08:52,697 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:08:52,704 DEBUG: 			View 0 : 0.421621621622
-2016-09-06 08:08:52,710 DEBUG: 			View 1 : 0.421621621622
-2016-09-06 08:08:52,717 DEBUG: 			View 2 : 0.421621621622
-2016-09-06 08:08:52,724 DEBUG: 			View 3 : 0.421621621622
-2016-09-06 08:08:52,724 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 08:08:52,757 DEBUG: 			 Best view : 		View0
-2016-09-06 08:08:52,838 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:08:52,845 DEBUG: 			View 0 : 0.675675675676
-2016-09-06 08:08:52,853 DEBUG: 			View 1 : 0.713513513514
-2016-09-06 08:08:52,860 DEBUG: 			View 2 : 0.648648648649
-2016-09-06 08:08:52,867 DEBUG: 			View 3 : 0.664864864865
-2016-09-06 08:08:52,908 DEBUG: 			 Best view : 		View1
-2016-09-06 08:08:53,059 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:08:53,066 DEBUG: 			View 0 : 0.675675675676
-2016-09-06 08:08:53,074 DEBUG: 			View 1 : 0.713513513514
-2016-09-06 08:08:53,081 DEBUG: 			View 2 : 0.648648648649
-2016-09-06 08:08:53,088 DEBUG: 			View 3 : 0.664864864865
-2016-09-06 08:08:53,131 DEBUG: 			 Best view : 		View1
-2016-09-06 08:08:53,352 INFO: 	Start: 	 Classification
-2016-09-06 08:08:53,708 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:08:53,708 INFO: 	Start:	 Fold number 4
-2016-09-06 08:08:53,752 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:08:53,761 DEBUG: 			View 0 : 0.585635359116
-2016-09-06 08:08:53,767 DEBUG: 			View 1 : 0.585635359116
-2016-09-06 08:08:53,775 DEBUG: 			View 2 : 0.585635359116
-2016-09-06 08:08:53,782 DEBUG: 			View 3 : 0.585635359116
-2016-09-06 08:08:53,819 DEBUG: 			 Best view : 		View0
-2016-09-06 08:08:53,907 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:08:53,917 DEBUG: 			View 0 : 0.635359116022
-2016-09-06 08:08:53,924 DEBUG: 			View 1 : 0.729281767956
-2016-09-06 08:08:53,934 DEBUG: 			View 2 : 0.657458563536
-2016-09-06 08:08:53,940 DEBUG: 			View 3 : 0.685082872928
-2016-09-06 08:08:53,985 DEBUG: 			 Best view : 		View1
-2016-09-06 08:08:54,147 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:08:54,156 DEBUG: 			View 0 : 0.635359116022
-2016-09-06 08:08:54,164 DEBUG: 			View 1 : 0.729281767956
-2016-09-06 08:08:54,173 DEBUG: 			View 2 : 0.657458563536
-2016-09-06 08:08:54,180 DEBUG: 			View 3 : 0.685082872928
-2016-09-06 08:08:54,228 DEBUG: 			 Best view : 		View1
-2016-09-06 08:08:54,460 INFO: 	Start: 	 Classification
-2016-09-06 08:08:54,814 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:08:54,814 INFO: 	Start:	 Fold number 5
-2016-09-06 08:08:54,844 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:08:54,851 DEBUG: 			View 0 : 0.406593406593
-2016-09-06 08:08:54,857 DEBUG: 			View 1 : 0.406593406593
-2016-09-06 08:08:54,864 DEBUG: 			View 2 : 0.406593406593
-2016-09-06 08:08:54,871 DEBUG: 			View 3 : 0.406593406593
-2016-09-06 08:08:54,871 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 08:08:54,904 DEBUG: 			 Best view : 		View0
-2016-09-06 08:08:54,985 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:08:54,992 DEBUG: 			View 0 : 0.725274725275
-2016-09-06 08:08:55,000 DEBUG: 			View 1 : 0.747252747253
-2016-09-06 08:08:55,007 DEBUG: 			View 2 : 0.714285714286
-2016-09-06 08:08:55,014 DEBUG: 			View 3 : 0.675824175824
-2016-09-06 08:08:55,053 DEBUG: 			 Best view : 		View1
-2016-09-06 08:08:55,204 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:08:55,211 DEBUG: 			View 0 : 0.725274725275
-2016-09-06 08:08:55,219 DEBUG: 			View 1 : 0.747252747253
-2016-09-06 08:08:55,226 DEBUG: 			View 2 : 0.714285714286
-2016-09-06 08:08:55,233 DEBUG: 			View 3 : 0.675824175824
-2016-09-06 08:08:55,274 DEBUG: 			 Best view : 		View1
-2016-09-06 08:08:55,492 INFO: 	Start: 	 Classification
-2016-09-06 08:08:55,848 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:08:55,849 INFO: Done:	 Classification
-2016-09-06 08:08:55,849 INFO: Info:	 Time for Classification: 5[s]
-2016-09-06 08:08:55,849 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 08:08:57,746 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 72.7748989743
-	-On Test : 51.2195121951
-	-On Validation : 68.5393258427Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 10), View1 of shape (300, 18), View2 of shape (300, 19), View3 of shape (300, 5)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:01        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:04        0:00:00
-	         Fold 5        0:00:05        0:00:00
-	          Total        0:00:15        0:00:01
-	So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.186440677966
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.171751412429
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.178531073446
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.175141242938
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.179032258065
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.188709677419
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.184408602151
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.175806451613
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.177297297297
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.184864864865
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.171891891892
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.175135135135
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.185635359116
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.204419889503
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.190055248619
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.195580110497
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.185714285714
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.19010989011
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.183516483516
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.175824175824
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 58.1920903955
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 57.5268817204
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 57.8378378378
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 58.5635359116
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 59.3406593407
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 72.3163841808
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.7865168539
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.1182795699
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 71.3513513514
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 72.9281767956
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 74.7252747253
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View1
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 71.7514124294
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.1182795699
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 71.3513513514
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 72.9281767956
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 74.7252747253
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View1
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 58.1920903955
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 57.5268817204
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 57.8378378378
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 58.5635359116
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 59.3406593407
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-2016-09-06 08:08:57,922 INFO: Done:	 Result Analysis
-2016-09-06 08:08:58,079 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:58,080 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:08:58,080 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:58,080 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:08:58,080 INFO: Info:	 Shape of View0 :(300, 10)
-2016-09-06 08:08:58,081 INFO: Info:	 Shape of View0 :(300, 10)
-2016-09-06 08:08:58,081 INFO: Info:	 Shape of View1 :(300, 18)
-2016-09-06 08:08:58,081 INFO: Info:	 Shape of View1 :(300, 18)
-2016-09-06 08:08:58,082 INFO: Info:	 Shape of View2 :(300, 19)
-2016-09-06 08:08:58,082 INFO: Info:	 Shape of View2 :(300, 19)
-2016-09-06 08:08:58,082 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 08:08:58,083 INFO: Done:	 Read Database Files
-2016-09-06 08:08:58,083 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:08:58,083 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 08:08:58,083 INFO: Done:	 Read Database Files
-2016-09-06 08:08:58,083 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:08:58,087 INFO: Done:	 Determine validation split
-2016-09-06 08:08:58,087 INFO: Start:	 Determine 5 folds
-2016-09-06 08:08:58,089 INFO: Done:	 Determine validation split
-2016-09-06 08:08:58,089 INFO: Start:	 Determine 5 folds
-2016-09-06 08:08:58,095 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:08:58,095 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:08:58,095 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:08:58,095 INFO: Done:	 Determine folds
-2016-09-06 08:08:58,095 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:08:58,095 INFO: Start:	 Classification
-2016-09-06 08:08:58,095 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:08:58,095 INFO: 	Start:	 Fold number 1
-2016-09-06 08:08:58,095 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:08:58,095 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:08:58,096 INFO: Done:	 Determine folds
-2016-09-06 08:08:58,096 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:08:58,096 INFO: Start:	 Classification
-2016-09-06 08:08:58,096 INFO: 	Start:	 Fold number 1
-2016-09-06 08:08:58,144 INFO: 	Start: 	 Classification
-2016-09-06 08:08:58,164 INFO: 	Start: 	 Classification
-2016-09-06 08:08:58,209 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:08:58,209 INFO: 	Start:	 Fold number 2
-2016-09-06 08:08:58,245 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:08:58,245 INFO: 	Start:	 Fold number 2
-2016-09-06 08:08:58,275 INFO: 	Start: 	 Classification
-2016-09-06 08:08:58,292 INFO: 	Start: 	 Classification
-2016-09-06 08:08:58,318 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:08:58,318 INFO: 	Start:	 Fold number 3
-2016-09-06 08:08:58,383 INFO: 	Start: 	 Classification
-2016-09-06 08:08:58,395 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:08:58,395 INFO: 	Start:	 Fold number 3
-2016-09-06 08:08:58,426 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:08:58,426 INFO: 	Start:	 Fold number 4
-2016-09-06 08:08:58,438 INFO: 	Start: 	 Classification
-2016-09-06 08:08:58,493 INFO: 	Start: 	 Classification
-2016-09-06 08:08:58,537 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:08:58,537 INFO: 	Start:	 Fold number 5
-2016-09-06 08:08:58,540 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:08:58,541 INFO: 	Start:	 Fold number 4
-2016-09-06 08:08:58,582 INFO: 	Start: 	 Classification
-2016-09-06 08:08:58,605 INFO: 	Start: 	 Classification
-2016-09-06 08:08:58,649 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:08:58,649 INFO: Done:	 Classification
-2016-09-06 08:08:58,649 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:08:58,650 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:08:58,654 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 58.5365853659
-	-On Validation : 88.7640449438
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 49
-		- K nearest Neighbors with  n_neighbors: 48
-		- SVM Linear with C : 9418
-		- K nearest Neighbors with  n_neighbors: 48
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:08:58,654 INFO: Done:	 Result Analysis
-2016-09-06 08:08:58,680 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:08:58,680 INFO: 	Start:	 Fold number 5
-2016-09-06 08:08:58,723 INFO: 	Start: 	 Classification
-2016-09-06 08:08:58,820 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:08:58,820 INFO: Done:	 Classification
-2016-09-06 08:08:58,820 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:08:58,820 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:08:58,824 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 60.3193359885
-	-On Test : 58.5365853659
-	-On Validation : 60.4494382022
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 49
-		- K nearest Neighbors with  n_neighbors: 48
-		- SVM Linear with C : 9418
-		- K nearest Neighbors with  n_neighbors: 48
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:08:58,824 INFO: Done:	 Result Analysis
-2016-09-06 08:08:58,929 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:58,929 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:58,930 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:08:58,930 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:08:58,930 INFO: Info:	 Shape of View0 :(300, 10)
-2016-09-06 08:08:58,930 INFO: Info:	 Shape of View0 :(300, 10)
-2016-09-06 08:08:58,931 INFO: Info:	 Shape of View1 :(300, 18)
-2016-09-06 08:08:58,931 INFO: Info:	 Shape of View1 :(300, 18)
-2016-09-06 08:08:58,931 INFO: Info:	 Shape of View2 :(300, 19)
-2016-09-06 08:08:58,932 INFO: Info:	 Shape of View2 :(300, 19)
-2016-09-06 08:08:58,932 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 08:08:58,932 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 08:08:58,932 INFO: Done:	 Read Database Files
-2016-09-06 08:08:58,933 INFO: Done:	 Read Database Files
-2016-09-06 08:08:58,933 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:08:58,933 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:08:58,940 INFO: Done:	 Determine validation split
-2016-09-06 08:08:58,940 INFO: Start:	 Determine 5 folds
-2016-09-06 08:08:58,942 INFO: Done:	 Determine validation split
-2016-09-06 08:08:58,942 INFO: Start:	 Determine 5 folds
-2016-09-06 08:08:58,947 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:08:58,948 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:08:58,948 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:08:58,948 INFO: Done:	 Determine folds
-2016-09-06 08:08:58,948 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:08:58,948 INFO: Start:	 Classification
-2016-09-06 08:08:58,948 INFO: 	Start:	 Fold number 1
-2016-09-06 08:08:58,951 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:08:58,951 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:08:58,951 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:08:58,951 INFO: Done:	 Determine folds
-2016-09-06 08:08:58,951 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:08:58,951 INFO: Start:	 Classification
-2016-09-06 08:08:58,952 INFO: 	Start:	 Fold number 1
-2016-09-06 08:08:58,990 INFO: 	Start: 	 Classification
-2016-09-06 08:08:58,994 INFO: 	Start: 	 Classification
-2016-09-06 08:08:59,020 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:08:59,020 INFO: 	Start:	 Fold number 2
-2016-09-06 08:08:59,047 INFO: 	Start: 	 Classification
-2016-09-06 08:08:59,078 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:08:59,079 INFO: 	Start:	 Fold number 3
-2016-09-06 08:08:59,106 INFO: 	Start: 	 Classification
-2016-09-06 08:08:59,134 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:08:59,134 INFO: 	Start:	 Fold number 4
-2016-09-06 08:08:59,163 INFO: 	Start: 	 Classification
-2016-09-06 08:08:59,192 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:08:59,192 INFO: 	Start:	 Fold number 5
-2016-09-06 08:08:59,218 INFO: 	Start: 	 Classification
-2016-09-06 08:08:59,249 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:08:59,249 INFO: Done:	 Classification
-2016-09-06 08:08:59,249 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:08:59,249 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:08:59,254 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 52.1951219512
-	-On Validation : 87.191011236
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:08:59,255 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bc68c031..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e44f25c9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cc696100..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 49
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7bcd03e6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b3acd10c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d66d65e3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 21, max_depth : 29
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index eb325db8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8c02c6bd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fddb5536..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 53820b69..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d49255a9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dd1c0db1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 16, max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cb31d3e0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cfbc23e9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 595f1759..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6dfced7a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6a32a488..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 192c222e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9418
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75c23f19..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8ba8b136..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f3b9d99b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 39843a28..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 21, max_depth : 29
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 477543be..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5210714f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e1fc28cf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9418
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0fb8d083..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e34c4d68..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 78e057af..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 21, max_depth : 29
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-Fusion-LateFusion-BayesianInference-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-Fusion-LateFusion-BayesianInference-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 49ba6c06..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-Fusion-LateFusion-BayesianInference-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 58.6889730599
-	-On Test : 58.5365853659
-	-On Validation : 58.8764044944
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 49
-		- K nearest Neighbors with  n_neighbors: 48
-		- SVM Linear with C : 9418
-		- K nearest Neighbors with  n_neighbors: 48
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f0efbadd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1a399586..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9265
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080857Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-080857Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index d6b6883e5caebf37821340382ae1eabf20654c03..0000000000000000000000000000000000000000
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080857Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080857Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5f59f5e7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080857Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,209 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 72.7748989743
-	-On Test : 51.2195121951
-	-On Validation : 68.5393258427Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 10), View1 of shape (300, 18), View2 of shape (300, 19), View3 of shape (300, 5)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:01        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:04        0:00:00
-	         Fold 5        0:00:05        0:00:00
-	          Total        0:00:15        0:00:01
-	So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.186440677966
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.171751412429
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.178531073446
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.175141242938
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.179032258065
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.188709677419
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.184408602151
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.175806451613
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.177297297297
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.184864864865
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.171891891892
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.175135135135
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.185635359116
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.204419889503
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.190055248619
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.195580110497
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.185714285714
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.19010989011
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.183516483516
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.175824175824
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 58.1920903955
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 57.5268817204
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 57.8378378378
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 58.5635359116
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 59.3406593407
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 72.3163841808
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.7865168539
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.1182795699
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 71.3513513514
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 72.9281767956
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 74.7252747253
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View1
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 71.7514124294
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.1182795699
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 71.3513513514
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 72.9281767956
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 74.7252747253
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View1
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 58.1920903955
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 57.5268817204
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 57.8378378378
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 58.5635359116
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 59.3406593407
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-MajorityVoting-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-MajorityVoting-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 66ff9806..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-MajorityVoting-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 60.3193359885
-	-On Test : 58.5365853659
-	-On Validation : 60.4494382022
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 49
-		- K nearest Neighbors with  n_neighbors: 48
-		- SVM Linear with C : 9418
-		- K nearest Neighbors with  n_neighbors: 48
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-SVMForLinear-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-SVMForLinear-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 36bb4115..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-SVMForLinear-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 58.5365853659
-	-On Validation : 88.7640449438
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 49
-		- K nearest Neighbors with  n_neighbors: 48
-		- SVM Linear with C : 9418
-		- K nearest Neighbors with  n_neighbors: 48
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080859Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080859Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index cde5d32e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080859Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 52.1951219512
-	-On Validation : 87.191011236
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-080940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 1c841d1c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1580 +0,0 @@
-2016-09-06 08:09:40,131 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:09:40,131 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00013103125 Gbytes /!\ 
-2016-09-06 08:09:45,141 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 08:09:45,143 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 08:09:45,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,196 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:09:45,196 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:09:45,196 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:45,196 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:45,197 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,197 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,197 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,197 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,197 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:45,197 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:45,198 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:45,198 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:45,253 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:45,253 DEBUG: Start:	 Training
-2016-09-06 08:09:45,255 DEBUG: Info:	 Time for Training: 0.0599348545074[s]
-2016-09-06 08:09:45,255 DEBUG: Done:	 Training
-2016-09-06 08:09:45,255 DEBUG: Start:	 Predicting
-2016-09-06 08:09:45,258 DEBUG: Done:	 Predicting
-2016-09-06 08:09:45,258 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:45,259 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:45,259 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:09:45,259 INFO: Done:	 Result Analysis
-2016-09-06 08:09:45,286 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:45,286 DEBUG: Start:	 Training
-2016-09-06 08:09:45,290 DEBUG: Info:	 Time for Training: 0.095223903656[s]
-2016-09-06 08:09:45,290 DEBUG: Done:	 Training
-2016-09-06 08:09:45,290 DEBUG: Start:	 Predicting
-2016-09-06 08:09:45,293 DEBUG: Done:	 Predicting
-2016-09-06 08:09:45,293 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:45,295 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:45,295 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:09:45,295 INFO: Done:	 Result Analysis
-2016-09-06 08:09:45,443 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,443 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:09:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:09:45,443 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:45,443 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:45,444 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,444 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,444 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,444 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:45,444 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,444 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:45,444 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:45,445 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:45,496 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:45,497 DEBUG: Start:	 Training
-2016-09-06 08:09:45,497 DEBUG: Info:	 Time for Training: 0.05499792099[s]
-2016-09-06 08:09:45,497 DEBUG: Done:	 Training
-2016-09-06 08:09:45,497 DEBUG: Start:	 Predicting
-2016-09-06 08:09:45,502 DEBUG: Done:	 Predicting
-2016-09-06 08:09:45,502 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:45,503 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:45,503 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.780952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.780952380952
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:09:45,504 INFO: Done:	 Result Analysis
-2016-09-06 08:09:45,806 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:45,806 DEBUG: Start:	 Training
-2016-09-06 08:09:45,850 DEBUG: Info:	 Time for Training: 0.407772064209[s]
-2016-09-06 08:09:45,850 DEBUG: Done:	 Training
-2016-09-06 08:09:45,850 DEBUG: Start:	 Predicting
-2016-09-06 08:09:45,856 DEBUG: Done:	 Predicting
-2016-09-06 08:09:45,856 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:45,857 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:45,857 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 17, max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:09:45,857 INFO: Done:	 Result Analysis
-2016-09-06 08:09:45,992 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,992 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,992 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:09:45,992 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:09:45,992 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:45,992 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:45,993 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,993 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,993 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,993 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,993 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:45,993 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:45,993 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:45,993 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,067 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:46,067 DEBUG: Start:	 Training
-2016-09-06 08:09:46,068 DEBUG: Info:	 Time for Training: 0.076208114624[s]
-2016-09-06 08:09:46,068 DEBUG: Done:	 Training
-2016-09-06 08:09:46,068 DEBUG: Start:	 Predicting
-2016-09-06 08:09:46,078 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:46,078 DEBUG: Start:	 Training
-2016-09-06 08:09:46,101 DEBUG: Done:	 Predicting
-2016-09-06 08:09:46,101 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:46,103 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:46,103 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,103 INFO: Done:	 Result Analysis
-2016-09-06 08:09:46,103 DEBUG: Info:	 Time for Training: 0.111602067947[s]
-2016-09-06 08:09:46,103 DEBUG: Done:	 Training
-2016-09-06 08:09:46,103 DEBUG: Start:	 Predicting
-2016-09-06 08:09:46,106 DEBUG: Done:	 Predicting
-2016-09-06 08:09:46,106 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:46,108 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:46,108 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,108 INFO: Done:	 Result Analysis
-2016-09-06 08:09:46,239 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,239 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,239 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:09:46,239 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:09:46,239 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:46,239 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:46,240 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:46,240 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:46,240 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:46,240 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:46,240 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:46,240 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:46,241 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,241 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,321 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:46,321 DEBUG: Start:	 Training
-2016-09-06 08:09:46,332 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:46,332 DEBUG: Start:	 Training
-2016-09-06 08:09:46,338 DEBUG: Info:	 Time for Training: 0.0998349189758[s]
-2016-09-06 08:09:46,338 DEBUG: Done:	 Training
-2016-09-06 08:09:46,338 DEBUG: Start:	 Predicting
-2016-09-06 08:09:46,344 DEBUG: Done:	 Predicting
-2016-09-06 08:09:46,344 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:46,345 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:46,345 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,345 INFO: Done:	 Result Analysis
-2016-09-06 08:09:46,350 DEBUG: Info:	 Time for Training: 0.111637830734[s]
-2016-09-06 08:09:46,350 DEBUG: Done:	 Training
-2016-09-06 08:09:46,350 DEBUG: Start:	 Predicting
-2016-09-06 08:09:46,354 DEBUG: Done:	 Predicting
-2016-09-06 08:09:46,354 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:46,355 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:46,355 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,355 INFO: Done:	 Result Analysis
-2016-09-06 08:09:46,495 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,495 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,496 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:09:46,496 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:09:46,496 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:46,496 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:46,497 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:46,497 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:46,497 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:46,497 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:46,497 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:46,497 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:46,497 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,497 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,578 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:46,579 DEBUG: Start:	 Training
-2016-09-06 08:09:46,580 DEBUG: Info:	 Time for Training: 0.0850560665131[s]
-2016-09-06 08:09:46,580 DEBUG: Done:	 Training
-2016-09-06 08:09:46,580 DEBUG: Start:	 Predicting
-2016-09-06 08:09:46,583 DEBUG: Done:	 Predicting
-2016-09-06 08:09:46,584 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:46,585 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:46,585 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.666666666667
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.666666666667
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,586 INFO: Done:	 Result Analysis
-2016-09-06 08:09:46,609 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:46,609 DEBUG: Start:	 Training
-2016-09-06 08:09:46,612 DEBUG: Info:	 Time for Training: 0.117750167847[s]
-2016-09-06 08:09:46,612 DEBUG: Done:	 Training
-2016-09-06 08:09:46,612 DEBUG: Start:	 Predicting
-2016-09-06 08:09:46,615 DEBUG: Done:	 Predicting
-2016-09-06 08:09:46,615 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:46,617 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:46,617 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,617 INFO: Done:	 Result Analysis
-2016-09-06 08:09:46,744 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,744 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,744 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:09:46,744 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:09:46,744 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:46,744 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:46,745 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:46,745 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:46,745 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:46,745 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:46,745 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:46,745 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:46,745 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,745 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,801 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:46,801 DEBUG: Start:	 Training
-2016-09-06 08:09:46,802 DEBUG: Info:	 Time for Training: 0.0580749511719[s]
-2016-09-06 08:09:46,802 DEBUG: Done:	 Training
-2016-09-06 08:09:46,802 DEBUG: Start:	 Predicting
-2016-09-06 08:09:46,807 DEBUG: Done:	 Predicting
-2016-09-06 08:09:46,807 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:46,808 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:46,809 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.733333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.733333333333
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,809 INFO: Done:	 Result Analysis
-2016-09-06 08:09:47,068 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:47,068 DEBUG: Start:	 Training
-2016-09-06 08:09:47,075 DEBUG: Info:	 Time for Training: 0.331625938416[s]
-2016-09-06 08:09:47,075 DEBUG: Done:	 Training
-2016-09-06 08:09:47,075 DEBUG: Start:	 Predicting
-2016-09-06 08:09:47,078 DEBUG: Done:	 Predicting
-2016-09-06 08:09:47,079 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:47,080 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:47,080 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.714285714286
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.714285714286
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,080 INFO: Done:	 Result Analysis
-2016-09-06 08:09:47,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,196 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:09:47,196 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:09:47,197 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:47,197 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:47,198 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:47,198 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:47,198 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:47,198 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:47,198 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:47,198 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:47,199 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,199 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,313 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:47,314 DEBUG: Start:	 Training
-2016-09-06 08:09:47,315 DEBUG: Info:	 Time for Training: 0.119544029236[s]
-2016-09-06 08:09:47,315 DEBUG: Done:	 Training
-2016-09-06 08:09:47,315 DEBUG: Start:	 Predicting
-2016-09-06 08:09:47,320 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:47,320 DEBUG: Start:	 Training
-2016-09-06 08:09:47,331 DEBUG: Done:	 Predicting
-2016-09-06 08:09:47,331 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:47,333 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:47,333 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,333 INFO: Done:	 Result Analysis
-2016-09-06 08:09:47,343 DEBUG: Info:	 Time for Training: 0.147957086563[s]
-2016-09-06 08:09:47,343 DEBUG: Done:	 Training
-2016-09-06 08:09:47,343 DEBUG: Start:	 Predicting
-2016-09-06 08:09:47,346 DEBUG: Done:	 Predicting
-2016-09-06 08:09:47,346 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:47,347 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:47,347 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,348 INFO: Done:	 Result Analysis
-2016-09-06 08:09:47,444 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,444 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,444 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:09:47,444 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:09:47,445 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:47,445 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:47,445 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:47,445 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:47,446 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:47,446 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:47,446 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:47,446 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:47,446 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,446 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,565 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:47,565 DEBUG: Start:	 Training
-2016-09-06 08:09:47,577 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:47,577 DEBUG: Start:	 Training
-2016-09-06 08:09:47,589 DEBUG: Info:	 Time for Training: 0.145581960678[s]
-2016-09-06 08:09:47,589 DEBUG: Done:	 Training
-2016-09-06 08:09:47,589 DEBUG: Start:	 Predicting
-2016-09-06 08:09:47,596 DEBUG: Done:	 Predicting
-2016-09-06 08:09:47,596 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:47,598 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:47,598 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,598 INFO: Done:	 Result Analysis
-2016-09-06 08:09:47,601 DEBUG: Info:	 Time for Training: 0.157520055771[s]
-2016-09-06 08:09:47,601 DEBUG: Done:	 Training
-2016-09-06 08:09:47,601 DEBUG: Start:	 Predicting
-2016-09-06 08:09:47,604 DEBUG: Done:	 Predicting
-2016-09-06 08:09:47,604 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:47,605 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:47,605 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.695238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9103
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.695238095238
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,605 INFO: Done:	 Result Analysis
-2016-09-06 08:09:47,692 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,692 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,692 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:09:47,692 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:09:47,692 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:47,692 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:47,692 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:47,692 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:47,693 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:47,693 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:47,693 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:47,693 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:47,693 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,693 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,754 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:47,754 DEBUG: Start:	 Training
-2016-09-06 08:09:47,757 DEBUG: Info:	 Time for Training: 0.065906047821[s]
-2016-09-06 08:09:47,757 DEBUG: Done:	 Training
-2016-09-06 08:09:47,757 DEBUG: Start:	 Predicting
-2016-09-06 08:09:47,760 DEBUG: Done:	 Predicting
-2016-09-06 08:09:47,760 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:47,761 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:47,761 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,761 INFO: Done:	 Result Analysis
-2016-09-06 08:09:47,795 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:47,795 DEBUG: Start:	 Training
-2016-09-06 08:09:47,800 DEBUG: Info:	 Time for Training: 0.109374046326[s]
-2016-09-06 08:09:47,801 DEBUG: Done:	 Training
-2016-09-06 08:09:47,801 DEBUG: Start:	 Predicting
-2016-09-06 08:09:47,804 DEBUG: Done:	 Predicting
-2016-09-06 08:09:47,804 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:47,805 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:47,805 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,805 INFO: Done:	 Result Analysis
-2016-09-06 08:09:47,942 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,942 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:09:47,943 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:47,943 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:47,943 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:47,944 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:47,943 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,944 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,944 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:09:47,944 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:47,945 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:47,945 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:47,945 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:47,945 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,995 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:47,995 DEBUG: Start:	 Training
-2016-09-06 08:09:47,995 DEBUG: Info:	 Time for Training: 0.0538508892059[s]
-2016-09-06 08:09:47,996 DEBUG: Done:	 Training
-2016-09-06 08:09:47,996 DEBUG: Start:	 Predicting
-2016-09-06 08:09:48,001 DEBUG: Done:	 Predicting
-2016-09-06 08:09:48,001 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:48,002 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:48,002 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.738095238095
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,002 INFO: Done:	 Result Analysis
-2016-09-06 08:09:48,305 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:48,305 DEBUG: Start:	 Training
-2016-09-06 08:09:48,313 DEBUG: Info:	 Time for Training: 0.370137929916[s]
-2016-09-06 08:09:48,313 DEBUG: Done:	 Training
-2016-09-06 08:09:48,313 DEBUG: Start:	 Predicting
-2016-09-06 08:09:48,318 DEBUG: Done:	 Predicting
-2016-09-06 08:09:48,318 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:48,319 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:48,319 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.728571428571
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.728571428571
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,319 INFO: Done:	 Result Analysis
-2016-09-06 08:09:48,392 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,392 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:09:48,393 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:48,393 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,393 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:09:48,394 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:48,394 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:48,394 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:48,394 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:48,394 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,394 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:48,395 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:48,395 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:48,395 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,467 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:48,467 DEBUG: Start:	 Training
-2016-09-06 08:09:48,468 DEBUG: Info:	 Time for Training: 0.0765888690948[s]
-2016-09-06 08:09:48,468 DEBUG: Done:	 Training
-2016-09-06 08:09:48,468 DEBUG: Start:	 Predicting
-2016-09-06 08:09:48,490 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:48,490 DEBUG: Start:	 Training
-2016-09-06 08:09:48,492 DEBUG: Done:	 Predicting
-2016-09-06 08:09:48,492 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:48,494 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:48,494 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,495 INFO: Done:	 Result Analysis
-2016-09-06 08:09:48,515 DEBUG: Info:	 Time for Training: 0.12251996994[s]
-2016-09-06 08:09:48,515 DEBUG: Done:	 Training
-2016-09-06 08:09:48,515 DEBUG: Start:	 Predicting
-2016-09-06 08:09:48,519 DEBUG: Done:	 Predicting
-2016-09-06 08:09:48,519 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:48,520 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:48,520 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,520 INFO: Done:	 Result Analysis
-2016-09-06 08:09:48,637 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,637 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,637 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:09:48,637 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:09:48,637 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:48,637 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:48,638 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:48,638 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:48,638 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:48,638 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:48,638 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:48,638 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:48,638 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,638 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,723 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:48,723 DEBUG: Start:	 Training
-2016-09-06 08:09:48,744 DEBUG: Info:	 Time for Training: 0.107270956039[s]
-2016-09-06 08:09:48,744 DEBUG: Done:	 Training
-2016-09-06 08:09:48,744 DEBUG: Start:	 Predicting
-2016-09-06 08:09:48,748 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:48,748 DEBUG: Start:	 Training
-2016-09-06 08:09:48,750 DEBUG: Done:	 Predicting
-2016-09-06 08:09:48,750 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:48,751 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:48,752 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,752 INFO: Done:	 Result Analysis
-2016-09-06 08:09:48,768 DEBUG: Info:	 Time for Training: 0.132148981094[s]
-2016-09-06 08:09:48,769 DEBUG: Done:	 Training
-2016-09-06 08:09:48,769 DEBUG: Start:	 Predicting
-2016-09-06 08:09:48,773 DEBUG: Done:	 Predicting
-2016-09-06 08:09:48,773 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:48,774 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:48,774 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,774 INFO: Done:	 Result Analysis
-2016-09-06 08:09:48,886 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,886 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,887 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:09:48,887 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:09:48,887 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:48,887 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:48,887 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:48,887 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:48,887 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:48,887 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:48,888 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:48,888 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:48,888 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,888 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,942 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:48,942 DEBUG: Start:	 Training
-2016-09-06 08:09:48,943 DEBUG: Info:	 Time for Training: 0.0570180416107[s]
-2016-09-06 08:09:48,943 DEBUG: Done:	 Training
-2016-09-06 08:09:48,943 DEBUG: Start:	 Predicting
-2016-09-06 08:09:48,946 DEBUG: Done:	 Predicting
-2016-09-06 08:09:48,946 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:48,947 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:48,947 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.685714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.685714285714
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,947 INFO: Done:	 Result Analysis
-2016-09-06 08:09:48,972 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:48,972 DEBUG: Start:	 Training
-2016-09-06 08:09:48,976 DEBUG: Info:	 Time for Training: 0.0900959968567[s]
-2016-09-06 08:09:48,976 DEBUG: Done:	 Training
-2016-09-06 08:09:48,976 DEBUG: Start:	 Predicting
-2016-09-06 08:09:48,979 DEBUG: Done:	 Predicting
-2016-09-06 08:09:48,979 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:48,980 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:48,980 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.388888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.388888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,981 INFO: Done:	 Result Analysis
-2016-09-06 08:09:49,135 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:49,135 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:49,135 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:09:49,135 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:09:49,135 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:49,135 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:49,136 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:49,136 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:49,136 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:49,136 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:49,136 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:49,136 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:49,136 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:49,136 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:49,195 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:49,195 DEBUG: Start:	 Training
-2016-09-06 08:09:49,195 DEBUG: Info:	 Time for Training: 0.0612609386444[s]
-2016-09-06 08:09:49,196 DEBUG: Done:	 Training
-2016-09-06 08:09:49,196 DEBUG: Start:	 Predicting
-2016-09-06 08:09:49,200 DEBUG: Done:	 Predicting
-2016-09-06 08:09:49,200 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:49,201 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:49,202 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.728571428571
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.728571428571
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:09:49,202 INFO: Done:	 Result Analysis
-2016-09-06 08:09:49,466 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:49,466 DEBUG: Start:	 Training
-2016-09-06 08:09:49,474 DEBUG: Info:	 Time for Training: 0.339534044266[s]
-2016-09-06 08:09:49,474 DEBUG: Done:	 Training
-2016-09-06 08:09:49,474 DEBUG: Start:	 Predicting
-2016-09-06 08:09:49,477 DEBUG: Done:	 Predicting
-2016-09-06 08:09:49,478 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:49,479 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:49,479 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.680952380952
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.680952380952
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:09:49,479 INFO: Done:	 Result Analysis
-2016-09-06 08:09:49,586 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:49,586 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:49,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:09:49,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:09:49,586 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:49,586 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:09:49,587 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:49,587 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:49,587 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:49,587 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:49,587 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:49,587 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:09:49,587 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:49,587 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:09:49,663 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:49,663 DEBUG: Start:	 Training
-2016-09-06 08:09:49,663 DEBUG: Info:	 Time for Training: 0.078113079071[s]
-2016-09-06 08:09:49,664 DEBUG: Done:	 Training
-2016-09-06 08:09:49,664 DEBUG: Start:	 Predicting
-2016-09-06 08:09:49,674 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:09:49,674 DEBUG: Start:	 Training
-2016-09-06 08:09:49,680 DEBUG: Done:	 Predicting
-2016-09-06 08:09:49,680 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:49,681 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:49,681 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:09:49,681 INFO: Done:	 Result Analysis
-2016-09-06 08:09:49,695 DEBUG: Info:	 Time for Training: 0.109401941299[s]
-2016-09-06 08:09:49,695 DEBUG: Done:	 Training
-2016-09-06 08:09:49,695 DEBUG: Start:	 Predicting
-2016-09-06 08:09:49,699 DEBUG: Done:	 Predicting
-2016-09-06 08:09:49,699 DEBUG: Start:	 Getting Results
-2016-09-06 08:09:49,700 DEBUG: Done:	 Getting Results
-2016-09-06 08:09:49,700 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:09:49,700 INFO: Done:	 Result Analysis
-2016-09-06 08:09:49,982 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:09:49,983 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:09:49,983 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:09:49,984 INFO: Info:	 Shape of View0 :(300, 11)
-2016-09-06 08:09:49,984 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:09:49,984 INFO: Info:	 Shape of View1 :(300, 6)
-2016-09-06 08:09:49,985 INFO: Info:	 Shape of View0 :(300, 11)
-2016-09-06 08:09:49,985 INFO: Info:	 Shape of View2 :(300, 19)
-2016-09-06 08:09:49,986 INFO: Info:	 Shape of View3 :(300, 12)
-2016-09-06 08:09:49,986 INFO: Done:	 Read Database Files
-2016-09-06 08:09:49,986 INFO: Info:	 Shape of View1 :(300, 6)
-2016-09-06 08:09:49,986 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:09:49,987 INFO: Info:	 Shape of View2 :(300, 19)
-2016-09-06 08:09:49,988 INFO: Info:	 Shape of View3 :(300, 12)
-2016-09-06 08:09:49,988 INFO: Done:	 Read Database Files
-2016-09-06 08:09:49,988 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:09:49,990 INFO: Done:	 Determine validation split
-2016-09-06 08:09:49,990 INFO: Start:	 Determine 5 folds
-2016-09-06 08:09:49,993 INFO: Done:	 Determine validation split
-2016-09-06 08:09:49,993 INFO: Start:	 Determine 5 folds
-2016-09-06 08:09:49,997 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:09:49,997 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:09:49,998 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:09:49,998 INFO: Done:	 Determine folds
-2016-09-06 08:09:49,998 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 08:09:49,998 INFO: Start:	 Classification
-2016-09-06 08:09:49,998 INFO: 	Start:	 Fold number 1
-2016-09-06 08:09:49,999 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:09:49,999 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:09:50,000 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:09:50,000 INFO: Done:	 Determine folds
-2016-09-06 08:09:50,000 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:09:50,000 INFO: Start:	 Classification
-2016-09-06 08:09:50,000 INFO: 	Start:	 Fold number 1
-2016-09-06 08:09:50,031 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:09:50,038 DEBUG: 			View 0 : 0.488888888889
-2016-09-06 08:09:50,046 DEBUG: 			View 1 : 0.533333333333
-2016-09-06 08:09:50,052 INFO: 	Start: 	 Classification
-2016-09-06 08:09:50,053 DEBUG: 			View 2 : 0.544444444444
-2016-09-06 08:09:50,060 DEBUG: 			View 3 : 0.455555555556
-2016-09-06 08:09:50,093 DEBUG: 			 Best view : 		View2
-2016-09-06 08:09:50,176 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:09:50,183 DEBUG: 			View 0 : 0.688888888889
-2016-09-06 08:09:50,190 DEBUG: 			View 1 : 0.611111111111
-2016-09-06 08:09:50,198 DEBUG: 			View 2 : 0.727777777778
-2016-09-06 08:09:50,206 DEBUG: 			View 3 : 0.694444444444
-2016-09-06 08:09:50,246 DEBUG: 			 Best view : 		View2
-2016-09-06 08:09:50,398 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:09:50,405 DEBUG: 			View 0 : 0.688888888889
-2016-09-06 08:09:50,414 DEBUG: 			View 1 : 0.611111111111
-2016-09-06 08:09:50,422 DEBUG: 			View 2 : 0.727777777778
-2016-09-06 08:09:50,430 DEBUG: 			View 3 : 0.694444444444
-2016-09-06 08:09:50,470 DEBUG: 			 Best view : 		View2
-2016-09-06 08:09:50,684 INFO: 	Start: 	 Classification
-2016-09-06 08:09:51,034 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:09:51,035 INFO: 	Start:	 Fold number 2
-2016-09-06 08:09:51,063 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:09:51,070 DEBUG: 			View 0 : 0.530726256983
-2016-09-06 08:09:51,076 DEBUG: 			View 1 : 0.530726256983
-2016-09-06 08:09:51,083 DEBUG: 			View 2 : 0.530726256983
-2016-09-06 08:09:51,089 DEBUG: 			View 3 : 0.530726256983
-2016-09-06 08:09:51,121 DEBUG: 			 Best view : 		View0
-2016-09-06 08:09:51,199 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:09:51,206 DEBUG: 			View 0 : 0.620111731844
-2016-09-06 08:09:51,213 DEBUG: 			View 1 : 0.63687150838
-2016-09-06 08:09:51,220 DEBUG: 			View 2 : 0.759776536313
-2016-09-06 08:09:51,228 DEBUG: 			View 3 : 0.720670391061
-2016-09-06 08:09:51,265 DEBUG: 			 Best view : 		View2
-2016-09-06 08:09:51,412 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:09:51,419 DEBUG: 			View 0 : 0.620111731844
-2016-09-06 08:09:51,426 DEBUG: 			View 1 : 0.63687150838
-2016-09-06 08:09:51,433 DEBUG: 			View 2 : 0.759776536313
-2016-09-06 08:09:51,440 DEBUG: 			View 3 : 0.720670391061
-2016-09-06 08:09:51,481 DEBUG: 			 Best view : 		View2
-2016-09-06 08:09:51,695 INFO: 	Start: 	 Classification
-2016-09-06 08:09:52,044 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:09:52,044 INFO: 	Start:	 Fold number 3
-2016-09-06 08:09:52,074 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:09:52,081 DEBUG: 			View 0 : 0.508196721311
-2016-09-06 08:09:52,088 DEBUG: 			View 1 : 0.55737704918
-2016-09-06 08:09:52,094 DEBUG: 			View 2 : 0.497267759563
-2016-09-06 08:09:52,101 DEBUG: 			View 3 : 0.48087431694
-2016-09-06 08:09:52,134 DEBUG: 			 Best view : 		View2
-2016-09-06 08:09:52,214 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:09:52,221 DEBUG: 			View 0 : 0.699453551913
-2016-09-06 08:09:52,228 DEBUG: 			View 1 : 0.666666666667
-2016-09-06 08:09:52,236 DEBUG: 			View 2 : 0.743169398907
-2016-09-06 08:09:52,243 DEBUG: 			View 3 : 0.699453551913
-2016-09-06 08:09:52,281 DEBUG: 			 Best view : 		View2
-2016-09-06 08:09:52,436 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:09:52,444 DEBUG: 			View 0 : 0.699453551913
-2016-09-06 08:09:52,451 DEBUG: 			View 1 : 0.666666666667
-2016-09-06 08:09:52,459 DEBUG: 			View 2 : 0.743169398907
-2016-09-06 08:09:52,466 DEBUG: 			View 3 : 0.699453551913
-2016-09-06 08:09:52,507 DEBUG: 			 Best view : 		View2
-2016-09-06 08:09:52,725 INFO: 	Start: 	 Classification
-2016-09-06 08:09:53,079 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:09:53,079 INFO: 	Start:	 Fold number 4
-2016-09-06 08:09:53,108 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:09:53,115 DEBUG: 			View 0 : 0.491712707182
-2016-09-06 08:09:53,122 DEBUG: 			View 1 : 0.519337016575
-2016-09-06 08:09:53,129 DEBUG: 			View 2 : 0.546961325967
-2016-09-06 08:09:53,135 DEBUG: 			View 3 : 0.508287292818
-2016-09-06 08:09:53,167 DEBUG: 			 Best view : 		View3
-2016-09-06 08:09:53,246 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:09:53,253 DEBUG: 			View 0 : 0.60773480663
-2016-09-06 08:09:53,260 DEBUG: 			View 1 : 0.60773480663
-2016-09-06 08:09:53,268 DEBUG: 			View 2 : 0.657458563536
-2016-09-06 08:09:53,275 DEBUG: 			View 3 : 0.696132596685
-2016-09-06 08:09:53,313 DEBUG: 			 Best view : 		View3
-2016-09-06 08:09:53,461 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:09:53,468 DEBUG: 			View 0 : 0.60773480663
-2016-09-06 08:09:53,475 DEBUG: 			View 1 : 0.60773480663
-2016-09-06 08:09:53,482 DEBUG: 			View 2 : 0.657458563536
-2016-09-06 08:09:53,489 DEBUG: 			View 3 : 0.696132596685
-2016-09-06 08:09:53,530 DEBUG: 			 Best view : 		View3
-2016-09-06 08:09:53,745 INFO: 	Start: 	 Classification
-2016-09-06 08:09:54,094 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:09:54,094 INFO: 	Start:	 Fold number 5
-2016-09-06 08:09:54,125 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:09:54,132 DEBUG: 			View 0 : 0.489361702128
-2016-09-06 08:09:54,139 DEBUG: 			View 1 : 0.478723404255
-2016-09-06 08:09:54,146 DEBUG: 			View 2 : 0.457446808511
-2016-09-06 08:09:54,153 DEBUG: 			View 3 : 0.521276595745
-2016-09-06 08:09:54,186 DEBUG: 			 Best view : 		View1
-2016-09-06 08:09:54,268 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:09:54,275 DEBUG: 			View 0 : 0.675531914894
-2016-09-06 08:09:54,282 DEBUG: 			View 1 : 0.664893617021
-2016-09-06 08:09:54,289 DEBUG: 			View 2 : 0.787234042553
-2016-09-06 08:09:54,297 DEBUG: 			View 3 : 0.734042553191
-2016-09-06 08:09:54,337 DEBUG: 			 Best view : 		View2
-2016-09-06 08:09:54,490 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:09:54,497 DEBUG: 			View 0 : 0.675531914894
-2016-09-06 08:09:54,504 DEBUG: 			View 1 : 0.664893617021
-2016-09-06 08:09:54,512 DEBUG: 			View 2 : 0.787234042553
-2016-09-06 08:09:54,519 DEBUG: 			View 3 : 0.734042553191
-2016-09-06 08:09:54,561 DEBUG: 			 Best view : 		View2
-2016-09-06 08:09:54,785 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:09:54,792 DEBUG: 			View 0 : 0.606382978723
-2016-09-06 08:09:54,799 DEBUG: 			View 1 : 0.579787234043
-2016-09-06 08:09:54,807 DEBUG: 			View 2 : 0.718085106383
-2016-09-06 08:09:54,814 DEBUG: 			View 3 : 0.696808510638
-2016-09-06 08:09:54,859 DEBUG: 			 Best view : 		View2
-2016-09-06 08:09:55,154 INFO: 	Start: 	 Classification
-2016-09-06 08:09:55,630 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:09:55,630 INFO: Done:	 Classification
-2016-09-06 08:09:55,630 INFO: Info:	 Time for Classification: 5[s]
-2016-09-06 08:09:55,630 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 08:09:57,632 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 74.2818070447
-	-On Test : 53.1707317073
-	-On Validation : 68.0898876404Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 11), View1 of shape (300, 6), View2 of shape (300, 19), View3 of shape (300, 12)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:01        0:00:00
-	         Fold 3        0:00:02        0:00:00
-	         Fold 4        0:00:03        0:00:00
-	         Fold 5        0:00:05        0:00:00
-	          Total        0:00:14        0:00:01
-	So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.186666666667
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.175555555556
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.2
-			- Percentage of time chosen : 0.3
-		- On View3 : 
-			- Mean average Accuracy : 0.184444444444
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.177094972067
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.180446927374
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.205027932961
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.197206703911
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.190710382514
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.189071038251
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.198360655738
-			- Percentage of time chosen : 0.3
-		- On View3 : 
-			- Mean average Accuracy : 0.187978142077
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.170718232044
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.173480662983
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.186187845304
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.190055248619
-			- Percentage of time chosen : 0.3
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.244680851064
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.238829787234
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.275
-			- Percentage of time chosen : 0.3
-		- On View3 : 
-			- Mean average Accuracy : 0.268617021277
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 51.6666666667
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 53.0726256983
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 53.0054644809
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 51.9337016575
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 50.5319148936
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View1
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 72.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 75.9776536313
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 74.3169398907
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 69.6132596685
-			Accuracy on test : 0.0
-			Accuracy on validation : 59.5505617978
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 78.7234042553
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View2
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 72.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 75.9776536313
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 74.3169398907
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 69.6132596685
-			Accuracy on test : 0.0
-			Accuracy on validation : 59.5505617978
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 78.7234042553
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View2
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 51.6666666667
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 53.0726256983
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 53.0054644809
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 51.9337016575
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 78.7234042553
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View2
-	- Iteration 5
-		 Fold 5
-			Accuracy on train : 50.5319148936
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
-2016-09-06 08:09:57,815 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 260bd337..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e3acff55..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 966b9ace..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.780952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.780952380952
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 703c8c37..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 17, max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6219637d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db9d8258..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.666666666667
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.666666666667
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3835c91f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.733333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.733333333333
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8e18dec5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 58aab88f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3d281449..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index be69d201..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3793474e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6042afd3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6aed140a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.738095238095
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ffc5f592..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.714285714286
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.714285714286
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 08543b9c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dfa42758..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 12cfab97..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.695238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9103
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.695238095238
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 696c1906..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bf8c8be2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.388888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.388888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9f00b6b2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.685714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.685714285714
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dcea9eb8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.728571428571
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.728571428571
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 615c2a9b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4e9420a4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 244785d4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 183892fd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 42aa09b7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.728571428571
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.728571428571
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a23a75a3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.680952380952
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.680952380952
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3227b236..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : perceptron, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4e10be90..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8811
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080957Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-080957Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index 85173b5e72ff3aec9eb20144b85ad444e07cdcf4..0000000000000000000000000000000000000000
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080957Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080957Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index fdb9968d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080957Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,215 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 74.2818070447
-	-On Test : 53.1707317073
-	-On Validation : 68.0898876404Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 11), View1 of shape (300, 6), View2 of shape (300, 19), View3 of shape (300, 12)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:01        0:00:00
-	         Fold 3        0:00:02        0:00:00
-	         Fold 4        0:00:03        0:00:00
-	         Fold 5        0:00:05        0:00:00
-	          Total        0:00:14        0:00:01
-	So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.186666666667
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.175555555556
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.2
-			- Percentage of time chosen : 0.3
-		- On View3 : 
-			- Mean average Accuracy : 0.184444444444
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.177094972067
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.180446927374
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.205027932961
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.197206703911
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.190710382514
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.189071038251
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.198360655738
-			- Percentage of time chosen : 0.3
-		- On View3 : 
-			- Mean average Accuracy : 0.187978142077
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.170718232044
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.173480662983
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.186187845304
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.190055248619
-			- Percentage of time chosen : 0.3
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.244680851064
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.238829787234
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.275
-			- Percentage of time chosen : 0.3
-		- On View3 : 
-			- Mean average Accuracy : 0.268617021277
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 51.6666666667
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 53.0726256983
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 53.0054644809
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 51.9337016575
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 50.5319148936
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View1
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 72.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 75.9776536313
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 74.3169398907
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 69.6132596685
-			Accuracy on test : 0.0
-			Accuracy on validation : 59.5505617978
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 78.7234042553
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View2
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 72.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 75.9776536313
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 74.3169398907
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 69.6132596685
-			Accuracy on test : 0.0
-			Accuracy on validation : 59.5505617978
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 78.7234042553
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View2
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 51.6666666667
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 53.0726256983
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 53.0054644809
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 51.9337016575
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 78.7234042553
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.9101123596
-			Selected View : View2
-	- Iteration 5
-		 Fold 5
-			Accuracy on train : 50.5319148936
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-081124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index ba133815..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1932 +0,0 @@
-2016-09-06 08:11:24,040 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:11:24,041 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000133375 Gbytes /!\ 
-2016-09-06 08:11:29,054 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 08:11:29,057 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 08:11:29,106 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,107 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:11:29,107 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:29,107 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,108 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,108 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:29,108 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,108 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,108 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:11:29,108 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:29,109 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,109 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,109 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:29,109 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,163 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:29,164 DEBUG: Start:	 Training
-2016-09-06 08:11:29,164 DEBUG: Info:	 Time for Training: 0.0569159984589[s]
-2016-09-06 08:11:29,164 DEBUG: Done:	 Training
-2016-09-06 08:11:29,164 DEBUG: Start:	 Predicting
-2016-09-06 08:11:29,167 DEBUG: Done:	 Predicting
-2016-09-06 08:11:29,167 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:29,168 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:29,168 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,168 INFO: Done:	 Result Analysis
-2016-09-06 08:11:29,187 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:29,187 DEBUG: Start:	 Training
-2016-09-06 08:11:29,190 DEBUG: Info:	 Time for Training: 0.0837590694427[s]
-2016-09-06 08:11:29,190 DEBUG: Done:	 Training
-2016-09-06 08:11:29,190 DEBUG: Start:	 Predicting
-2016-09-06 08:11:29,193 DEBUG: Done:	 Predicting
-2016-09-06 08:11:29,193 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:29,195 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:29,195 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,195 INFO: Done:	 Result Analysis
-2016-09-06 08:11:29,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,255 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:11:29,255 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:11:29,255 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:29,255 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:29,256 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,256 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,256 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,256 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,256 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:29,256 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:29,256 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,256 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,308 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:29,308 DEBUG: Start:	 Training
-2016-09-06 08:11:29,308 DEBUG: Info:	 Time for Training: 0.0547308921814[s]
-2016-09-06 08:11:29,308 DEBUG: Done:	 Training
-2016-09-06 08:11:29,309 DEBUG: Start:	 Predicting
-2016-09-06 08:11:29,313 DEBUG: Done:	 Predicting
-2016-09-06 08:11:29,313 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:29,314 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:29,314 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.671428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.671428571429
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,314 INFO: Done:	 Result Analysis
-2016-09-06 08:11:29,399 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:29,399 DEBUG: Start:	 Training
-2016-09-06 08:11:29,408 DEBUG: Info:	 Time for Training: 0.154690027237[s]
-2016-09-06 08:11:29,408 DEBUG: Done:	 Training
-2016-09-06 08:11:29,409 DEBUG: Start:	 Predicting
-2016-09-06 08:11:29,412 DEBUG: Done:	 Predicting
-2016-09-06 08:11:29,412 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:29,413 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:29,413 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.638095238095
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.638095238095
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,413 INFO: Done:	 Result Analysis
-2016-09-06 08:11:29,506 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,506 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,506 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:11:29,506 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:11:29,506 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:29,506 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:29,507 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,507 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,507 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,507 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,507 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:29,507 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:29,508 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,508 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,587 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:29,587 DEBUG: Start:	 Training
-2016-09-06 08:11:29,588 DEBUG: Info:	 Time for Training: 0.0827720165253[s]
-2016-09-06 08:11:29,588 DEBUG: Done:	 Training
-2016-09-06 08:11:29,588 DEBUG: Start:	 Predicting
-2016-09-06 08:11:29,592 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:29,592 DEBUG: Start:	 Training
-2016-09-06 08:11:29,609 DEBUG: Info:	 Time for Training: 0.10356593132[s]
-2016-09-06 08:11:29,609 DEBUG: Done:	 Training
-2016-09-06 08:11:29,609 DEBUG: Start:	 Predicting
-2016-09-06 08:11:29,612 DEBUG: Done:	 Predicting
-2016-09-06 08:11:29,612 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:29,613 DEBUG: Done:	 Predicting
-2016-09-06 08:11:29,613 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:29,613 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:29,613 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,614 INFO: Done:	 Result Analysis
-2016-09-06 08:11:29,615 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:29,615 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,616 INFO: Done:	 Result Analysis
-2016-09-06 08:11:29,757 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,757 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,757 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:11:29,757 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:11:29,757 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:29,757 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:29,758 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,758 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,758 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,758 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,758 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:29,758 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:29,758 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,759 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,842 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:29,842 DEBUG: Start:	 Training
-2016-09-06 08:11:29,849 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:29,850 DEBUG: Start:	 Training
-2016-09-06 08:11:29,860 DEBUG: Info:	 Time for Training: 0.103305101395[s]
-2016-09-06 08:11:29,860 DEBUG: Done:	 Training
-2016-09-06 08:11:29,860 DEBUG: Start:	 Predicting
-2016-09-06 08:11:29,865 DEBUG: Done:	 Predicting
-2016-09-06 08:11:29,866 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:29,867 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:29,867 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,867 INFO: Done:	 Result Analysis
-2016-09-06 08:11:29,868 DEBUG: Info:	 Time for Training: 0.112034082413[s]
-2016-09-06 08:11:29,869 DEBUG: Done:	 Training
-2016-09-06 08:11:29,869 DEBUG: Start:	 Predicting
-2016-09-06 08:11:29,872 DEBUG: Done:	 Predicting
-2016-09-06 08:11:29,872 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:29,873 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:29,873 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5059
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,873 INFO: Done:	 Result Analysis
-2016-09-06 08:11:30,010 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,010 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:11:30,010 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:30,010 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,011 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:11:30,011 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,011 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:30,011 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,011 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:30,011 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,012 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,012 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,012 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:30,012 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,061 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:30,061 DEBUG: Start:	 Training
-2016-09-06 08:11:30,062 DEBUG: Info:	 Time for Training: 0.0535268783569[s]
-2016-09-06 08:11:30,063 DEBUG: Done:	 Training
-2016-09-06 08:11:30,063 DEBUG: Start:	 Predicting
-2016-09-06 08:11:30,065 DEBUG: Done:	 Predicting
-2016-09-06 08:11:30,065 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:30,066 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:30,066 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 29
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,067 INFO: Done:	 Result Analysis
-2016-09-06 08:11:30,098 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:30,098 DEBUG: Start:	 Training
-2016-09-06 08:11:30,102 DEBUG: Info:	 Time for Training: 0.092768907547[s]
-2016-09-06 08:11:30,102 DEBUG: Done:	 Training
-2016-09-06 08:11:30,102 DEBUG: Start:	 Predicting
-2016-09-06 08:11:30,105 DEBUG: Done:	 Predicting
-2016-09-06 08:11:30,105 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:30,107 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:30,107 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,107 INFO: Done:	 Result Analysis
-2016-09-06 08:11:30,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,255 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:11:30,255 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:11:30,255 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:30,255 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:30,256 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,256 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,256 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,256 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,256 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:30,256 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:30,257 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,257 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,306 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:30,306 DEBUG: Start:	 Training
-2016-09-06 08:11:30,306 DEBUG: Info:	 Time for Training: 0.0526769161224[s]
-2016-09-06 08:11:30,307 DEBUG: Done:	 Training
-2016-09-06 08:11:30,307 DEBUG: Start:	 Predicting
-2016-09-06 08:11:30,311 DEBUG: Done:	 Predicting
-2016-09-06 08:11:30,311 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:30,312 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:30,312 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.680952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 6
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.680952380952
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,312 INFO: Done:	 Result Analysis
-2016-09-06 08:11:30,395 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:30,396 DEBUG: Start:	 Training
-2016-09-06 08:11:30,403 DEBUG: Info:	 Time for Training: 0.149629116058[s]
-2016-09-06 08:11:30,404 DEBUG: Done:	 Training
-2016-09-06 08:11:30,404 DEBUG: Start:	 Predicting
-2016-09-06 08:11:30,407 DEBUG: Done:	 Predicting
-2016-09-06 08:11:30,407 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:30,408 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:30,408 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,408 INFO: Done:	 Result Analysis
-2016-09-06 08:11:30,510 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,510 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,511 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:11:30,511 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:11:30,511 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:30,511 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:30,512 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,512 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,512 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,512 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,513 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:30,513 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:30,513 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,513 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,592 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:30,592 DEBUG: Start:	 Training
-2016-09-06 08:11:30,593 DEBUG: Info:	 Time for Training: 0.0842258930206[s]
-2016-09-06 08:11:30,593 DEBUG: Done:	 Training
-2016-09-06 08:11:30,593 DEBUG: Start:	 Predicting
-2016-09-06 08:11:30,606 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:30,607 DEBUG: Start:	 Training
-2016-09-06 08:11:30,609 DEBUG: Done:	 Predicting
-2016-09-06 08:11:30,609 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:30,610 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:30,611 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,611 INFO: Done:	 Result Analysis
-2016-09-06 08:11:30,624 DEBUG: Info:	 Time for Training: 0.114921092987[s]
-2016-09-06 08:11:30,624 DEBUG: Done:	 Training
-2016-09-06 08:11:30,624 DEBUG: Start:	 Predicting
-2016-09-06 08:11:30,628 DEBUG: Done:	 Predicting
-2016-09-06 08:11:30,628 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:30,629 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:30,629 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.452380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.452380952381
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,629 INFO: Done:	 Result Analysis
-2016-09-06 08:11:30,755 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,755 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,756 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:11:30,756 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:11:30,756 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:30,756 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:30,757 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,757 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,757 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,757 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,757 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:30,757 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:30,758 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,758 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,884 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:30,884 DEBUG: Start:	 Training
-2016-09-06 08:11:30,893 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:30,893 DEBUG: Start:	 Training
-2016-09-06 08:11:30,912 DEBUG: Info:	 Time for Training: 0.157926082611[s]
-2016-09-06 08:11:30,913 DEBUG: Done:	 Training
-2016-09-06 08:11:30,913 DEBUG: Start:	 Predicting
-2016-09-06 08:11:30,919 DEBUG: Done:	 Predicting
-2016-09-06 08:11:30,919 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:30,921 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:30,921 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,921 INFO: Done:	 Result Analysis
-2016-09-06 08:11:30,922 DEBUG: Info:	 Time for Training: 0.167793035507[s]
-2016-09-06 08:11:30,922 DEBUG: Done:	 Training
-2016-09-06 08:11:30,923 DEBUG: Start:	 Predicting
-2016-09-06 08:11:30,926 DEBUG: Done:	 Predicting
-2016-09-06 08:11:30,926 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:30,927 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:30,927 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,927 INFO: Done:	 Result Analysis
-2016-09-06 08:11:31,004 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,004 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,005 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:11:31,005 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:11:31,005 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:31,005 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:31,006 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,006 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,006 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,006 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,006 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:31,006 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:31,006 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,006 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,063 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:31,063 DEBUG: Start:	 Training
-2016-09-06 08:11:31,064 DEBUG: Info:	 Time for Training: 0.0602171421051[s]
-2016-09-06 08:11:31,064 DEBUG: Done:	 Training
-2016-09-06 08:11:31,064 DEBUG: Start:	 Predicting
-2016-09-06 08:11:31,067 DEBUG: Done:	 Predicting
-2016-09-06 08:11:31,067 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:31,068 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:31,068 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,068 INFO: Done:	 Result Analysis
-2016-09-06 08:11:31,097 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:31,097 DEBUG: Start:	 Training
-2016-09-06 08:11:31,101 DEBUG: Info:	 Time for Training: 0.0974199771881[s]
-2016-09-06 08:11:31,101 DEBUG: Done:	 Training
-2016-09-06 08:11:31,102 DEBUG: Start:	 Predicting
-2016-09-06 08:11:31,104 DEBUG: Done:	 Predicting
-2016-09-06 08:11:31,105 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:31,106 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:31,106 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,107 INFO: Done:	 Result Analysis
-2016-09-06 08:11:31,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,254 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:11:31,254 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:31,254 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:11:31,254 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:31,255 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,255 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,255 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,255 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,255 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:31,255 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:31,255 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,255 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,306 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:31,306 DEBUG: Start:	 Training
-2016-09-06 08:11:31,307 DEBUG: Info:	 Time for Training: 0.0538859367371[s]
-2016-09-06 08:11:31,307 DEBUG: Done:	 Training
-2016-09-06 08:11:31,307 DEBUG: Start:	 Predicting
-2016-09-06 08:11:31,312 DEBUG: Done:	 Predicting
-2016-09-06 08:11:31,313 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:31,314 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:31,314 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.77619047619
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.77619047619
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,314 INFO: Done:	 Result Analysis
-2016-09-06 08:11:31,388 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:31,388 DEBUG: Start:	 Training
-2016-09-06 08:11:31,396 DEBUG: Info:	 Time for Training: 0.14239692688[s]
-2016-09-06 08:11:31,396 DEBUG: Done:	 Training
-2016-09-06 08:11:31,396 DEBUG: Start:	 Predicting
-2016-09-06 08:11:31,399 DEBUG: Done:	 Predicting
-2016-09-06 08:11:31,399 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:31,400 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:31,400 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.633333333333
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.633333333333
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,400 INFO: Done:	 Result Analysis
-2016-09-06 08:11:31,503 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,503 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:11:31,504 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:31,504 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,504 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,505 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:31,504 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,505 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,505 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:11:31,505 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:31,506 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,506 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,506 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:31,506 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,586 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:31,587 DEBUG: Start:	 Training
-2016-09-06 08:11:31,588 DEBUG: Info:	 Time for Training: 0.0840480327606[s]
-2016-09-06 08:11:31,588 DEBUG: Done:	 Training
-2016-09-06 08:11:31,588 DEBUG: Start:	 Predicting
-2016-09-06 08:11:31,592 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:31,592 DEBUG: Start:	 Training
-2016-09-06 08:11:31,609 DEBUG: Done:	 Predicting
-2016-09-06 08:11:31,609 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:31,610 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:31,610 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,610 INFO: Done:	 Result Analysis
-2016-09-06 08:11:31,613 DEBUG: Info:	 Time for Training: 0.110257148743[s]
-2016-09-06 08:11:31,613 DEBUG: Done:	 Training
-2016-09-06 08:11:31,613 DEBUG: Start:	 Predicting
-2016-09-06 08:11:31,616 DEBUG: Done:	 Predicting
-2016-09-06 08:11:31,616 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:31,617 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:31,618 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,618 INFO: Done:	 Result Analysis
-2016-09-06 08:11:31,750 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,751 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:11:31,751 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,751 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:31,751 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:11:31,751 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:31,752 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,752 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,752 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,752 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,752 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:31,752 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:31,753 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,753 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,845 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:31,845 DEBUG: Start:	 Training
-2016-09-06 08:11:31,848 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:31,848 DEBUG: Start:	 Training
-2016-09-06 08:11:31,862 DEBUG: Info:	 Time for Training: 0.112237930298[s]
-2016-09-06 08:11:31,862 DEBUG: Done:	 Training
-2016-09-06 08:11:31,862 DEBUG: Start:	 Predicting
-2016-09-06 08:11:31,866 DEBUG: Info:	 Time for Training: 0.116894960403[s]
-2016-09-06 08:11:31,867 DEBUG: Done:	 Training
-2016-09-06 08:11:31,867 DEBUG: Start:	 Predicting
-2016-09-06 08:11:31,868 DEBUG: Done:	 Predicting
-2016-09-06 08:11:31,869 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:31,870 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:31,870 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,870 INFO: Done:	 Result Analysis
-2016-09-06 08:11:31,870 DEBUG: Done:	 Predicting
-2016-09-06 08:11:31,871 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:31,872 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:31,872 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,872 INFO: Done:	 Result Analysis
-2016-09-06 08:11:32,003 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,003 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,004 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:11:32,004 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:11:32,004 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:32,004 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:32,004 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,004 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,005 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,005 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,005 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:32,005 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:32,005 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,005 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,062 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:32,062 DEBUG: Start:	 Training
-2016-09-06 08:11:32,063 DEBUG: Info:	 Time for Training: 0.059504032135[s]
-2016-09-06 08:11:32,063 DEBUG: Done:	 Training
-2016-09-06 08:11:32,063 DEBUG: Start:	 Predicting
-2016-09-06 08:11:32,065 DEBUG: Done:	 Predicting
-2016-09-06 08:11:32,065 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:32,066 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:32,066 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.628571428571
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,067 INFO: Done:	 Result Analysis
-2016-09-06 08:11:32,095 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:32,096 DEBUG: Start:	 Training
-2016-09-06 08:11:32,100 DEBUG: Info:	 Time for Training: 0.0969760417938[s]
-2016-09-06 08:11:32,100 DEBUG: Done:	 Training
-2016-09-06 08:11:32,100 DEBUG: Start:	 Predicting
-2016-09-06 08:11:32,103 DEBUG: Done:	 Predicting
-2016-09-06 08:11:32,103 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:32,105 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:32,105 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,105 INFO: Done:	 Result Analysis
-2016-09-06 08:11:32,255 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,256 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,256 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:11:32,256 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:11:32,256 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:32,256 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:32,257 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,257 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,257 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,257 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,257 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:32,257 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:32,257 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,257 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,308 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:32,308 DEBUG: Start:	 Training
-2016-09-06 08:11:32,309 DEBUG: Info:	 Time for Training: 0.0539190769196[s]
-2016-09-06 08:11:32,309 DEBUG: Done:	 Training
-2016-09-06 08:11:32,309 DEBUG: Start:	 Predicting
-2016-09-06 08:11:32,315 DEBUG: Done:	 Predicting
-2016-09-06 08:11:32,315 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:32,316 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:32,316 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 6
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.738095238095
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,317 INFO: Done:	 Result Analysis
-2016-09-06 08:11:32,394 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:32,395 DEBUG: Start:	 Training
-2016-09-06 08:11:32,402 DEBUG: Info:	 Time for Training: 0.147303104401[s]
-2016-09-06 08:11:32,403 DEBUG: Done:	 Training
-2016-09-06 08:11:32,403 DEBUG: Start:	 Predicting
-2016-09-06 08:11:32,406 DEBUG: Done:	 Predicting
-2016-09-06 08:11:32,406 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:32,407 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:32,407 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.714285714286
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.714285714286
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,407 INFO: Done:	 Result Analysis
-2016-09-06 08:11:32,500 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,500 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:11:32,500 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:32,501 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,502 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,502 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:11:32,502 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,502 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:11:32,502 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:32,502 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,503 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,503 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,503 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:11:32,503 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,585 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:32,585 DEBUG: Start:	 Training
-2016-09-06 08:11:32,586 DEBUG: Info:	 Time for Training: 0.0862069129944[s]
-2016-09-06 08:11:32,586 DEBUG: Done:	 Training
-2016-09-06 08:11:32,587 DEBUG: Start:	 Predicting
-2016-09-06 08:11:32,591 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:11:32,592 DEBUG: Start:	 Training
-2016-09-06 08:11:32,601 DEBUG: Done:	 Predicting
-2016-09-06 08:11:32,601 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:32,602 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:32,602 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.652380952381
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,603 INFO: Done:	 Result Analysis
-2016-09-06 08:11:32,613 DEBUG: Info:	 Time for Training: 0.113891839981[s]
-2016-09-06 08:11:32,614 DEBUG: Done:	 Training
-2016-09-06 08:11:32,614 DEBUG: Start:	 Predicting
-2016-09-06 08:11:32,617 DEBUG: Done:	 Predicting
-2016-09-06 08:11:32,617 DEBUG: Start:	 Getting Results
-2016-09-06 08:11:32,618 DEBUG: Done:	 Getting Results
-2016-09-06 08:11:32,619 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.471428571429
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,619 INFO: Done:	 Result Analysis
-2016-09-06 08:11:32,891 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:32,892 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:11:32,892 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:32,892 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:32,892 INFO: Info:	 Shape of View0 :(300, 7)
-2016-09-06 08:11:32,893 INFO: Info:	 Shape of View0 :(300, 7)
-2016-09-06 08:11:32,893 INFO: Info:	 Shape of View1 :(300, 5)
-2016-09-06 08:11:32,894 INFO: Info:	 Shape of View1 :(300, 5)
-2016-09-06 08:11:32,894 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:11:32,895 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:11:32,895 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:11:32,895 INFO: Done:	 Read Database Files
-2016-09-06 08:11:32,895 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:11:32,895 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:11:32,896 INFO: Done:	 Read Database Files
-2016-09-06 08:11:32,896 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:11:32,901 INFO: Done:	 Determine validation split
-2016-09-06 08:11:32,901 INFO: Start:	 Determine 5 folds
-2016-09-06 08:11:32,903 INFO: Done:	 Determine validation split
-2016-09-06 08:11:32,903 INFO: Start:	 Determine 5 folds
-2016-09-06 08:11:32,910 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:11:32,911 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:11:32,911 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:11:32,911 INFO: Done:	 Determine folds
-2016-09-06 08:11:32,911 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:11:32,911 INFO: Start:	 Classification
-2016-09-06 08:11:32,911 INFO: 	Start:	 Fold number 1
-2016-09-06 08:11:32,913 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:11:32,914 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:11:32,914 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:11:32,914 INFO: Done:	 Determine folds
-2016-09-06 08:11:32,914 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 08:11:32,914 INFO: Start:	 Classification
-2016-09-06 08:11:32,914 INFO: 	Start:	 Fold number 1
-2016-09-06 08:11:32,953 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:11:32,960 DEBUG: 			View 0 : 0.535519125683
-2016-09-06 08:11:32,969 DEBUG: 			View 1 : 0.551912568306
-2016-09-06 08:11:32,971 INFO: 	Start: 	 Classification
-2016-09-06 08:11:32,976 DEBUG: 			View 2 : 0.535519125683
-2016-09-06 08:11:32,984 DEBUG: 			View 3 : 0.513661202186
-2016-09-06 08:11:33,023 DEBUG: 			 Best view : 		View0
-2016-09-06 08:11:33,027 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:11:33,027 INFO: 	Start:	 Fold number 2
-2016-09-06 08:11:33,081 INFO: 	Start: 	 Classification
-2016-09-06 08:11:33,110 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:11:33,110 INFO: 	Start:	 Fold number 3
-2016-09-06 08:11:33,116 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:11:33,124 DEBUG: 			View 0 : 0.661202185792
-2016-09-06 08:11:33,132 DEBUG: 			View 1 : 0.590163934426
-2016-09-06 08:11:33,141 DEBUG: 			View 2 : 0.754098360656
-2016-09-06 08:11:33,151 DEBUG: 			View 3 : 0.743169398907
-2016-09-06 08:11:33,163 INFO: 	Start: 	 Classification
-2016-09-06 08:11:33,191 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:11:33,191 INFO: 	Start:	 Fold number 4
-2016-09-06 08:11:33,199 DEBUG: 			 Best view : 		View2
-2016-09-06 08:11:33,245 INFO: 	Start: 	 Classification
-2016-09-06 08:11:33,273 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:11:33,273 INFO: 	Start:	 Fold number 5
-2016-09-06 08:11:33,328 INFO: 	Start: 	 Classification
-2016-09-06 08:11:33,357 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:11:33,357 INFO: Done:	 Classification
-2016-09-06 08:11:33,357 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:11:33,357 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:11:33,361 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 70.9440472969
-	-On Test : 56.6666666667
-	-On Validation : 66.2222222222
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SVM Linear with C : 5501
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:11:33,362 INFO: Done:	 Result Analysis
-2016-09-06 08:11:33,379 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:11:33,393 DEBUG: 			View 0 : 0.661202185792
-2016-09-06 08:11:33,400 DEBUG: 			View 1 : 0.590163934426
-2016-09-06 08:11:33,407 DEBUG: 			View 2 : 0.754098360656
-2016-09-06 08:11:33,415 DEBUG: 			View 3 : 0.743169398907
-2016-09-06 08:11:33,456 DEBUG: 			 Best view : 		View2
-2016-09-06 08:11:33,673 INFO: 	Start: 	 Classification
-2016-09-06 08:11:34,028 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:11:34,028 INFO: 	Start:	 Fold number 2
-2016-09-06 08:11:34,057 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:11:34,064 DEBUG: 			View 0 : 0.579545454545
-2016-09-06 08:11:34,071 DEBUG: 			View 1 : 0.471590909091
-2016-09-06 08:11:34,078 DEBUG: 			View 2 : 0.4375
-2016-09-06 08:11:34,085 DEBUG: 			View 3 : 0.471590909091
-2016-09-06 08:11:34,115 DEBUG: 			 Best view : 		View2
-2016-09-06 08:11:34,193 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:11:34,200 DEBUG: 			View 0 : 0.664772727273
-2016-09-06 08:11:34,207 DEBUG: 			View 1 : 0.670454545455
-2016-09-06 08:11:34,213 DEBUG: 			View 2 : 0.642045454545
-2016-09-06 08:11:34,221 DEBUG: 			View 3 : 0.784090909091
-2016-09-06 08:11:34,258 DEBUG: 			 Best view : 		View3
-2016-09-06 08:11:34,401 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:11:34,408 DEBUG: 			View 0 : 0.664772727273
-2016-09-06 08:11:34,415 DEBUG: 			View 1 : 0.670454545455
-2016-09-06 08:11:34,422 DEBUG: 			View 2 : 0.642045454545
-2016-09-06 08:11:34,430 DEBUG: 			View 3 : 0.784090909091
-2016-09-06 08:11:34,469 DEBUG: 			 Best view : 		View3
-2016-09-06 08:11:34,679 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:11:34,685 DEBUG: 			View 0 : 0.653409090909
-2016-09-06 08:11:34,692 DEBUG: 			View 1 : 0.613636363636
-2016-09-06 08:11:34,699 DEBUG: 			View 2 : 0.676136363636
-2016-09-06 08:11:34,707 DEBUG: 			View 3 : 0.744318181818
-2016-09-06 08:11:34,748 DEBUG: 			 Best view : 		View3
-2016-09-06 08:11:35,024 INFO: 	Start: 	 Classification
-2016-09-06 08:11:35,486 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:11:35,486 INFO: 	Start:	 Fold number 3
-2016-09-06 08:11:35,515 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:11:35,522 DEBUG: 			View 0 : 0.522727272727
-2016-09-06 08:11:35,529 DEBUG: 			View 1 : 0.460227272727
-2016-09-06 08:11:35,536 DEBUG: 			View 2 : 0.5
-2016-09-06 08:11:35,543 DEBUG: 			View 3 : 0.477272727273
-2016-09-06 08:11:35,573 DEBUG: 			 Best view : 		View0
-2016-09-06 08:11:35,651 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:11:35,657 DEBUG: 			View 0 : 0.698863636364
-2016-09-06 08:11:35,664 DEBUG: 			View 1 : 0.619318181818
-2016-09-06 08:11:35,671 DEBUG: 			View 2 : 0.693181818182
-2016-09-06 08:11:35,678 DEBUG: 			View 3 : 0.721590909091
-2016-09-06 08:11:35,715 DEBUG: 			 Best view : 		View3
-2016-09-06 08:11:35,859 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:11:35,865 DEBUG: 			View 0 : 0.698863636364
-2016-09-06 08:11:35,872 DEBUG: 			View 1 : 0.619318181818
-2016-09-06 08:11:35,879 DEBUG: 			View 2 : 0.676136363636
-2016-09-06 08:11:35,887 DEBUG: 			View 3 : 0.721590909091
-2016-09-06 08:11:35,926 DEBUG: 			 Best view : 		View2
-2016-09-06 08:11:36,134 INFO: 	Start: 	 Classification
-2016-09-06 08:11:36,479 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:11:36,479 INFO: 	Start:	 Fold number 4
-2016-09-06 08:11:36,508 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:11:36,515 DEBUG: 			View 0 : 0.525139664804
-2016-09-06 08:11:36,522 DEBUG: 			View 1 : 0.541899441341
-2016-09-06 08:11:36,528 DEBUG: 			View 2 : 0.536312849162
-2016-09-06 08:11:36,536 DEBUG: 			View 3 : 0.474860335196
-2016-09-06 08:11:36,567 DEBUG: 			 Best view : 		View3
-2016-09-06 08:11:36,645 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:11:36,652 DEBUG: 			View 0 : 0.703910614525
-2016-09-06 08:11:36,659 DEBUG: 			View 1 : 0.586592178771
-2016-09-06 08:11:36,666 DEBUG: 			View 2 : 0.72625698324
-2016-09-06 08:11:36,674 DEBUG: 			View 3 : 0.709497206704
-2016-09-06 08:11:36,711 DEBUG: 			 Best view : 		View2
-2016-09-06 08:11:36,856 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:11:36,863 DEBUG: 			View 0 : 0.687150837989
-2016-09-06 08:11:36,870 DEBUG: 			View 1 : 0.586592178771
-2016-09-06 08:11:36,877 DEBUG: 			View 2 : 0.72625698324
-2016-09-06 08:11:36,884 DEBUG: 			View 3 : 0.709497206704
-2016-09-06 08:11:36,924 DEBUG: 			 Best view : 		View0
-2016-09-06 08:11:37,136 INFO: 	Start: 	 Classification
-2016-09-06 08:11:37,486 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:11:37,486 INFO: 	Start:	 Fold number 5
-2016-09-06 08:11:37,516 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:11:37,523 DEBUG: 			View 0 : 0.524590163934
-2016-09-06 08:11:37,529 DEBUG: 			View 1 : 0.524590163934
-2016-09-06 08:11:37,536 DEBUG: 			View 2 : 0.524590163934
-2016-09-06 08:11:37,542 DEBUG: 			View 3 : 0.524590163934
-2016-09-06 08:11:37,574 DEBUG: 			 Best view : 		View0
-2016-09-06 08:11:37,653 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:11:37,660 DEBUG: 			View 0 : 0.693989071038
-2016-09-06 08:11:37,667 DEBUG: 			View 1 : 0.677595628415
-2016-09-06 08:11:37,674 DEBUG: 			View 2 : 0.68306010929
-2016-09-06 08:11:37,682 DEBUG: 			View 3 : 0.72131147541
-2016-09-06 08:11:37,720 DEBUG: 			 Best view : 		View3
-2016-09-06 08:11:37,868 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:11:37,875 DEBUG: 			View 0 : 0.693989071038
-2016-09-06 08:11:37,882 DEBUG: 			View 1 : 0.677595628415
-2016-09-06 08:11:37,890 DEBUG: 			View 2 : 0.68306010929
-2016-09-06 08:11:37,897 DEBUG: 			View 3 : 0.72131147541
-2016-09-06 08:11:37,938 DEBUG: 			 Best view : 		View3
-2016-09-06 08:11:38,155 INFO: 	Start: 	 Classification
-2016-09-06 08:11:38,509 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:11:38,509 INFO: Done:	 Classification
-2016-09-06 08:11:38,509 INFO: Info:	 Time for Classification: 5[s]
-2016-09-06 08:11:38,509 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 08:11:40,533 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 72.4557589356
-	-On Test : 49.0476190476
-	-On Validation : 62.6666666667Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 7), View1 of shape (300, 5), View2 of shape (300, 17), View3 of shape (300, 20)
-	-5 folds
-	- Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:04        0:00:00
-	         Fold 5        0:00:05        0:00:00
-	          Total        0:00:15        0:00:01
-	So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.185792349727
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.173224043716
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.204371584699
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.2
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.25625
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.242613636364
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.239772727273
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.278409090909
-			- Percentage of time chosen : 0.3
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.192045454545
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.169886363636
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.186931818182
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.192045454545
-			- Percentage of time chosen : 0.1
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.191620111732
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.171508379888
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.198882681564
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.18938547486
-			- Percentage of time chosen : 0.1
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.191256830601
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.187978142077
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.189071038251
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.196721311475
-			- Percentage of time chosen : 0.2
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 52.4590163934
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 52.8409090909
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 53.4090909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 51.9553072626
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 52.4590163934
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 75.4098360656
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.0
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 78.4090909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View3
-		 Fold 3
-			Accuracy on train : 72.1590909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.0
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 72.625698324
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.2222222222
-			Selected View : View2
-		 Fold 5
-			Accuracy on train : 72.131147541
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.6666666667
-			Selected View : View3
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 75.4098360656
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.0
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 78.4090909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View3
-		 Fold 3
-			Accuracy on train : 67.6136363636
-			Accuracy on test : 0.0
-			Accuracy on validation : 56.6666666667
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 68.7150837989
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.2222222222
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 72.131147541
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.6666666667
-			Selected View : View3
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 52.4590163934
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 78.4090909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View3
-		 Fold 3
-			Accuracy on train : 53.4090909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 51.9553072626
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 52.4590163934
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-	- Iteration 5
-		 Fold 2
-			Accuracy on train : 52.8409090909
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-2016-09-06 08:11:40,716 INFO: Done:	 Result Analysis
-2016-09-06 08:11:40,858 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:40,858 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:40,858 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:40,859 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:40,859 INFO: Info:	 Shape of View0 :(300, 7)
-2016-09-06 08:11:40,859 INFO: Info:	 Shape of View0 :(300, 7)
-2016-09-06 08:11:40,860 INFO: Info:	 Shape of View1 :(300, 5)
-2016-09-06 08:11:40,860 INFO: Info:	 Shape of View1 :(300, 5)
-2016-09-06 08:11:40,860 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:11:40,860 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:11:40,861 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:11:40,861 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:11:40,861 INFO: Done:	 Read Database Files
-2016-09-06 08:11:40,861 INFO: Done:	 Read Database Files
-2016-09-06 08:11:40,861 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:11:40,861 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:11:40,867 INFO: Done:	 Determine validation split
-2016-09-06 08:11:40,867 INFO: Start:	 Determine 5 folds
-2016-09-06 08:11:40,867 INFO: Done:	 Determine validation split
-2016-09-06 08:11:40,867 INFO: Start:	 Determine 5 folds
-2016-09-06 08:11:40,877 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:11:40,877 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:11:40,877 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:11:40,877 INFO: Done:	 Determine folds
-2016-09-06 08:11:40,877 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:11:40,877 INFO: Start:	 Classification
-2016-09-06 08:11:40,877 INFO: 	Start:	 Fold number 1
-2016-09-06 08:11:40,878 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:11:40,878 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:11:40,878 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:11:40,878 INFO: Done:	 Determine folds
-2016-09-06 08:11:40,878 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:11:40,878 INFO: Start:	 Classification
-2016-09-06 08:11:40,878 INFO: 	Start:	 Fold number 1
-2016-09-06 08:11:40,937 INFO: 	Start: 	 Classification
-2016-09-06 08:11:40,964 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,000 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:11:41,000 INFO: 	Start:	 Fold number 2
-2016-09-06 08:11:41,029 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:11:41,030 INFO: 	Start:	 Fold number 2
-2016-09-06 08:11:41,075 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,084 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,107 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:11:41,107 INFO: 	Start:	 Fold number 3
-2016-09-06 08:11:41,159 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:11:41,159 INFO: 	Start:	 Fold number 3
-2016-09-06 08:11:41,180 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,212 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:11:41,212 INFO: 	Start:	 Fold number 4
-2016-09-06 08:11:41,214 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,288 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,289 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:11:41,289 INFO: 	Start:	 Fold number 4
-2016-09-06 08:11:41,321 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:11:41,321 INFO: 	Start:	 Fold number 5
-2016-09-06 08:11:41,343 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,395 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,417 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:11:41,418 INFO: 	Start:	 Fold number 5
-2016-09-06 08:11:41,429 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:11:41,429 INFO: Done:	 Classification
-2016-09-06 08:11:41,429 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:11:41,429 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:11:41,434 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 71.9436574753
-	-On Test : 50.4761904762
-	-On Validation : 69.7777777778
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SVM Linear with C : 5501
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:11:41,434 INFO: Done:	 Result Analysis
-2016-09-06 08:11:41,471 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,543 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:11:41,544 INFO: Done:	 Classification
-2016-09-06 08:11:41,544 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:11:41,544 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:11:41,548 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 67.9066961467
-	-On Test : 56.1904761905
-	-On Validation : 61.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SVM Linear with C : 5501
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:11:41,548 INFO: Done:	 Result Analysis
-2016-09-06 08:11:41,611 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:41,611 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:41,612 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:41,612 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:41,613 INFO: Info:	 Shape of View0 :(300, 7)
-2016-09-06 08:11:41,613 INFO: Info:	 Shape of View0 :(300, 7)
-2016-09-06 08:11:41,613 INFO: Info:	 Shape of View1 :(300, 5)
-2016-09-06 08:11:41,613 INFO: Info:	 Shape of View1 :(300, 5)
-2016-09-06 08:11:41,614 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:11:41,614 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:11:41,615 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:11:41,615 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:11:41,615 INFO: Done:	 Read Database Files
-2016-09-06 08:11:41,615 INFO: Done:	 Read Database Files
-2016-09-06 08:11:41,616 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:11:41,616 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:11:41,624 INFO: Done:	 Determine validation split
-2016-09-06 08:11:41,624 INFO: Done:	 Determine validation split
-2016-09-06 08:11:41,624 INFO: Start:	 Determine 5 folds
-2016-09-06 08:11:41,624 INFO: Start:	 Determine 5 folds
-2016-09-06 08:11:41,636 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:11:41,636 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:11:41,637 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:11:41,637 INFO: Done:	 Determine folds
-2016-09-06 08:11:41,637 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:11:41,637 INFO: Start:	 Classification
-2016-09-06 08:11:41,637 INFO: 	Start:	 Fold number 1
-2016-09-06 08:11:41,639 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:11:41,639 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:11:41,639 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:11:41,639 INFO: Done:	 Determine folds
-2016-09-06 08:11:41,639 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:11:41,639 INFO: Start:	 Classification
-2016-09-06 08:11:41,640 INFO: 	Start:	 Fold number 1
-2016-09-06 08:11:41,681 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,722 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:11:41,722 INFO: 	Start:	 Fold number 2
-2016-09-06 08:11:41,732 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,760 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,792 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:11:41,792 INFO: 	Start:	 Fold number 3
-2016-09-06 08:11:41,793 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:11:41,793 INFO: 	Start:	 Fold number 2
-2016-09-06 08:11:41,815 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,840 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:11:41,840 INFO: 	Start:	 Fold number 4
-2016-09-06 08:11:41,849 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,865 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,883 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:11:41,883 INFO: 	Start:	 Fold number 3
-2016-09-06 08:11:41,890 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:11:41,890 INFO: 	Start:	 Fold number 5
-2016-09-06 08:11:41,912 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,937 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:11:41,937 INFO: Done:	 Classification
-2016-09-06 08:11:41,937 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:11:41,937 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:11:41,938 INFO: 	Start: 	 Classification
-2016-09-06 08:11:41,942 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 49.5238095238
-	-On Validation : 84.8888888889
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:11:41,942 INFO: Done:	 Result Analysis
-2016-09-06 08:11:41,972 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:11:41,972 INFO: 	Start:	 Fold number 4
-2016-09-06 08:11:42,024 INFO: 	Start: 	 Classification
-2016-09-06 08:11:42,057 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:11:42,058 INFO: 	Start:	 Fold number 5
-2016-09-06 08:11:42,111 INFO: 	Start: 	 Classification
-2016-09-06 08:11:42,144 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:11:42,144 INFO: Done:	 Classification
-2016-09-06 08:11:42,144 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:11:42,144 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:11:42,149 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 10.3999960613
-	-On Test : 9.04761904762
-	-On Validation : 9.55555555556
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SVM Linear with C : 5501
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:11:42,149 INFO: Done:	 Result Analysis
-2016-09-06 08:11:42,262 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:42,262 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:42,262 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:42,262 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:42,263 INFO: Info:	 Shape of View0 :(300, 7)
-2016-09-06 08:11:42,263 INFO: Info:	 Shape of View0 :(300, 7)
-2016-09-06 08:11:42,263 INFO: Info:	 Shape of View1 :(300, 5)
-2016-09-06 08:11:42,263 INFO: Info:	 Shape of View1 :(300, 5)
-2016-09-06 08:11:42,264 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:11:42,264 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:11:42,264 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:11:42,264 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:11:42,264 INFO: Done:	 Read Database Files
-2016-09-06 08:11:42,264 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:11:42,264 INFO: Done:	 Read Database Files
-2016-09-06 08:11:42,264 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:11:42,270 INFO: Done:	 Determine validation split
-2016-09-06 08:11:42,270 INFO: Done:	 Determine validation split
-2016-09-06 08:11:42,270 INFO: Start:	 Determine 5 folds
-2016-09-06 08:11:42,270 INFO: Start:	 Determine 5 folds
-2016-09-06 08:11:42,281 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:11:42,281 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:11:42,281 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:11:42,281 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:11:42,282 INFO: Done:	 Determine folds
-2016-09-06 08:11:42,282 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:11:42,282 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:11:42,282 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:11:42,282 INFO: Start:	 Classification
-2016-09-06 08:11:42,282 INFO: Done:	 Determine folds
-2016-09-06 08:11:42,282 INFO: 	Start:	 Fold number 1
-2016-09-06 08:11:42,282 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:11:42,282 INFO: Start:	 Classification
-2016-09-06 08:11:42,282 INFO: 	Start:	 Fold number 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c0cb8a00..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 841bc5e7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 21d20232..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.671428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.671428571429
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0724566e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.638095238095
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.638095238095
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e4a004d2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ec5fd5cc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db4896c6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5059
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9abab3ad..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7a4f3e07..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 023fb03b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 29
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 22f0d083..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.680952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 6
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.680952380952
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9e35d2e7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b02bf490..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cd534e78..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.452380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.452380952381
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4703bb45..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bb42a14a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 33fd6ac9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a010ca9a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 26b9fae1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.77619047619
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.77619047619
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fdb9c4cf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.633333333333
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.633333333333
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 46fdee04..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7cb67987..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 408a48a6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b06ad467..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 30ac7fad..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6c528ab1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.628571428571
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 712fa6e9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 6
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.738095238095
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 12e0b666..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.714285714286
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.714285714286
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5a304273..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.652380952381
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ea26c2d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5501
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.471428571429
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081133Results-Fusion-LateFusion-BayesianInference-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081133Results-Fusion-LateFusion-BayesianInference-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index a83cf9eb..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081133Results-Fusion-LateFusion-BayesianInference-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 70.9440472969
-	-On Test : 56.6666666667
-	-On Validation : 66.2222222222
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SVM Linear with C : 5501
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081140Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-081140Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081140Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081140Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index ef6fa379..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081140Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,215 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 72.4557589356
-	-On Test : 49.0476190476
-	-On Validation : 62.6666666667Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 7), View1 of shape (300, 5), View2 of shape (300, 17), View3 of shape (300, 20)
-	-5 folds
-	- Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:04        0:00:00
-	         Fold 5        0:00:05        0:00:00
-	          Total        0:00:15        0:00:01
-	So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.185792349727
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.173224043716
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.204371584699
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.2
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.25625
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.242613636364
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.239772727273
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.278409090909
-			- Percentage of time chosen : 0.3
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.192045454545
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.169886363636
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.186931818182
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.192045454545
-			- Percentage of time chosen : 0.1
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.191620111732
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.171508379888
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.198882681564
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.18938547486
-			- Percentage of time chosen : 0.1
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.191256830601
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.187978142077
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.189071038251
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.196721311475
-			- Percentage of time chosen : 0.2
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 52.4590163934
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 52.8409090909
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 53.4090909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 51.9553072626
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 52.4590163934
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 75.4098360656
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.0
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 78.4090909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View3
-		 Fold 3
-			Accuracy on train : 72.1590909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.0
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 72.625698324
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.2222222222
-			Selected View : View2
-		 Fold 5
-			Accuracy on train : 72.131147541
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.6666666667
-			Selected View : View3
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 75.4098360656
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.0
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 78.4090909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View3
-		 Fold 3
-			Accuracy on train : 67.6136363636
-			Accuracy on test : 0.0
-			Accuracy on validation : 56.6666666667
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 68.7150837989
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.2222222222
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 72.131147541
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.6666666667
-			Selected View : View3
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 52.4590163934
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 78.4090909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.7777777778
-			Selected View : View3
-		 Fold 3
-			Accuracy on train : 53.4090909091
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 51.9553072626
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 52.4590163934
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-	- Iteration 5
-		 Fold 2
-			Accuracy on train : 52.8409090909
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 793ed7da..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 49.5238095238
-	-On Validation : 84.8888888889
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-MajorityVoting-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-MajorityVoting-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 1c144111..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-MajorityVoting-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 67.9066961467
-	-On Test : 56.1904761905
-	-On Validation : 61.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SVM Linear with C : 5501
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-SVMForLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-SVMForLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 4253761d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-SVMForLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 71.9436574753
-	-On Test : 50.4761904762
-	-On Validation : 69.7777777778
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SVM Linear with C : 5501
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081142Results-Fusion-LateFusion-WeightedLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081142Results-Fusion-LateFusion-WeightedLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 73489cf0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081142Results-Fusion-LateFusion-WeightedLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 10.3999960613
-	-On Test : 9.04761904762
-	-On Validation : 9.55555555556
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SVM Linear with C : 5501
-		- Random Forest with num_esimators : 3, max_depth : 2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081346-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-081346-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index c46e481b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081346-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1922 +0,0 @@
-2016-09-06 08:13:46,019 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:13:46,019 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000152125 Gbytes /!\ 
-2016-09-06 08:13:51,034 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 08:13:51,037 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 08:13:51,094 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,094 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,095 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:13:51,095 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:13:51,095 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:51,095 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:51,095 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,096 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,096 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,096 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,096 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:51,096 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:51,096 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,096 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,160 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:51,161 DEBUG: Start:	 Training
-2016-09-06 08:13:51,163 DEBUG: Info:	 Time for Training: 0.0696048736572[s]
-2016-09-06 08:13:51,163 DEBUG: Done:	 Training
-2016-09-06 08:13:51,163 DEBUG: Start:	 Predicting
-2016-09-06 08:13:51,166 DEBUG: Done:	 Predicting
-2016-09-06 08:13:51,166 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:51,167 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:51,167 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,167 INFO: Done:	 Result Analysis
-2016-09-06 08:13:51,187 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:51,187 DEBUG: Start:	 Training
-2016-09-06 08:13:51,191 DEBUG: Info:	 Time for Training: 0.0979900360107[s]
-2016-09-06 08:13:51,191 DEBUG: Done:	 Training
-2016-09-06 08:13:51,192 DEBUG: Start:	 Predicting
-2016-09-06 08:13:51,194 DEBUG: Done:	 Predicting
-2016-09-06 08:13:51,195 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:51,196 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:51,196 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,196 INFO: Done:	 Result Analysis
-2016-09-06 08:13:51,340 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,341 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:13:51,341 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:51,342 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,342 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:13:51,342 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,342 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:51,342 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,342 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:51,343 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,343 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,343 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,343 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:51,343 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,394 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:51,394 DEBUG: Start:	 Training
-2016-09-06 08:13:51,395 DEBUG: Info:	 Time for Training: 0.0554051399231[s]
-2016-09-06 08:13:51,395 DEBUG: Done:	 Training
-2016-09-06 08:13:51,395 DEBUG: Start:	 Predicting
-2016-09-06 08:13:51,402 DEBUG: Done:	 Predicting
-2016-09-06 08:13:51,402 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:51,404 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:51,404 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 32
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,404 INFO: Done:	 Result Analysis
-2016-09-06 08:13:51,645 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:51,645 DEBUG: Start:	 Training
-2016-09-06 08:13:51,677 DEBUG: Info:	 Time for Training: 0.336097955704[s]
-2016-09-06 08:13:51,677 DEBUG: Done:	 Training
-2016-09-06 08:13:51,677 DEBUG: Start:	 Predicting
-2016-09-06 08:13:51,682 DEBUG: Done:	 Predicting
-2016-09-06 08:13:51,682 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:51,683 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:51,683 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 11, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,683 INFO: Done:	 Result Analysis
-2016-09-06 08:13:51,788 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,788 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,788 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:13:51,788 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:13:51,789 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:51,789 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:51,789 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,789 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,789 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,789 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,790 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:51,790 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:51,790 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,790 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,872 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:51,872 DEBUG: Start:	 Training
-2016-09-06 08:13:51,873 DEBUG: Info:	 Time for Training: 0.0851731300354[s]
-2016-09-06 08:13:51,873 DEBUG: Done:	 Training
-2016-09-06 08:13:51,873 DEBUG: Start:	 Predicting
-2016-09-06 08:13:51,877 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:51,877 DEBUG: Start:	 Training
-2016-09-06 08:13:51,891 DEBUG: Done:	 Predicting
-2016-09-06 08:13:51,891 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:51,893 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:51,893 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,893 INFO: Done:	 Result Analysis
-2016-09-06 08:13:51,907 DEBUG: Info:	 Time for Training: 0.119738101959[s]
-2016-09-06 08:13:51,907 DEBUG: Done:	 Training
-2016-09-06 08:13:51,908 DEBUG: Start:	 Predicting
-2016-09-06 08:13:51,911 DEBUG: Done:	 Predicting
-2016-09-06 08:13:51,911 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:51,912 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:51,912 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,913 INFO: Done:	 Result Analysis
-2016-09-06 08:13:52,037 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,037 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:13:52,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:13:52,037 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:52,037 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:52,038 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:52,038 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:52,038 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:52,038 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:52,038 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:52,038 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:52,038 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,038 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,121 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:52,121 DEBUG: Start:	 Training
-2016-09-06 08:13:52,134 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:52,134 DEBUG: Start:	 Training
-2016-09-06 08:13:52,139 DEBUG: Info:	 Time for Training: 0.102441072464[s]
-2016-09-06 08:13:52,139 DEBUG: Done:	 Training
-2016-09-06 08:13:52,139 DEBUG: Start:	 Predicting
-2016-09-06 08:13:52,145 DEBUG: Done:	 Predicting
-2016-09-06 08:13:52,145 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:52,146 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:52,146 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,146 INFO: Done:	 Result Analysis
-2016-09-06 08:13:52,154 DEBUG: Info:	 Time for Training: 0.117784976959[s]
-2016-09-06 08:13:52,154 DEBUG: Done:	 Training
-2016-09-06 08:13:52,154 DEBUG: Start:	 Predicting
-2016-09-06 08:13:52,159 DEBUG: Done:	 Predicting
-2016-09-06 08:13:52,159 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:52,160 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:52,160 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5781
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,160 INFO: Done:	 Result Analysis
-2016-09-06 08:13:52,291 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,291 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:13:52,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:13:52,292 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:52,292 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:52,293 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,293 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,293 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:52,293 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,294 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,294 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,294 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:52,294 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,359 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:52,359 DEBUG: Start:	 Training
-2016-09-06 08:13:52,361 DEBUG: Info:	 Time for Training: 0.070493221283[s]
-2016-09-06 08:13:52,361 DEBUG: Done:	 Training
-2016-09-06 08:13:52,361 DEBUG: Start:	 Predicting
-2016-09-06 08:13:52,365 DEBUG: Done:	 Predicting
-2016-09-06 08:13:52,365 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:52,366 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:52,366 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,366 INFO: Done:	 Result Analysis
-2016-09-06 08:13:52,391 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:52,391 DEBUG: Start:	 Training
-2016-09-06 08:13:52,395 DEBUG: Info:	 Time for Training: 0.10508108139[s]
-2016-09-06 08:13:52,395 DEBUG: Done:	 Training
-2016-09-06 08:13:52,395 DEBUG: Start:	 Predicting
-2016-09-06 08:13:52,399 DEBUG: Done:	 Predicting
-2016-09-06 08:13:52,399 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:52,401 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:52,402 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,402 INFO: Done:	 Result Analysis
-2016-09-06 08:13:52,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:13:52,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:13:52,536 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:52,536 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:52,537 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,537 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,537 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,537 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,537 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:52,537 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:52,537 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,537 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,594 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:52,594 DEBUG: Start:	 Training
-2016-09-06 08:13:52,595 DEBUG: Info:	 Time for Training: 0.0595271587372[s]
-2016-09-06 08:13:52,595 DEBUG: Done:	 Training
-2016-09-06 08:13:52,595 DEBUG: Start:	 Predicting
-2016-09-06 08:13:52,602 DEBUG: Done:	 Predicting
-2016-09-06 08:13:52,602 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:52,603 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:52,603 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,603 INFO: Done:	 Result Analysis
-2016-09-06 08:13:52,812 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:52,812 DEBUG: Start:	 Training
-2016-09-06 08:13:52,842 DEBUG: Info:	 Time for Training: 0.306573152542[s]
-2016-09-06 08:13:52,842 DEBUG: Done:	 Training
-2016-09-06 08:13:52,842 DEBUG: Start:	 Predicting
-2016-09-06 08:13:52,847 DEBUG: Done:	 Predicting
-2016-09-06 08:13:52,847 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:52,848 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:52,848 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 11, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,848 INFO: Done:	 Result Analysis
-2016-09-06 08:13:52,988 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,988 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,988 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:13:52,988 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:13:52,989 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:52,989 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:52,989 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,989 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,989 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,989 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,990 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:52,990 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:52,990 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,990 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,065 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:53,065 DEBUG: Start:	 Training
-2016-09-06 08:13:53,066 DEBUG: Info:	 Time for Training: 0.0787451267242[s]
-2016-09-06 08:13:53,066 DEBUG: Done:	 Training
-2016-09-06 08:13:53,066 DEBUG: Start:	 Predicting
-2016-09-06 08:13:53,077 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:53,077 DEBUG: Start:	 Training
-2016-09-06 08:13:53,083 DEBUG: Done:	 Predicting
-2016-09-06 08:13:53,083 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:53,085 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:53,085 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,085 INFO: Done:	 Result Analysis
-2016-09-06 08:13:53,104 DEBUG: Info:	 Time for Training: 0.116367816925[s]
-2016-09-06 08:13:53,104 DEBUG: Done:	 Training
-2016-09-06 08:13:53,104 DEBUG: Start:	 Predicting
-2016-09-06 08:13:53,108 DEBUG: Done:	 Predicting
-2016-09-06 08:13:53,108 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:53,109 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:53,109 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,109 INFO: Done:	 Result Analysis
-2016-09-06 08:13:53,238 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,238 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,238 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:13:53,238 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:13:53,238 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:53,238 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:53,239 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:53,239 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:53,239 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:53,239 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:53,239 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:53,239 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:53,240 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,240 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,358 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:53,359 DEBUG: Start:	 Training
-2016-09-06 08:13:53,373 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:53,373 DEBUG: Start:	 Training
-2016-09-06 08:13:53,382 DEBUG: Info:	 Time for Training: 0.144605875015[s]
-2016-09-06 08:13:53,382 DEBUG: Done:	 Training
-2016-09-06 08:13:53,382 DEBUG: Start:	 Predicting
-2016-09-06 08:13:53,390 DEBUG: Done:	 Predicting
-2016-09-06 08:13:53,390 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:53,391 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:53,391 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,392 INFO: Done:	 Result Analysis
-2016-09-06 08:13:53,394 DEBUG: Info:	 Time for Training: 0.156768798828[s]
-2016-09-06 08:13:53,394 DEBUG: Done:	 Training
-2016-09-06 08:13:53,394 DEBUG: Start:	 Predicting
-2016-09-06 08:13:53,398 DEBUG: Done:	 Predicting
-2016-09-06 08:13:53,398 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:53,399 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:53,399 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,400 INFO: Done:	 Result Analysis
-2016-09-06 08:13:53,484 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,484 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,484 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:13:53,484 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:13:53,484 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:53,484 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:53,485 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:53,485 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:53,485 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:53,485 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:53,485 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:53,485 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,485 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:53,486 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,543 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:53,543 DEBUG: Start:	 Training
-2016-09-06 08:13:53,545 DEBUG: Info:	 Time for Training: 0.0612480640411[s]
-2016-09-06 08:13:53,545 DEBUG: Done:	 Training
-2016-09-06 08:13:53,545 DEBUG: Start:	 Predicting
-2016-09-06 08:13:53,547 DEBUG: Done:	 Predicting
-2016-09-06 08:13:53,548 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:53,549 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:53,549 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,549 INFO: Done:	 Result Analysis
-2016-09-06 08:13:53,570 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:53,570 DEBUG: Start:	 Training
-2016-09-06 08:13:53,574 DEBUG: Info:	 Time for Training: 0.0900461673737[s]
-2016-09-06 08:13:53,574 DEBUG: Done:	 Training
-2016-09-06 08:13:53,574 DEBUG: Start:	 Predicting
-2016-09-06 08:13:53,577 DEBUG: Done:	 Predicting
-2016-09-06 08:13:53,577 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:53,578 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:53,578 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,579 INFO: Done:	 Result Analysis
-2016-09-06 08:13:53,634 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,634 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:13:53,634 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,634 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:53,634 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:13:53,634 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:53,635 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:53,635 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:53,635 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:53,635 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:53,635 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:53,635 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:53,635 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,635 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,688 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:53,688 DEBUG: Start:	 Training
-2016-09-06 08:13:53,689 DEBUG: Info:	 Time for Training: 0.0553061962128[s]
-2016-09-06 08:13:53,689 DEBUG: Done:	 Training
-2016-09-06 08:13:53,689 DEBUG: Start:	 Predicting
-2016-09-06 08:13:53,696 DEBUG: Done:	 Predicting
-2016-09-06 08:13:53,696 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:53,697 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:53,697 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,697 INFO: Done:	 Result Analysis
-2016-09-06 08:13:53,904 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:53,904 DEBUG: Start:	 Training
-2016-09-06 08:13:53,932 DEBUG: Info:	 Time for Training: 0.298815011978[s]
-2016-09-06 08:13:53,932 DEBUG: Done:	 Training
-2016-09-06 08:13:53,933 DEBUG: Start:	 Predicting
-2016-09-06 08:13:53,937 DEBUG: Done:	 Predicting
-2016-09-06 08:13:53,937 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:53,938 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:53,938 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 11, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,939 INFO: Done:	 Result Analysis
-2016-09-06 08:13:54,083 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,084 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:13:54,084 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:54,084 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,084 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:13:54,085 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:54,085 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:54,085 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:54,085 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:54,085 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,086 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:54,086 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:54,086 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:54,087 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,169 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:54,170 DEBUG: Start:	 Training
-2016-09-06 08:13:54,170 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:54,170 DEBUG: Start:	 Training
-2016-09-06 08:13:54,170 DEBUG: Info:	 Time for Training: 0.0873100757599[s]
-2016-09-06 08:13:54,171 DEBUG: Done:	 Training
-2016-09-06 08:13:54,171 DEBUG: Start:	 Predicting
-2016-09-06 08:13:54,190 DEBUG: Info:	 Time for Training: 0.107427835464[s]
-2016-09-06 08:13:54,191 DEBUG: Done:	 Training
-2016-09-06 08:13:54,191 DEBUG: Start:	 Predicting
-2016-09-06 08:13:54,192 DEBUG: Done:	 Predicting
-2016-09-06 08:13:54,193 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:54,195 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:54,195 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.655555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.655555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,196 INFO: Done:	 Result Analysis
-2016-09-06 08:13:54,198 DEBUG: Done:	 Predicting
-2016-09-06 08:13:54,198 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:54,199 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:54,199 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.457142857143
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,199 INFO: Done:	 Result Analysis
-2016-09-06 08:13:54,332 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,332 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,333 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:13:54,333 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:13:54,333 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:54,333 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:54,334 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:54,334 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:54,334 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:54,334 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:54,334 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:54,334 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:54,334 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,334 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,418 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:54,418 DEBUG: Start:	 Training
-2016-09-06 08:13:54,423 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:54,423 DEBUG: Start:	 Training
-2016-09-06 08:13:54,436 DEBUG: Info:	 Time for Training: 0.103874921799[s]
-2016-09-06 08:13:54,436 DEBUG: Done:	 Training
-2016-09-06 08:13:54,436 DEBUG: Start:	 Predicting
-2016-09-06 08:13:54,441 DEBUG: Info:	 Time for Training: 0.109354972839[s]
-2016-09-06 08:13:54,441 DEBUG: Done:	 Training
-2016-09-06 08:13:54,442 DEBUG: Start:	 Predicting
-2016-09-06 08:13:54,442 DEBUG: Done:	 Predicting
-2016-09-06 08:13:54,442 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:54,443 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:54,443 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,443 INFO: Done:	 Result Analysis
-2016-09-06 08:13:54,445 DEBUG: Done:	 Predicting
-2016-09-06 08:13:54,445 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:54,446 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:54,446 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,446 INFO: Done:	 Result Analysis
-2016-09-06 08:13:54,584 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,584 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,585 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:13:54,585 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:13:54,585 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:54,585 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:54,586 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:54,586 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:54,586 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:54,586 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:54,586 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:54,586 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:54,586 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,586 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,688 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:54,689 DEBUG: Start:	 Training
-2016-09-06 08:13:54,694 DEBUG: Info:	 Time for Training: 0.110232114792[s]
-2016-09-06 08:13:54,694 DEBUG: Done:	 Training
-2016-09-06 08:13:54,694 DEBUG: Start:	 Predicting
-2016-09-06 08:13:54,698 DEBUG: Done:	 Predicting
-2016-09-06 08:13:54,698 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:54,699 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:54,699 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,700 INFO: Done:	 Result Analysis
-2016-09-06 08:13:54,720 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:54,720 DEBUG: Start:	 Training
-2016-09-06 08:13:54,725 DEBUG: Info:	 Time for Training: 0.141868114471[s]
-2016-09-06 08:13:54,726 DEBUG: Done:	 Training
-2016-09-06 08:13:54,726 DEBUG: Start:	 Predicting
-2016-09-06 08:13:54,729 DEBUG: Done:	 Predicting
-2016-09-06 08:13:54,729 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:54,730 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:54,730 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,731 INFO: Done:	 Result Analysis
-2016-09-06 08:13:54,836 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,836 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:13:54,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:13:54,837 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:54,837 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:54,838 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:54,838 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:54,838 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:54,838 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:54,838 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:54,838 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:54,838 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,838 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,923 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:54,923 DEBUG: Start:	 Training
-2016-09-06 08:13:54,924 DEBUG: Info:	 Time for Training: 0.088819026947[s]
-2016-09-06 08:13:54,924 DEBUG: Done:	 Training
-2016-09-06 08:13:54,924 DEBUG: Start:	 Predicting
-2016-09-06 08:13:54,936 DEBUG: Done:	 Predicting
-2016-09-06 08:13:54,936 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:54,937 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:54,937 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,938 INFO: Done:	 Result Analysis
-2016-09-06 08:13:55,151 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:55,152 DEBUG: Start:	 Training
-2016-09-06 08:13:55,181 DEBUG: Info:	 Time for Training: 0.346240997314[s]
-2016-09-06 08:13:55,181 DEBUG: Done:	 Training
-2016-09-06 08:13:55,181 DEBUG: Start:	 Predicting
-2016-09-06 08:13:55,186 DEBUG: Done:	 Predicting
-2016-09-06 08:13:55,186 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:55,187 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:55,187 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 11, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.980952380952
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:13:55,187 INFO: Done:	 Result Analysis
-2016-09-06 08:13:55,284 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:55,285 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:55,285 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:13:55,285 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:13:55,285 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:55,285 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:13:55,286 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:55,286 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:55,286 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:55,286 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:55,287 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:55,287 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:13:55,287 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:55,287 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:13:55,363 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:55,363 DEBUG: Start:	 Training
-2016-09-06 08:13:55,364 DEBUG: Info:	 Time for Training: 0.0802390575409[s]
-2016-09-06 08:13:55,364 DEBUG: Done:	 Training
-2016-09-06 08:13:55,364 DEBUG: Start:	 Predicting
-2016-09-06 08:13:55,376 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:13:55,376 DEBUG: Start:	 Training
-2016-09-06 08:13:55,377 DEBUG: Done:	 Predicting
-2016-09-06 08:13:55,377 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:55,378 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:55,378 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:13:55,379 INFO: Done:	 Result Analysis
-2016-09-06 08:13:55,398 DEBUG: Info:	 Time for Training: 0.113732099533[s]
-2016-09-06 08:13:55,398 DEBUG: Done:	 Training
-2016-09-06 08:13:55,398 DEBUG: Start:	 Predicting
-2016-09-06 08:13:55,402 DEBUG: Done:	 Predicting
-2016-09-06 08:13:55,402 DEBUG: Start:	 Getting Results
-2016-09-06 08:13:55,403 DEBUG: Done:	 Getting Results
-2016-09-06 08:13:55,403 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:13:55,403 INFO: Done:	 Result Analysis
-2016-09-06 08:13:55,682 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:13:55,682 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:13:55,683 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:13:55,683 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:13:55,683 INFO: Info:	 Shape of View0 :(300, 17)
-2016-09-06 08:13:55,683 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:13:55,683 INFO: Info:	 Shape of View0 :(300, 17)
-2016-09-06 08:13:55,684 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:13:55,684 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 08:13:55,684 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 08:13:55,684 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:13:55,684 INFO: Done:	 Read Database Files
-2016-09-06 08:13:55,685 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:13:55,685 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:13:55,685 INFO: Done:	 Read Database Files
-2016-09-06 08:13:55,685 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:13:55,689 INFO: Done:	 Determine validation split
-2016-09-06 08:13:55,689 INFO: Start:	 Determine 5 folds
-2016-09-06 08:13:55,691 INFO: Done:	 Determine validation split
-2016-09-06 08:13:55,691 INFO: Start:	 Determine 5 folds
-2016-09-06 08:13:55,697 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:13:55,698 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:13:55,698 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:13:55,698 INFO: Done:	 Determine folds
-2016-09-06 08:13:55,698 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:13:55,698 INFO: Start:	 Classification
-2016-09-06 08:13:55,698 INFO: 	Start:	 Fold number 1
-2016-09-06 08:13:55,699 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:13:55,699 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:13:55,699 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:13:55,699 INFO: Done:	 Determine folds
-2016-09-06 08:13:55,699 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 08:13:55,699 INFO: Start:	 Classification
-2016-09-06 08:13:55,699 INFO: 	Start:	 Fold number 1
-2016-09-06 08:13:55,727 INFO: 	Start: 	 Classification
-2016-09-06 08:13:55,735 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:13:55,744 DEBUG: 			View 0 : 0.459016393443
-2016-09-06 08:13:55,751 DEBUG: 			View 1 : 0.459016393443
-2016-09-06 08:13:55,760 DEBUG: 			View 2 : 0.459016393443
-2016-09-06 08:13:55,767 DEBUG: 			View 3 : 0.459016393443
-2016-09-06 08:13:55,767 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 08:13:55,777 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:13:55,777 INFO: 	Start:	 Fold number 2
-2016-09-06 08:13:55,804 INFO: 	Start: 	 Classification
-2016-09-06 08:13:55,805 DEBUG: 			 Best view : 		View0
-2016-09-06 08:13:55,836 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:13:55,837 INFO: 	Start:	 Fold number 3
-2016-09-06 08:13:55,862 INFO: 	Start: 	 Classification
-2016-09-06 08:13:55,894 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:13:55,895 INFO: 	Start:	 Fold number 4
-2016-09-06 08:13:55,898 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:13:55,906 DEBUG: 			View 0 : 0.770491803279
-2016-09-06 08:13:55,915 DEBUG: 			View 1 : 0.68306010929
-2016-09-06 08:13:55,921 INFO: 	Start: 	 Classification
-2016-09-06 08:13:55,922 DEBUG: 			View 2 : 0.715846994536
-2016-09-06 08:13:55,933 DEBUG: 			View 3 : 0.655737704918
-2016-09-06 08:13:55,953 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:13:55,954 INFO: 	Start:	 Fold number 5
-2016-09-06 08:13:55,977 DEBUG: 			 Best view : 		View0
-2016-09-06 08:13:55,980 INFO: 	Start: 	 Classification
-2016-09-06 08:13:56,012 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:13:56,012 INFO: Done:	 Classification
-2016-09-06 08:13:56,012 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:13:56,012 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:13:56,017 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 59.2121406739
-	-On Test : 52.6829268293
-	-On Validation : 57.7528089888
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- K nearest Neighbors with  n_neighbors: 43
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:13:56,017 INFO: Done:	 Result Analysis
-2016-09-06 08:13:56,148 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:13:56,159 DEBUG: 			View 0 : 0.770491803279
-2016-09-06 08:13:56,166 DEBUG: 			View 1 : 0.68306010929
-2016-09-06 08:13:56,173 DEBUG: 			View 2 : 0.715846994536
-2016-09-06 08:13:56,181 DEBUG: 			View 3 : 0.655737704918
-2016-09-06 08:13:56,222 DEBUG: 			 Best view : 		View0
-2016-09-06 08:13:56,442 INFO: 	Start: 	 Classification
-2016-09-06 08:13:56,796 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:13:56,796 INFO: 	Start:	 Fold number 2
-2016-09-06 08:13:56,825 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:13:56,832 DEBUG: 			View 0 : 0.541899441341
-2016-09-06 08:13:56,839 DEBUG: 			View 1 : 0.541899441341
-2016-09-06 08:13:56,845 DEBUG: 			View 2 : 0.541899441341
-2016-09-06 08:13:56,851 DEBUG: 			View 3 : 0.541899441341
-2016-09-06 08:13:56,883 DEBUG: 			 Best view : 		View0
-2016-09-06 08:13:56,964 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:13:56,971 DEBUG: 			View 0 : 0.648044692737
-2016-09-06 08:13:56,978 DEBUG: 			View 1 : 0.664804469274
-2016-09-06 08:13:56,985 DEBUG: 			View 2 : 0.715083798883
-2016-09-06 08:13:56,993 DEBUG: 			View 3 : 0.675977653631
-2016-09-06 08:13:57,030 DEBUG: 			 Best view : 		View2
-2016-09-06 08:13:57,178 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:13:57,185 DEBUG: 			View 0 : 0.648044692737
-2016-09-06 08:13:57,192 DEBUG: 			View 1 : 0.664804469274
-2016-09-06 08:13:57,199 DEBUG: 			View 2 : 0.715083798883
-2016-09-06 08:13:57,206 DEBUG: 			View 3 : 0.675977653631
-2016-09-06 08:13:57,247 DEBUG: 			 Best view : 		View2
-2016-09-06 08:13:57,460 INFO: 	Start: 	 Classification
-2016-09-06 08:13:57,809 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:13:57,809 INFO: 	Start:	 Fold number 3
-2016-09-06 08:13:57,839 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:13:57,847 DEBUG: 			View 0 : 0.483870967742
-2016-09-06 08:13:57,854 DEBUG: 			View 1 : 0.483870967742
-2016-09-06 08:13:57,861 DEBUG: 			View 2 : 0.537634408602
-2016-09-06 08:13:57,868 DEBUG: 			View 3 : 0.516129032258
-2016-09-06 08:13:57,901 DEBUG: 			 Best view : 		View3
-2016-09-06 08:13:57,983 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:13:57,991 DEBUG: 			View 0 : 0.720430107527
-2016-09-06 08:13:57,998 DEBUG: 			View 1 : 0.629032258065
-2016-09-06 08:13:58,005 DEBUG: 			View 2 : 0.704301075269
-2016-09-06 08:13:58,013 DEBUG: 			View 3 : 0.731182795699
-2016-09-06 08:13:58,052 DEBUG: 			 Best view : 		View3
-2016-09-06 08:13:58,206 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:13:58,213 DEBUG: 			View 0 : 0.720430107527
-2016-09-06 08:13:58,221 DEBUG: 			View 1 : 0.629032258065
-2016-09-06 08:13:58,228 DEBUG: 			View 2 : 0.693548387097
-2016-09-06 08:13:58,236 DEBUG: 			View 3 : 0.731182795699
-2016-09-06 08:13:58,278 DEBUG: 			 Best view : 		View2
-2016-09-06 08:13:58,500 INFO: 	Start: 	 Classification
-2016-09-06 08:13:58,857 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:13:58,857 INFO: 	Start:	 Fold number 4
-2016-09-06 08:13:58,886 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:13:58,893 DEBUG: 			View 0 : 0.530726256983
-2016-09-06 08:13:58,899 DEBUG: 			View 1 : 0.530726256983
-2016-09-06 08:13:58,906 DEBUG: 			View 2 : 0.530726256983
-2016-09-06 08:13:58,912 DEBUG: 			View 3 : 0.530726256983
-2016-09-06 08:13:58,944 DEBUG: 			 Best view : 		View0
-2016-09-06 08:13:59,023 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:13:59,030 DEBUG: 			View 0 : 0.709497206704
-2016-09-06 08:13:59,037 DEBUG: 			View 1 : 0.614525139665
-2016-09-06 08:13:59,044 DEBUG: 			View 2 : 0.659217877095
-2016-09-06 08:13:59,051 DEBUG: 			View 3 : 0.648044692737
-2016-09-06 08:13:59,089 DEBUG: 			 Best view : 		View0
-2016-09-06 08:13:59,235 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:13:59,243 DEBUG: 			View 0 : 0.709497206704
-2016-09-06 08:13:59,250 DEBUG: 			View 1 : 0.614525139665
-2016-09-06 08:13:59,257 DEBUG: 			View 2 : 0.659217877095
-2016-09-06 08:13:59,264 DEBUG: 			View 3 : 0.648044692737
-2016-09-06 08:13:59,305 DEBUG: 			 Best view : 		View0
-2016-09-06 08:13:59,519 INFO: 	Start: 	 Classification
-2016-09-06 08:13:59,869 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:13:59,869 INFO: 	Start:	 Fold number 5
-2016-09-06 08:13:59,898 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:13:59,905 DEBUG: 			View 0 : 0.456043956044
-2016-09-06 08:13:59,911 DEBUG: 			View 1 : 0.456043956044
-2016-09-06 08:13:59,918 DEBUG: 			View 2 : 0.456043956044
-2016-09-06 08:13:59,925 DEBUG: 			View 3 : 0.456043956044
-2016-09-06 08:13:59,925 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 08:13:59,957 DEBUG: 			 Best view : 		View0
-2016-09-06 08:14:00,037 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:14:00,044 DEBUG: 			View 0 : 0.692307692308
-2016-09-06 08:14:00,052 DEBUG: 			View 1 : 0.637362637363
-2016-09-06 08:14:00,059 DEBUG: 			View 2 : 0.675824175824
-2016-09-06 08:14:00,067 DEBUG: 			View 3 : 0.659340659341
-2016-09-06 08:14:00,105 DEBUG: 			 Best view : 		View0
-2016-09-06 08:14:00,254 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:14:00,261 DEBUG: 			View 0 : 0.692307692308
-2016-09-06 08:14:00,269 DEBUG: 			View 1 : 0.637362637363
-2016-09-06 08:14:00,276 DEBUG: 			View 2 : 0.675824175824
-2016-09-06 08:14:00,283 DEBUG: 			View 3 : 0.659340659341
-2016-09-06 08:14:00,324 DEBUG: 			 Best view : 		View0
-2016-09-06 08:14:00,541 INFO: 	Start: 	 Classification
-2016-09-06 08:14:00,894 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:14:00,894 INFO: Done:	 Classification
-2016-09-06 08:14:00,894 INFO: Info:	 Time for Classification: 5[s]
-2016-09-06 08:14:00,894 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 08:14:02,808 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 71.7284678753
-	-On Test : 54.1463414634
-	-On Validation : 64.0449438202Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 17), View1 of shape (300, 12), View2 of shape (300, 8), View3 of shape (300, 20)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:01        0:00:00
-	         Fold 3        0:00:02        0:00:00
-	         Fold 4        0:00:03        0:00:00
-	         Fold 5        0:00:04        0:00:00
-	          Total        0:00:13        0:00:01
-	So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.2
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.182513661202
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.189071038251
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.177049180328
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.183798882682
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.187150837989
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.197206703911
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.18938547486
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.19247311828
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.174193548387
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.193548387097
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.197849462366
-			- Percentage of time chosen : 0.2
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.194972067039
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.175977653631
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.184916201117
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.182681564246
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.184065934066
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.173076923077
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.180769230769
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.177472527473
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 54.0983606557
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 54.1899441341
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 54.3010752688
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 53.0726256983
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 54.3956043956
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 77.0491803279
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.797752809
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 71.5083798883
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 73.1182795699
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.9213483146
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 70.9497206704
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.9213483146
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 69.2307692308
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 77.0491803279
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.797752809
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 71.5083798883
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 69.3548387097
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.6741573034
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 70.9497206704
-			Accuracy on test : 0.0
-			Accuracy on validation : 64.0449438202
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 69.7802197802
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 54.0983606557
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 54.1899441341
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 54.3010752688
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 53.0726256983
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 54.3956043956
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-2016-09-06 08:14:02,984 INFO: Done:	 Result Analysis
-2016-09-06 08:14:03,053 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:03,053 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:03,054 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:03,054 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:03,055 INFO: Info:	 Shape of View0 :(300, 17)
-2016-09-06 08:14:03,055 INFO: Info:	 Shape of View0 :(300, 17)
-2016-09-06 08:14:03,055 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:14:03,056 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:14:03,056 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 08:14:03,057 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 08:14:03,057 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:14:03,057 INFO: Done:	 Read Database Files
-2016-09-06 08:14:03,057 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:14:03,058 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:14:03,058 INFO: Done:	 Read Database Files
-2016-09-06 08:14:03,058 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:14:03,066 INFO: Done:	 Determine validation split
-2016-09-06 08:14:03,066 INFO: Done:	 Determine validation split
-2016-09-06 08:14:03,066 INFO: Start:	 Determine 5 folds
-2016-09-06 08:14:03,066 INFO: Start:	 Determine 5 folds
-2016-09-06 08:14:03,076 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:14:03,076 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:14:03,077 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:14:03,077 INFO: Done:	 Determine folds
-2016-09-06 08:14:03,077 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:14:03,077 INFO: Start:	 Classification
-2016-09-06 08:14:03,077 INFO: 	Start:	 Fold number 1
-2016-09-06 08:14:03,077 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:14:03,078 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:14:03,078 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:14:03,078 INFO: Done:	 Determine folds
-2016-09-06 08:14:03,078 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:14:03,078 INFO: Start:	 Classification
-2016-09-06 08:14:03,078 INFO: 	Start:	 Fold number 1
-2016-09-06 08:14:03,125 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,155 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,192 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:14:03,193 INFO: 	Start:	 Fold number 2
-2016-09-06 08:14:03,209 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:14:03,209 INFO: 	Start:	 Fold number 2
-2016-09-06 08:14:03,236 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,242 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,280 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:14:03,280 INFO: 	Start:	 Fold number 3
-2016-09-06 08:14:03,314 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:14:03,314 INFO: 	Start:	 Fold number 3
-2016-09-06 08:14:03,329 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,340 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,366 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:14:03,366 INFO: 	Start:	 Fold number 4
-2016-09-06 08:14:03,415 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,419 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:14:03,419 INFO: 	Start:	 Fold number 4
-2016-09-06 08:14:03,445 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,451 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:14:03,451 INFO: 	Start:	 Fold number 5
-2016-09-06 08:14:03,500 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,523 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:14:03,523 INFO: 	Start:	 Fold number 5
-2016-09-06 08:14:03,537 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:14:03,538 INFO: Done:	 Classification
-2016-09-06 08:14:03,538 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:14:03,538 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:14:03,542 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 60.1962236658
-	-On Test : 55.6097560976
-	-On Validation : 59.3258426966
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- K nearest Neighbors with  n_neighbors: 43
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:14:03,542 INFO: Done:	 Result Analysis
-2016-09-06 08:14:03,550 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,626 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:14:03,626 INFO: Done:	 Classification
-2016-09-06 08:14:03,626 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:14:03,626 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:14:03,630 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 54.0717294888
-	-On Test : 53.6585365854
-	-On Validation : 53.9325842697
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- K nearest Neighbors with  n_neighbors: 43
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:14:03,630 INFO: Done:	 Result Analysis
-2016-09-06 08:14:03,701 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:03,701 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:03,701 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:03,701 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:03,701 INFO: Info:	 Shape of View0 :(300, 17)
-2016-09-06 08:14:03,701 INFO: Info:	 Shape of View0 :(300, 17)
-2016-09-06 08:14:03,702 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:14:03,702 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:14:03,702 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 08:14:03,702 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 08:14:03,703 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:14:03,703 INFO: Done:	 Read Database Files
-2016-09-06 08:14:03,703 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:14:03,703 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:14:03,703 INFO: Done:	 Read Database Files
-2016-09-06 08:14:03,703 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:14:03,707 INFO: Done:	 Determine validation split
-2016-09-06 08:14:03,708 INFO: Start:	 Determine 5 folds
-2016-09-06 08:14:03,708 INFO: Done:	 Determine validation split
-2016-09-06 08:14:03,708 INFO: Start:	 Determine 5 folds
-2016-09-06 08:14:03,715 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:14:03,716 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:14:03,716 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:14:03,716 INFO: Done:	 Determine folds
-2016-09-06 08:14:03,716 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:14:03,716 INFO: Start:	 Classification
-2016-09-06 08:14:03,716 INFO: 	Start:	 Fold number 1
-2016-09-06 08:14:03,716 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:14:03,717 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:14:03,717 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:14:03,717 INFO: Done:	 Determine folds
-2016-09-06 08:14:03,717 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:14:03,717 INFO: Start:	 Classification
-2016-09-06 08:14:03,717 INFO: 	Start:	 Fold number 1
-2016-09-06 08:14:03,742 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,747 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,767 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:14:03,768 INFO: 	Start:	 Fold number 2
-2016-09-06 08:14:03,792 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,800 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:14:03,800 INFO: 	Start:	 Fold number 2
-2016-09-06 08:14:03,817 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:14:03,817 INFO: 	Start:	 Fold number 3
-2016-09-06 08:14:03,828 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,841 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,866 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:14:03,866 INFO: 	Start:	 Fold number 4
-2016-09-06 08:14:03,870 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:14:03,870 INFO: 	Start:	 Fold number 3
-2016-09-06 08:14:03,890 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,901 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,915 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:14:03,915 INFO: 	Start:	 Fold number 5
-2016-09-06 08:14:03,938 INFO: 	Start: 	 Classification
-2016-09-06 08:14:03,942 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:14:03,942 INFO: 	Start:	 Fold number 4
-2016-09-06 08:14:03,964 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:14:03,964 INFO: Done:	 Classification
-2016-09-06 08:14:03,964 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:14:03,965 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:14:03,969 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 47.3170731707
-	-On Validation : 83.8202247191
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:14:03,970 INFO: Done:	 Result Analysis
-2016-09-06 08:14:03,970 INFO: 	Start: 	 Classification
-2016-09-06 08:14:04,005 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:14:04,006 INFO: 	Start:	 Fold number 5
-2016-09-06 08:14:04,030 INFO: 	Start: 	 Classification
-2016-09-06 08:14:04,066 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:14:04,066 INFO: Done:	 Classification
-2016-09-06 08:14:04,066 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:14:04,066 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:14:04,071 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 18.5734395421
-	-On Test : 16.5853658537
-	-On Validation : 18.4269662921
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- K nearest Neighbors with  n_neighbors: 43
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:14:04,071 INFO: Done:	 Result Analysis
-2016-09-06 08:14:04,151 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:04,151 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:04,151 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:04,152 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:04,152 INFO: Info:	 Shape of View0 :(300, 17)
-2016-09-06 08:14:04,152 INFO: Info:	 Shape of View0 :(300, 17)
-2016-09-06 08:14:04,153 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:14:04,153 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:14:04,153 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 08:14:04,153 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 08:14:04,154 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:14:04,154 INFO: Done:	 Read Database Files
-2016-09-06 08:14:04,154 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:14:04,154 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 08:14:04,154 INFO: Done:	 Read Database Files
-2016-09-06 08:14:04,154 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:14:04,158 INFO: Done:	 Determine validation split
-2016-09-06 08:14:04,158 INFO: Start:	 Determine 5 folds
-2016-09-06 08:14:04,159 INFO: Done:	 Determine validation split
-2016-09-06 08:14:04,159 INFO: Start:	 Determine 5 folds
-2016-09-06 08:14:04,164 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:14:04,164 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:14:04,164 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:14:04,164 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:14:04,164 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:14:04,164 INFO: Done:	 Determine folds
-2016-09-06 08:14:04,164 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:14:04,165 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:14:04,165 INFO: Done:	 Determine folds
-2016-09-06 08:14:04,165 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:14:04,165 INFO: Start:	 Classification
-2016-09-06 08:14:04,165 INFO: Start:	 Classification
-2016-09-06 08:14:04,165 INFO: 	Start:	 Fold number 1
-2016-09-06 08:14:04,165 INFO: 	Start:	 Fold number 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cfed7f4e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6c7c657d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8b525565..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 32
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index df07d806..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 11, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 59196751..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ee8523e8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2929de7b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 67ffaac5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2e842819..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b1177ee7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 11, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ab54ec5a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5781
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 187a9c3e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 76bbebd8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ae987995..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 15b2572e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fc434994..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 11, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b0df0939..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 63a4473e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f4d2a567..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9ac33236..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 243793e9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3861a374..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index aaa776bf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e03e18e0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.655555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.655555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5c76abd8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.457142857143
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b21c5aae..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8b562853..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 553d0927..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 11, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.980952380952
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7686d0da..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9a50b87f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5883
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081356Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081356Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 33353e5d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081356Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 59.2121406739
-	-On Test : 52.6829268293
-	-On Validation : 57.7528089888
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- K nearest Neighbors with  n_neighbors: 43
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081402Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-081402Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081402Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081402Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 4cc4e759..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081402Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,209 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 71.7284678753
-	-On Test : 54.1463414634
-	-On Validation : 64.0449438202Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 17), View1 of shape (300, 12), View2 of shape (300, 8), View3 of shape (300, 20)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:01        0:00:00
-	         Fold 3        0:00:02        0:00:00
-	         Fold 4        0:00:03        0:00:00
-	         Fold 5        0:00:04        0:00:00
-	          Total        0:00:13        0:00:01
-	So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.2
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.182513661202
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.189071038251
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.177049180328
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.183798882682
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.187150837989
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.197206703911
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.18938547486
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.19247311828
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.174193548387
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.193548387097
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.197849462366
-			- Percentage of time chosen : 0.2
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.194972067039
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.175977653631
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.184916201117
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.182681564246
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.184065934066
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.173076923077
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.180769230769
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.177472527473
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 54.0983606557
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 54.1899441341
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 54.3010752688
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 53.0726256983
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 54.3956043956
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 77.0491803279
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.797752809
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 71.5083798883
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 73.1182795699
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.9213483146
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 70.9497206704
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.9213483146
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 69.2307692308
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 77.0491803279
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.797752809
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 71.5083798883
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 69.3548387097
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.6741573034
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 70.9497206704
-			Accuracy on test : 0.0
-			Accuracy on validation : 64.0449438202
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 69.7802197802
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 54.0983606557
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 54.1899441341
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 54.3010752688
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 53.0726256983
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 54.3956043956
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index b4b3727b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 47.3170731707
-	-On Validation : 83.8202247191
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3b8c97f4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 54.0717294888
-	-On Test : 53.6585365854
-	-On Validation : 53.9325842697
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- K nearest Neighbors with  n_neighbors: 43
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 42ae0b9c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 60.1962236658
-	-On Test : 55.6097560976
-	-On Validation : 59.3258426966
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- K nearest Neighbors with  n_neighbors: 43
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081404Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081404Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 71d4c5b3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081404Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 18.5734395421
-	-On Test : 16.5853658537
-	-On Validation : 18.4269662921
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- K nearest Neighbors with  n_neighbors: 43
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081613-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-081613-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 0eb32752..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081613-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1977 +0,0 @@
-2016-09-06 08:16:13,564 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:16:13,564 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00013571875 Gbytes /!\ 
-2016-09-06 08:16:18,576 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 08:16:18,578 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 08:16:18,632 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:18,632 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:18,632 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:16:18,632 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:16:18,632 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:18,632 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:18,633 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:18,633 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:18,633 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:18,633 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:18,633 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:18,633 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:18,634 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:18,634 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:18,695 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:18,695 DEBUG: Start:	 Training
-2016-09-06 08:16:18,697 DEBUG: Info:	 Time for Training: 0.0656869411469[s]
-2016-09-06 08:16:18,697 DEBUG: Done:	 Training
-2016-09-06 08:16:18,697 DEBUG: Start:	 Predicting
-2016-09-06 08:16:18,700 DEBUG: Done:	 Predicting
-2016-09-06 08:16:18,700 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:18,701 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:18,701 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:16:18,701 INFO: Done:	 Result Analysis
-2016-09-06 08:16:18,732 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:18,732 DEBUG: Start:	 Training
-2016-09-06 08:16:18,736 DEBUG: Info:	 Time for Training: 0.104928970337[s]
-2016-09-06 08:16:18,736 DEBUG: Done:	 Training
-2016-09-06 08:16:18,736 DEBUG: Start:	 Predicting
-2016-09-06 08:16:18,739 DEBUG: Done:	 Predicting
-2016-09-06 08:16:18,739 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:18,741 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:18,741 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:16:18,741 INFO: Done:	 Result Analysis
-2016-09-06 08:16:18,884 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:18,884 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:18,884 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:16:18,884 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:16:18,884 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:18,884 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:18,885 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:18,885 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:18,885 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:18,885 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:18,886 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:18,886 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:18,886 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:18,886 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:18,971 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:18,971 DEBUG: Start:	 Training
-2016-09-06 08:16:18,972 DEBUG: Info:	 Time for Training: 0.0888061523438[s]
-2016-09-06 08:16:18,972 DEBUG: Done:	 Training
-2016-09-06 08:16:18,972 DEBUG: Start:	 Predicting
-2016-09-06 08:16:18,981 DEBUG: Done:	 Predicting
-2016-09-06 08:16:18,981 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:18,983 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:18,983 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.628571428571
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:16:18,983 INFO: Done:	 Result Analysis
-2016-09-06 08:16:19,664 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:19,664 DEBUG: Start:	 Training
-2016-09-06 08:16:19,737 DEBUG: Info:	 Time for Training: 0.853468894958[s]
-2016-09-06 08:16:19,737 DEBUG: Done:	 Training
-2016-09-06 08:16:19,737 DEBUG: Start:	 Predicting
-2016-09-06 08:16:19,745 DEBUG: Done:	 Predicting
-2016-09-06 08:16:19,745 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:19,746 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:19,746 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 28, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:16:19,746 INFO: Done:	 Result Analysis
-2016-09-06 08:16:19,839 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:19,839 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:19,840 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:16:19,840 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:16:19,840 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:19,840 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:19,841 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:19,841 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:19,841 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:19,841 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:19,841 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:19,841 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:19,841 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:19,841 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:19,925 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:19,925 DEBUG: Start:	 Training
-2016-09-06 08:16:19,926 DEBUG: Info:	 Time for Training: 0.0877830982208[s]
-2016-09-06 08:16:19,926 DEBUG: Done:	 Training
-2016-09-06 08:16:19,926 DEBUG: Start:	 Predicting
-2016-09-06 08:16:19,929 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:19,929 DEBUG: Start:	 Training
-2016-09-06 08:16:19,941 DEBUG: Done:	 Predicting
-2016-09-06 08:16:19,942 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:19,943 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:19,943 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:19,943 INFO: Done:	 Result Analysis
-2016-09-06 08:16:19,952 DEBUG: Info:	 Time for Training: 0.113765001297[s]
-2016-09-06 08:16:19,952 DEBUG: Done:	 Training
-2016-09-06 08:16:19,952 DEBUG: Start:	 Predicting
-2016-09-06 08:16:19,956 DEBUG: Done:	 Predicting
-2016-09-06 08:16:19,956 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:19,957 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:19,957 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.666666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.666666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:16:19,957 INFO: Done:	 Result Analysis
-2016-09-06 08:16:20,087 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,088 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:16:20,088 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:20,089 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,089 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:20,089 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:20,089 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:16:20,089 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:20,090 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:20,090 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,091 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:20,091 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:20,092 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:20,092 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,180 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:20,180 DEBUG: Start:	 Training
-2016-09-06 08:16:20,183 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:20,183 DEBUG: Start:	 Training
-2016-09-06 08:16:20,200 DEBUG: Info:	 Time for Training: 0.113780021667[s]
-2016-09-06 08:16:20,200 DEBUG: Done:	 Training
-2016-09-06 08:16:20,200 DEBUG: Start:	 Predicting
-2016-09-06 08:16:20,202 DEBUG: Info:	 Time for Training: 0.114318132401[s]
-2016-09-06 08:16:20,202 DEBUG: Done:	 Training
-2016-09-06 08:16:20,202 DEBUG: Start:	 Predicting
-2016-09-06 08:16:20,204 DEBUG: Done:	 Predicting
-2016-09-06 08:16:20,205 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:20,206 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:20,206 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:16:20,206 INFO: Done:	 Result Analysis
-2016-09-06 08:16:20,208 DEBUG: Done:	 Predicting
-2016-09-06 08:16:20,209 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:20,210 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:20,210 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:20,211 INFO: Done:	 Result Analysis
-2016-09-06 08:16:20,333 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,333 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:16:20,334 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:20,334 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:20,334 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:20,334 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,335 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:20,335 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:16:20,335 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:20,335 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,335 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:20,335 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:20,335 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:20,336 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,393 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:20,393 DEBUG: Start:	 Training
-2016-09-06 08:16:20,394 DEBUG: Info:	 Time for Training: 0.0608160495758[s]
-2016-09-06 08:16:20,395 DEBUG: Done:	 Training
-2016-09-06 08:16:20,395 DEBUG: Start:	 Predicting
-2016-09-06 08:16:20,397 DEBUG: Done:	 Predicting
-2016-09-06 08:16:20,397 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:20,398 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:20,398 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:16:20,399 INFO: Done:	 Result Analysis
-2016-09-06 08:16:20,418 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:20,419 DEBUG: Start:	 Training
-2016-09-06 08:16:20,423 DEBUG: Info:	 Time for Training: 0.0899488925934[s]
-2016-09-06 08:16:20,423 DEBUG: Done:	 Training
-2016-09-06 08:16:20,423 DEBUG: Start:	 Predicting
-2016-09-06 08:16:20,426 DEBUG: Done:	 Predicting
-2016-09-06 08:16:20,426 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:20,427 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:20,427 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:16:20,428 INFO: Done:	 Result Analysis
-2016-09-06 08:16:20,583 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,583 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,584 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:16:20,584 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:16:20,584 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:20,584 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:20,585 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:20,585 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:20,585 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:20,585 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:20,585 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:20,585 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:20,585 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,585 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,670 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:20,670 DEBUG: Start:	 Training
-2016-09-06 08:16:20,671 DEBUG: Info:	 Time for Training: 0.0880181789398[s]
-2016-09-06 08:16:20,671 DEBUG: Done:	 Training
-2016-09-06 08:16:20,671 DEBUG: Start:	 Predicting
-2016-09-06 08:16:20,681 DEBUG: Done:	 Predicting
-2016-09-06 08:16:20,681 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:20,682 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:20,683 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:16:20,683 INFO: Done:	 Result Analysis
-2016-09-06 08:16:21,366 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:21,366 DEBUG: Start:	 Training
-2016-09-06 08:16:21,439 DEBUG: Info:	 Time for Training: 0.856797218323[s]
-2016-09-06 08:16:21,440 DEBUG: Done:	 Training
-2016-09-06 08:16:21,440 DEBUG: Start:	 Predicting
-2016-09-06 08:16:21,448 DEBUG: Done:	 Predicting
-2016-09-06 08:16:21,448 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:21,449 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:21,449 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 28, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:16:21,449 INFO: Done:	 Result Analysis
-2016-09-06 08:16:21,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:21,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:21,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:16:21,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:16:21,536 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:21,536 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:21,537 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:21,537 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:21,537 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:21,537 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:21,537 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:21,537 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:21,537 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:21,537 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:21,614 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:21,614 DEBUG: Start:	 Training
-2016-09-06 08:16:21,615 DEBUG: Info:	 Time for Training: 0.0793581008911[s]
-2016-09-06 08:16:21,615 DEBUG: Done:	 Training
-2016-09-06 08:16:21,615 DEBUG: Start:	 Predicting
-2016-09-06 08:16:21,624 DEBUG: Done:	 Predicting
-2016-09-06 08:16:21,625 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:21,626 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:21,626 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:21,626 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:21,626 DEBUG: Start:	 Training
-2016-09-06 08:16:21,626 INFO: Done:	 Result Analysis
-2016-09-06 08:16:21,644 DEBUG: Info:	 Time for Training: 0.108808994293[s]
-2016-09-06 08:16:21,644 DEBUG: Done:	 Training
-2016-09-06 08:16:21,644 DEBUG: Start:	 Predicting
-2016-09-06 08:16:21,648 DEBUG: Done:	 Predicting
-2016-09-06 08:16:21,648 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:21,649 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:21,649 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:16:21,649 INFO: Done:	 Result Analysis
-2016-09-06 08:16:21,786 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:21,786 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:21,787 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:16:21,787 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:16:21,787 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:21,787 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:21,788 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:21,788 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:21,788 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:21,788 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:21,788 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:21,788 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:21,788 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:21,788 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:21,907 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:21,907 DEBUG: Start:	 Training
-2016-09-06 08:16:21,919 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:21,919 DEBUG: Start:	 Training
-2016-09-06 08:16:21,931 DEBUG: Info:	 Time for Training: 0.145541191101[s]
-2016-09-06 08:16:21,931 DEBUG: Done:	 Training
-2016-09-06 08:16:21,932 DEBUG: Start:	 Predicting
-2016-09-06 08:16:21,939 DEBUG: Done:	 Predicting
-2016-09-06 08:16:21,939 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:21,940 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:21,940 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:16:21,941 INFO: Done:	 Result Analysis
-2016-09-06 08:16:21,946 DEBUG: Info:	 Time for Training: 0.159810066223[s]
-2016-09-06 08:16:21,946 DEBUG: Done:	 Training
-2016-09-06 08:16:21,946 DEBUG: Start:	 Predicting
-2016-09-06 08:16:21,949 DEBUG: Done:	 Predicting
-2016-09-06 08:16:21,950 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:21,951 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:21,951 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3904
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:16:21,951 INFO: Done:	 Result Analysis
-2016-09-06 08:16:22,039 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:22,039 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:22,039 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:16:22,039 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:16:22,039 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:22,039 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:22,040 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:22,040 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:22,040 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:22,040 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:22,040 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:22,040 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:22,041 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:22,041 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:22,131 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:22,131 DEBUG: Start:	 Training
-2016-09-06 08:16:22,134 DEBUG: Info:	 Time for Training: 0.0960428714752[s]
-2016-09-06 08:16:22,134 DEBUG: Done:	 Training
-2016-09-06 08:16:22,134 DEBUG: Start:	 Predicting
-2016-09-06 08:16:22,137 DEBUG: Done:	 Predicting
-2016-09-06 08:16:22,138 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:22,139 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:22,139 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:22,139 INFO: Done:	 Result Analysis
-2016-09-06 08:16:22,160 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:22,160 DEBUG: Start:	 Training
-2016-09-06 08:16:22,164 DEBUG: Info:	 Time for Training: 0.126334905624[s]
-2016-09-06 08:16:22,164 DEBUG: Done:	 Training
-2016-09-06 08:16:22,164 DEBUG: Start:	 Predicting
-2016-09-06 08:16:22,167 DEBUG: Done:	 Predicting
-2016-09-06 08:16:22,167 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:22,169 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:22,169 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:22,169 INFO: Done:	 Result Analysis
-2016-09-06 08:16:22,283 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:22,283 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:22,283 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:16:22,283 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:16:22,283 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:22,283 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:22,284 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:22,284 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:22,284 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:22,284 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:22,284 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:22,284 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:22,284 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:22,284 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:22,339 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:22,339 DEBUG: Start:	 Training
-2016-09-06 08:16:22,340 DEBUG: Info:	 Time for Training: 0.0575180053711[s]
-2016-09-06 08:16:22,340 DEBUG: Done:	 Training
-2016-09-06 08:16:22,340 DEBUG: Start:	 Predicting
-2016-09-06 08:16:22,346 DEBUG: Done:	 Predicting
-2016-09-06 08:16:22,346 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:22,347 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:22,347 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:16:22,348 INFO: Done:	 Result Analysis
-2016-09-06 08:16:23,030 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:23,031 DEBUG: Start:	 Training
-2016-09-06 08:16:23,104 DEBUG: Info:	 Time for Training: 0.82169508934[s]
-2016-09-06 08:16:23,104 DEBUG: Done:	 Training
-2016-09-06 08:16:23,104 DEBUG: Start:	 Predicting
-2016-09-06 08:16:23,112 DEBUG: Done:	 Predicting
-2016-09-06 08:16:23,112 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:23,113 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:23,113 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 28, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,113 INFO: Done:	 Result Analysis
-2016-09-06 08:16:23,231 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,231 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,232 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:16:23,232 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:16:23,232 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:23,232 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:23,232 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:23,232 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:23,233 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:23,233 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:23,233 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:23,233 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:23,233 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,233 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,309 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:23,309 DEBUG: Start:	 Training
-2016-09-06 08:16:23,310 DEBUG: Info:	 Time for Training: 0.0788888931274[s]
-2016-09-06 08:16:23,310 DEBUG: Done:	 Training
-2016-09-06 08:16:23,310 DEBUG: Start:	 Predicting
-2016-09-06 08:16:23,317 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:23,318 DEBUG: Start:	 Training
-2016-09-06 08:16:23,336 DEBUG: Info:	 Time for Training: 0.10564994812[s]
-2016-09-06 08:16:23,337 DEBUG: Done:	 Training
-2016-09-06 08:16:23,337 DEBUG: Start:	 Predicting
-2016-09-06 08:16:23,337 DEBUG: Done:	 Predicting
-2016-09-06 08:16:23,337 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:23,339 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:23,339 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,339 INFO: Done:	 Result Analysis
-2016-09-06 08:16:23,340 DEBUG: Done:	 Predicting
-2016-09-06 08:16:23,340 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:23,341 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:23,341 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,342 INFO: Done:	 Result Analysis
-2016-09-06 08:16:23,491 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,491 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,491 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:16:23,491 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:16:23,491 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:23,491 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:23,492 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:23,492 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:23,492 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:23,492 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:23,493 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:23,493 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:23,493 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,493 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,613 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:23,613 DEBUG: Start:	 Training
-2016-09-06 08:16:23,618 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:23,618 DEBUG: Start:	 Training
-2016-09-06 08:16:23,633 DEBUG: Info:	 Time for Training: 0.142898797989[s]
-2016-09-06 08:16:23,633 DEBUG: Done:	 Training
-2016-09-06 08:16:23,633 DEBUG: Start:	 Predicting
-2016-09-06 08:16:23,636 DEBUG: Info:	 Time for Training: 0.146584033966[s]
-2016-09-06 08:16:23,637 DEBUG: Done:	 Training
-2016-09-06 08:16:23,637 DEBUG: Start:	 Predicting
-2016-09-06 08:16:23,639 DEBUG: Done:	 Predicting
-2016-09-06 08:16:23,639 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:23,640 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:23,640 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,640 DEBUG: Done:	 Predicting
-2016-09-06 08:16:23,641 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:23,641 INFO: Done:	 Result Analysis
-2016-09-06 08:16:23,642 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:23,642 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,642 INFO: Done:	 Result Analysis
-2016-09-06 08:16:23,739 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,739 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,739 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:16:23,739 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:16:23,740 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:23,740 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:23,741 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:23,741 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:23,741 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:23,741 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:23,741 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:23,741 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:23,741 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,741 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,805 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:23,806 DEBUG: Start:	 Training
-2016-09-06 08:16:23,808 DEBUG: Info:	 Time for Training: 0.0693278312683[s]
-2016-09-06 08:16:23,808 DEBUG: Done:	 Training
-2016-09-06 08:16:23,808 DEBUG: Start:	 Predicting
-2016-09-06 08:16:23,810 DEBUG: Done:	 Predicting
-2016-09-06 08:16:23,810 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:23,811 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:23,811 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,812 INFO: Done:	 Result Analysis
-2016-09-06 08:16:23,831 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:23,831 DEBUG: Start:	 Training
-2016-09-06 08:16:23,835 DEBUG: Info:	 Time for Training: 0.0968770980835[s]
-2016-09-06 08:16:23,835 DEBUG: Done:	 Training
-2016-09-06 08:16:23,835 DEBUG: Start:	 Predicting
-2016-09-06 08:16:23,838 DEBUG: Done:	 Predicting
-2016-09-06 08:16:23,838 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:23,840 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:23,840 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,840 INFO: Done:	 Result Analysis
-2016-09-06 08:16:23,986 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,986 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,986 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:16:23,986 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:16:23,987 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:23,987 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:23,987 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:23,988 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:23,988 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:23,988 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:23,988 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:23,988 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,988 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:23,988 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:24,042 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:24,043 DEBUG: Start:	 Training
-2016-09-06 08:16:24,043 DEBUG: Info:	 Time for Training: 0.0575971603394[s]
-2016-09-06 08:16:24,043 DEBUG: Done:	 Training
-2016-09-06 08:16:24,043 DEBUG: Start:	 Predicting
-2016-09-06 08:16:24,051 DEBUG: Done:	 Predicting
-2016-09-06 08:16:24,051 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:24,052 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:24,052 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:16:24,052 INFO: Done:	 Result Analysis
-2016-09-06 08:16:24,711 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:24,711 DEBUG: Start:	 Training
-2016-09-06 08:16:24,761 DEBUG: Info:	 Time for Training: 0.775108098984[s]
-2016-09-06 08:16:24,761 DEBUG: Done:	 Training
-2016-09-06 08:16:24,761 DEBUG: Start:	 Predicting
-2016-09-06 08:16:24,767 DEBUG: Done:	 Predicting
-2016-09-06 08:16:24,767 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:24,769 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:24,769 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 19, max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:16:24,769 INFO: Done:	 Result Analysis
-2016-09-06 08:16:24,838 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:24,838 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:24,838 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:16:24,838 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:16:24,839 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:24,839 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:16:24,840 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:24,840 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:24,840 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:24,840 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:24,840 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:24,840 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:16:24,840 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:24,840 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:16:24,916 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:24,916 DEBUG: Start:	 Training
-2016-09-06 08:16:24,917 DEBUG: Info:	 Time for Training: 0.0791480541229[s]
-2016-09-06 08:16:24,917 DEBUG: Done:	 Training
-2016-09-06 08:16:24,917 DEBUG: Start:	 Predicting
-2016-09-06 08:16:24,924 DEBUG: Done:	 Predicting
-2016-09-06 08:16:24,924 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:24,925 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:24,926 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:24,926 INFO: Done:	 Result Analysis
-2016-09-06 08:16:24,928 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:16:24,928 DEBUG: Start:	 Training
-2016-09-06 08:16:24,948 DEBUG: Info:	 Time for Training: 0.11057305336[s]
-2016-09-06 08:16:24,948 DEBUG: Done:	 Training
-2016-09-06 08:16:24,948 DEBUG: Start:	 Predicting
-2016-09-06 08:16:24,952 DEBUG: Done:	 Predicting
-2016-09-06 08:16:24,952 DEBUG: Start:	 Getting Results
-2016-09-06 08:16:24,953 DEBUG: Done:	 Getting Results
-2016-09-06 08:16:24,953 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:16:24,953 INFO: Done:	 Result Analysis
-2016-09-06 08:16:25,237 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:25,238 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:16:25,239 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:25,239 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:25,239 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:16:25,240 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:16:25,240 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:16:25,241 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:16:25,241 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 08:16:25,242 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 08:16:25,242 INFO: Info:	 Shape of View3 :(300, 15)
-2016-09-06 08:16:25,242 INFO: Done:	 Read Database Files
-2016-09-06 08:16:25,242 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:16:25,243 INFO: Info:	 Shape of View3 :(300, 15)
-2016-09-06 08:16:25,243 INFO: Done:	 Read Database Files
-2016-09-06 08:16:25,243 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:16:25,248 INFO: Done:	 Determine validation split
-2016-09-06 08:16:25,248 INFO: Start:	 Determine 5 folds
-2016-09-06 08:16:25,249 INFO: Done:	 Determine validation split
-2016-09-06 08:16:25,249 INFO: Start:	 Determine 5 folds
-2016-09-06 08:16:25,262 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:16:25,263 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:16:25,263 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:16:25,263 INFO: Done:	 Determine folds
-2016-09-06 08:16:25,263 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 08:16:25,263 INFO: Start:	 Classification
-2016-09-06 08:16:25,263 INFO: 	Start:	 Fold number 1
-2016-09-06 08:16:25,266 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:16:25,266 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:16:25,267 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:16:25,267 INFO: Done:	 Determine folds
-2016-09-06 08:16:25,267 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:16:25,267 INFO: Start:	 Classification
-2016-09-06 08:16:25,267 INFO: 	Start:	 Fold number 1
-2016-09-06 08:16:25,304 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:16:25,312 DEBUG: 			View 0 : 0.576086956522
-2016-09-06 08:16:25,322 DEBUG: 			View 1 : 0.467391304348
-2016-09-06 08:16:25,329 INFO: 	Start: 	 Classification
-2016-09-06 08:16:25,329 DEBUG: 			View 2 : 0.505434782609
-2016-09-06 08:16:25,339 DEBUG: 			View 3 : 0.538043478261
-2016-09-06 08:16:25,378 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:16:25,378 INFO: 	Start:	 Fold number 2
-2016-09-06 08:16:25,379 DEBUG: 			 Best view : 		View1
-2016-09-06 08:16:25,433 INFO: 	Start: 	 Classification
-2016-09-06 08:16:25,464 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:16:25,464 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:16:25,465 INFO: 	Start:	 Fold number 3
-2016-09-06 08:16:25,473 DEBUG: 			View 0 : 0.673913043478
-2016-09-06 08:16:25,480 DEBUG: 			View 1 : 0.690217391304
-2016-09-06 08:16:25,487 DEBUG: 			View 2 : 0.701086956522
-2016-09-06 08:16:25,496 DEBUG: 			View 3 : 0.70652173913
-2016-09-06 08:16:25,522 INFO: 	Start: 	 Classification
-2016-09-06 08:16:25,536 DEBUG: 			 Best view : 		View3
-2016-09-06 08:16:25,559 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:16:25,559 INFO: 	Start:	 Fold number 4
-2016-09-06 08:16:25,619 INFO: 	Start: 	 Classification
-2016-09-06 08:16:25,654 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:16:25,654 INFO: 	Start:	 Fold number 5
-2016-09-06 08:16:25,711 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:16:25,719 DEBUG: 			View 0 : 0.673913043478
-2016-09-06 08:16:25,719 INFO: 	Start: 	 Classification
-2016-09-06 08:16:25,726 DEBUG: 			View 1 : 0.690217391304
-2016-09-06 08:16:25,734 DEBUG: 			View 2 : 0.701086956522
-2016-09-06 08:16:25,741 DEBUG: 			View 3 : 0.70652173913
-2016-09-06 08:16:25,750 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:16:25,750 INFO: Done:	 Classification
-2016-09-06 08:16:25,750 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:16:25,750 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:16:25,755 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 55.6119923167
-	-On Test : 57.1428571429
-	-On Validation : 57.0786516854
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- SVM Linear with C : 4160
-		- SVM Linear with C : 4160
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:16:25,755 INFO: Done:	 Result Analysis
-2016-09-06 08:16:25,784 DEBUG: 			 Best view : 		View3
-2016-09-06 08:16:26,004 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:16:26,011 DEBUG: 			View 0 : 0.630434782609
-2016-09-06 08:16:26,018 DEBUG: 			View 1 : 0.619565217391
-2016-09-06 08:16:26,026 DEBUG: 			View 2 : 0.684782608696
-2016-09-06 08:16:26,033 DEBUG: 			View 3 : 0.673913043478
-2016-09-06 08:16:26,077 DEBUG: 			 Best view : 		View3
-2016-09-06 08:16:26,365 INFO: 	Start: 	 Classification
-2016-09-06 08:16:26,840 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:16:26,841 INFO: 	Start:	 Fold number 2
-2016-09-06 08:16:26,870 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:16:26,877 DEBUG: 			View 0 : 0.533707865169
-2016-09-06 08:16:26,884 DEBUG: 			View 1 : 0.556179775281
-2016-09-06 08:16:26,891 DEBUG: 			View 2 : 0.421348314607
-2016-09-06 08:16:26,898 DEBUG: 			View 3 : 0.578651685393
-2016-09-06 08:16:26,929 DEBUG: 			 Best view : 		View0
-2016-09-06 08:16:27,008 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:16:27,015 DEBUG: 			View 0 : 0.707865168539
-2016-09-06 08:16:27,022 DEBUG: 			View 1 : 0.691011235955
-2016-09-06 08:16:27,029 DEBUG: 			View 2 : 0.691011235955
-2016-09-06 08:16:27,036 DEBUG: 			View 3 : 0.651685393258
-2016-09-06 08:16:27,074 DEBUG: 			 Best view : 		View0
-2016-09-06 08:16:27,219 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:16:27,226 DEBUG: 			View 0 : 0.707865168539
-2016-09-06 08:16:27,234 DEBUG: 			View 1 : 0.685393258427
-2016-09-06 08:16:27,240 DEBUG: 			View 2 : 0.691011235955
-2016-09-06 08:16:27,248 DEBUG: 			View 3 : 0.651685393258
-2016-09-06 08:16:27,288 DEBUG: 			 Best view : 		View1
-2016-09-06 08:16:27,501 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:16:27,509 DEBUG: 			View 0 : 0.674157303371
-2016-09-06 08:16:27,516 DEBUG: 			View 1 : 0.685393258427
-2016-09-06 08:16:27,523 DEBUG: 			View 2 : 0.634831460674
-2016-09-06 08:16:27,530 DEBUG: 			View 3 : 0.679775280899
-2016-09-06 08:16:27,573 DEBUG: 			 Best view : 		View1
-2016-09-06 08:16:27,854 INFO: 	Start: 	 Classification
-2016-09-06 08:16:28,319 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:16:28,319 INFO: 	Start:	 Fold number 3
-2016-09-06 08:16:28,348 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:16:28,355 DEBUG: 			View 0 : 0.438202247191
-2016-09-06 08:16:28,361 DEBUG: 			View 1 : 0.438202247191
-2016-09-06 08:16:28,367 DEBUG: 			View 2 : 0.438202247191
-2016-09-06 08:16:28,374 DEBUG: 			View 3 : 0.438202247191
-2016-09-06 08:16:28,374 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 08:16:28,406 DEBUG: 			 Best view : 		View0
-2016-09-06 08:16:28,483 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:16:28,491 DEBUG: 			View 0 : 0.662921348315
-2016-09-06 08:16:28,498 DEBUG: 			View 1 : 0.691011235955
-2016-09-06 08:16:28,505 DEBUG: 			View 2 : 0.64606741573
-2016-09-06 08:16:28,512 DEBUG: 			View 3 : 0.668539325843
-2016-09-06 08:16:28,549 DEBUG: 			 Best view : 		View1
-2016-09-06 08:16:28,696 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:16:28,704 DEBUG: 			View 0 : 0.662921348315
-2016-09-06 08:16:28,710 DEBUG: 			View 1 : 0.691011235955
-2016-09-06 08:16:28,718 DEBUG: 			View 2 : 0.64606741573
-2016-09-06 08:16:28,725 DEBUG: 			View 3 : 0.668539325843
-2016-09-06 08:16:28,765 DEBUG: 			 Best view : 		View1
-2016-09-06 08:16:28,979 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:16:28,986 DEBUG: 			View 0 : 0.668539325843
-2016-09-06 08:16:28,993 DEBUG: 			View 1 : 0.674157303371
-2016-09-06 08:16:29,000 DEBUG: 			View 2 : 0.634831460674
-2016-09-06 08:16:29,007 DEBUG: 			View 3 : 0.691011235955
-2016-09-06 08:16:29,050 DEBUG: 			 Best view : 		View0
-2016-09-06 08:16:29,329 INFO: 	Start: 	 Classification
-2016-09-06 08:16:29,793 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:16:29,794 INFO: 	Start:	 Fold number 4
-2016-09-06 08:16:29,823 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:16:29,830 DEBUG: 			View 0 : 0.556756756757
-2016-09-06 08:16:29,837 DEBUG: 			View 1 : 0.556756756757
-2016-09-06 08:16:29,843 DEBUG: 			View 2 : 0.556756756757
-2016-09-06 08:16:29,850 DEBUG: 			View 3 : 0.556756756757
-2016-09-06 08:16:29,883 DEBUG: 			 Best view : 		View0
-2016-09-06 08:16:29,964 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:16:29,972 DEBUG: 			View 0 : 0.718918918919
-2016-09-06 08:16:29,979 DEBUG: 			View 1 : 0.664864864865
-2016-09-06 08:16:29,986 DEBUG: 			View 2 : 0.664864864865
-2016-09-06 08:16:29,994 DEBUG: 			View 3 : 0.632432432432
-2016-09-06 08:16:30,033 DEBUG: 			 Best view : 		View0
-2016-09-06 08:16:30,184 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:16:30,192 DEBUG: 			View 0 : 0.718918918919
-2016-09-06 08:16:30,199 DEBUG: 			View 1 : 0.664864864865
-2016-09-06 08:16:30,206 DEBUG: 			View 2 : 0.664864864865
-2016-09-06 08:16:30,213 DEBUG: 			View 3 : 0.632432432432
-2016-09-06 08:16:30,254 DEBUG: 			 Best view : 		View0
-2016-09-06 08:16:30,475 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:16:30,482 DEBUG: 			View 0 : 0.637837837838
-2016-09-06 08:16:30,489 DEBUG: 			View 1 : 0.697297297297
-2016-09-06 08:16:30,497 DEBUG: 			View 2 : 0.67027027027
-2016-09-06 08:16:30,504 DEBUG: 			View 3 : 0.6
-2016-09-06 08:16:30,548 DEBUG: 			 Best view : 		View1
-2016-09-06 08:16:30,839 INFO: 	Start: 	 Classification
-2016-09-06 08:16:31,314 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:16:31,314 INFO: 	Start:	 Fold number 5
-2016-09-06 08:16:31,343 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:16:31,349 DEBUG: 			View 0 : 0.559322033898
-2016-09-06 08:16:31,356 DEBUG: 			View 1 : 0.559322033898
-2016-09-06 08:16:31,363 DEBUG: 			View 2 : 0.559322033898
-2016-09-06 08:16:31,369 DEBUG: 			View 3 : 0.559322033898
-2016-09-06 08:16:31,401 DEBUG: 			 Best view : 		View0
-2016-09-06 08:16:31,478 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:16:31,485 DEBUG: 			View 0 : 0.71186440678
-2016-09-06 08:16:31,492 DEBUG: 			View 1 : 0.677966101695
-2016-09-06 08:16:31,499 DEBUG: 			View 2 : 0.689265536723
-2016-09-06 08:16:31,507 DEBUG: 			View 3 : 0.649717514124
-2016-09-06 08:16:31,544 DEBUG: 			 Best view : 		View0
-2016-09-06 08:16:31,689 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:16:31,696 DEBUG: 			View 0 : 0.71186440678
-2016-09-06 08:16:31,703 DEBUG: 			View 1 : 0.677966101695
-2016-09-06 08:16:31,710 DEBUG: 			View 2 : 0.689265536723
-2016-09-06 08:16:31,717 DEBUG: 			View 3 : 0.649717514124
-2016-09-06 08:16:31,757 DEBUG: 			 Best view : 		View0
-2016-09-06 08:16:31,967 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:16:31,974 DEBUG: 			View 0 : 0.638418079096
-2016-09-06 08:16:31,981 DEBUG: 			View 1 : 0.610169491525
-2016-09-06 08:16:31,988 DEBUG: 			View 2 : 0.683615819209
-2016-09-06 08:16:31,995 DEBUG: 			View 3 : 0.649717514124
-2016-09-06 08:16:32,037 DEBUG: 			 Best view : 		View2
-2016-09-06 08:16:32,315 INFO: 	Start: 	 Classification
-2016-09-06 08:16:32,782 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:16:32,782 INFO: Done:	 Classification
-2016-09-06 08:16:32,783 INFO: Info:	 Time for Classification: 7[s]
-2016-09-06 08:16:32,783 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 08:16:35,270 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 70.835988937
-	-On Test : 48.5714285714
-	-On Validation : 62.0224719101Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 14), View1 of shape (300, 10), View2 of shape (300, 11), View3 of shape (300, 15)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:07        0:00:00
-	          Total        0:00:20        0:00:02
-	So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.255434782609
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.246739130435
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.259239130435
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.2625
-			- Percentage of time chosen : 0.3
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.262359550562
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.261797752809
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.243820224719
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.256179775281
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.243258426966
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.249438202247
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.236516853933
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.246629213483
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.263243243243
-			- Percentage of time chosen : 0.9
-		- On View1 : 
-			- Mean average Accuracy : 0.258378378378
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.255675675676
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.242162162162
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.262146892655
-			- Percentage of time chosen : 0.9
-		- On View1 : 
-			- Mean average Accuracy : 0.252542372881
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.262146892655
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.250847457627
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 44.0217391304
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View1
-		 Fold 2
-			Accuracy on train : 44.3820224719
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 43.8202247191
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 44.3243243243
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 44.0677966102
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 70.652173913
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 70.7865168539
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 69.1011235955
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.797752809
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 71.8918918919
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 71.186440678
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View0
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 70.652173913
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 68.5393258427
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 68.5393258427
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.797752809
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 71.8918918919
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 71.186440678
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View0
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 70.652173913
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 71.3483146067
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.808988764
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 69.1011235955
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.797752809
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 71.8918918919
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 71.186440678
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View2
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 44.0217391304
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 44.3820224719
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 43.8202247191
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 44.3243243243
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 44.0677966102
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-2016-09-06 08:16:35,456 INFO: Done:	 Result Analysis
-2016-09-06 08:16:35,608 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:35,608 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:35,608 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:35,609 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:35,609 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:16:35,609 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:16:35,609 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:16:35,610 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:16:35,610 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 08:16:35,610 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 08:16:35,610 INFO: Info:	 Shape of View3 :(300, 15)
-2016-09-06 08:16:35,611 INFO: Done:	 Read Database Files
-2016-09-06 08:16:35,611 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:16:35,611 INFO: Info:	 Shape of View3 :(300, 15)
-2016-09-06 08:16:35,611 INFO: Done:	 Read Database Files
-2016-09-06 08:16:35,611 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:16:35,616 INFO: Done:	 Determine validation split
-2016-09-06 08:16:35,616 INFO: Start:	 Determine 5 folds
-2016-09-06 08:16:35,616 INFO: Done:	 Determine validation split
-2016-09-06 08:16:35,616 INFO: Start:	 Determine 5 folds
-2016-09-06 08:16:35,626 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:16:35,626 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:16:35,626 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:16:35,626 INFO: Done:	 Determine folds
-2016-09-06 08:16:35,626 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:16:35,627 INFO: Start:	 Classification
-2016-09-06 08:16:35,627 INFO: 	Start:	 Fold number 1
-2016-09-06 08:16:35,628 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:16:35,628 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:16:35,628 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:16:35,628 INFO: Done:	 Determine folds
-2016-09-06 08:16:35,628 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:16:35,629 INFO: Start:	 Classification
-2016-09-06 08:16:35,629 INFO: 	Start:	 Fold number 1
-2016-09-06 08:16:35,695 INFO: 	Start: 	 Classification
-2016-09-06 08:16:35,731 INFO: 	Start: 	 Classification
-2016-09-06 08:16:35,762 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:16:35,762 INFO: 	Start:	 Fold number 2
-2016-09-06 08:16:35,791 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:16:35,791 INFO: 	Start:	 Fold number 2
-2016-09-06 08:16:35,835 INFO: 	Start: 	 Classification
-2016-09-06 08:16:35,851 INFO: 	Start: 	 Classification
-2016-09-06 08:16:35,865 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:16:35,865 INFO: 	Start:	 Fold number 3
-2016-09-06 08:16:35,934 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:16:35,934 INFO: 	Start:	 Fold number 3
-2016-09-06 08:16:35,939 INFO: 	Start: 	 Classification
-2016-09-06 08:16:35,971 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:16:35,972 INFO: 	Start:	 Fold number 4
-2016-09-06 08:16:35,994 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,047 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,078 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:16:36,078 INFO: 	Start:	 Fold number 5
-2016-09-06 08:16:36,078 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:16:36,078 INFO: 	Start:	 Fold number 4
-2016-09-06 08:16:36,140 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,153 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,183 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:16:36,183 INFO: Done:	 Classification
-2016-09-06 08:16:36,183 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:16:36,183 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:16:36,188 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 57.1428571429
-	-On Validation : 85.1685393258
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- SVM Linear with C : 4160
-		- SVM Linear with C : 4160
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:16:36,188 INFO: Done:	 Result Analysis
-2016-09-06 08:16:36,225 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:16:36,225 INFO: 	Start:	 Fold number 5
-2016-09-06 08:16:36,281 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,361 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:16:36,361 INFO: Done:	 Classification
-2016-09-06 08:16:36,361 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:16:36,361 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:16:36,365 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 77.2241829793
-	-On Test : 57.619047619
-	-On Validation : 68.5393258427
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- SVM Linear with C : 4160
-		- SVM Linear with C : 4160
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:16:36,366 INFO: Done:	 Result Analysis
-2016-09-06 08:16:36,455 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:36,456 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:36,456 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:36,456 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:16:36,456 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:36,457 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:16:36,457 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:16:36,457 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 08:16:36,458 INFO: Info:	 Shape of View3 :(300, 15)
-2016-09-06 08:16:36,458 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:16:36,458 INFO: Done:	 Read Database Files
-2016-09-06 08:16:36,458 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:16:36,458 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 08:16:36,459 INFO: Info:	 Shape of View3 :(300, 15)
-2016-09-06 08:16:36,459 INFO: Done:	 Read Database Files
-2016-09-06 08:16:36,459 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:16:36,462 INFO: Done:	 Determine validation split
-2016-09-06 08:16:36,462 INFO: Start:	 Determine 5 folds
-2016-09-06 08:16:36,463 INFO: Done:	 Determine validation split
-2016-09-06 08:16:36,464 INFO: Start:	 Determine 5 folds
-2016-09-06 08:16:36,470 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:16:36,470 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:16:36,470 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:16:36,470 INFO: Done:	 Determine folds
-2016-09-06 08:16:36,470 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:16:36,470 INFO: Start:	 Classification
-2016-09-06 08:16:36,470 INFO: 	Start:	 Fold number 1
-2016-09-06 08:16:36,474 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:16:36,474 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:16:36,474 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:16:36,474 INFO: Done:	 Determine folds
-2016-09-06 08:16:36,475 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:16:36,475 INFO: Start:	 Classification
-2016-09-06 08:16:36,475 INFO: 	Start:	 Fold number 1
-2016-09-06 08:16:36,495 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,520 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:16:36,521 INFO: 	Start:	 Fold number 2
-2016-09-06 08:16:36,539 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,544 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,569 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:16:36,570 INFO: 	Start:	 Fold number 3
-2016-09-06 08:16:36,610 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,611 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:16:36,611 INFO: 	Start:	 Fold number 2
-2016-09-06 08:16:36,649 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:16:36,649 INFO: 	Start:	 Fold number 4
-2016-09-06 08:16:36,680 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,688 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,705 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:16:36,705 INFO: 	Start:	 Fold number 5
-2016-09-06 08:16:36,729 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:16:36,729 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,730 INFO: 	Start:	 Fold number 3
-2016-09-06 08:16:36,755 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:16:36,755 INFO: Done:	 Classification
-2016-09-06 08:16:36,755 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:16:36,755 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:16:36,760 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 56.6666666667
-	-On Validation : 85.1685393258
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:16:36,760 INFO: Done:	 Result Analysis
-2016-09-06 08:16:36,792 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,833 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:16:36,833 INFO: 	Start:	 Fold number 4
-2016-09-06 08:16:36,888 INFO: 	Start: 	 Classification
-2016-09-06 08:16:36,923 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:16:36,923 INFO: 	Start:	 Fold number 5
-2016-09-06 08:16:36,976 INFO: 	Start: 	 Classification
-2016-09-06 08:16:37,010 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:16:37,010 INFO: Done:	 Classification
-2016-09-06 08:16:37,010 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:16:37,010 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:16:37,015 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 23.3651324521
-	-On Test : 21.4285714286
-	-On Validation : 20.0
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- SVM Linear with C : 4160
-		- SVM Linear with C : 4160
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:16:37,015 INFO: Done:	 Result Analysis
-2016-09-06 08:16:37,101 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:37,101 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:37,101 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:37,101 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:37,102 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:16:37,102 INFO: Info:	 Shape of View0 :(300, 14)
-2016-09-06 08:16:37,102 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:16:37,102 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 08:16:37,103 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 08:16:37,103 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 08:16:37,103 INFO: Info:	 Shape of View3 :(300, 15)
-2016-09-06 08:16:37,103 INFO: Info:	 Shape of View3 :(300, 15)
-2016-09-06 08:16:37,103 INFO: Done:	 Read Database Files
-2016-09-06 08:16:37,103 INFO: Done:	 Read Database Files
-2016-09-06 08:16:37,104 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:16:37,104 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:16:37,108 INFO: Done:	 Determine validation split
-2016-09-06 08:16:37,108 INFO: Done:	 Determine validation split
-2016-09-06 08:16:37,108 INFO: Start:	 Determine 5 folds
-2016-09-06 08:16:37,108 INFO: Start:	 Determine 5 folds
-2016-09-06 08:16:37,115 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:16:37,115 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:16:37,115 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:16:37,115 INFO: Done:	 Determine folds
-2016-09-06 08:16:37,115 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:16:37,116 INFO: Start:	 Classification
-2016-09-06 08:16:37,116 INFO: 	Start:	 Fold number 1
-2016-09-06 08:16:37,118 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 08:16:37,118 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:16:37,118 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:16:37,118 INFO: Done:	 Determine folds
-2016-09-06 08:16:37,118 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:16:37,119 INFO: Start:	 Classification
-2016-09-06 08:16:37,119 INFO: 	Start:	 Fold number 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index aca2f4e3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4985e158..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 661fc596..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.628571428571
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bd742e24..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 28, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7c21013c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ab82a3f3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.666666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.666666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 646cc11f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f5f0502c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c4674ee3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d22eb125..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4887e38c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a57838cc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 28, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7b171c60..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b20b752d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fb445cc2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3904
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7286019a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b892e081..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 098c6142..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fc8244e3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8e5be8ed..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fc62cd0f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fafd56e1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 28, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 24e1dd84..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2314cfaf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a9b1e75d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0ebf8cd6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a09329d1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 80d8383f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 19, max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ffa5b233..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d9839a8d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081625Results-Fusion-LateFusion-BayesianInference-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081625Results-Fusion-LateFusion-BayesianInference-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 93341e7a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081625Results-Fusion-LateFusion-BayesianInference-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 55.6119923167
-	-On Test : 57.1428571429
-	-On Validation : 57.0786516854
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- SVM Linear with C : 4160
-		- SVM Linear with C : 4160
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081635Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-081635Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081635Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081635Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index b1a0e372..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081635Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,235 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 70.835988937
-	-On Test : 48.5714285714
-	-On Validation : 62.0224719101Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 14), View1 of shape (300, 10), View2 of shape (300, 11), View3 of shape (300, 15)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:07        0:00:00
-	          Total        0:00:20        0:00:02
-	So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.255434782609
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.246739130435
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.259239130435
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.2625
-			- Percentage of time chosen : 0.3
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.262359550562
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.261797752809
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.243820224719
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.256179775281
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.243258426966
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.249438202247
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.236516853933
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.246629213483
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.263243243243
-			- Percentage of time chosen : 0.9
-		- On View1 : 
-			- Mean average Accuracy : 0.258378378378
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.255675675676
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.242162162162
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.262146892655
-			- Percentage of time chosen : 0.9
-		- On View1 : 
-			- Mean average Accuracy : 0.252542372881
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.262146892655
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.250847457627
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 44.0217391304
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View1
-		 Fold 2
-			Accuracy on train : 44.3820224719
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 43.8202247191
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 44.3243243243
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 44.0677966102
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 70.652173913
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 70.7865168539
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 69.1011235955
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.797752809
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 71.8918918919
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 71.186440678
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View0
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 70.652173913
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 68.5393258427
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 68.5393258427
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.797752809
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 71.8918918919
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 71.186440678
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View0
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 70.652173913
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 71.3483146067
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.808988764
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 69.1011235955
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.797752809
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 71.8918918919
-			Accuracy on test : 0.0
-			Accuracy on validation : 69.6629213483
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 71.186440678
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View2
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 44.0217391304
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 44.3820224719
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 43.8202247191
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 44.3243243243
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 44.0677966102
-			Accuracy on test : 0.0
-			Accuracy on validation : 42.6966292135
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 121ae6ee..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 56.6666666667
-	-On Validation : 85.1685393258
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-MajorityVoting-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-MajorityVoting-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 77d5a0f8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-MajorityVoting-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 77.2241829793
-	-On Test : 57.619047619
-	-On Validation : 68.5393258427
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- SVM Linear with C : 4160
-		- SVM Linear with C : 4160
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-SVMForLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-SVMForLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index f0611ce5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-SVMForLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 57.1428571429
-	-On Validation : 85.1685393258
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- SVM Linear with C : 4160
-		- SVM Linear with C : 4160
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081637Results-Fusion-LateFusion-WeightedLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081637Results-Fusion-LateFusion-WeightedLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index e87cbf61..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081637Results-Fusion-LateFusion-WeightedLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 23.3651324521
-	-On Test : 21.4285714286
-	-On Validation : 20.0
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- SVM Linear with C : 4160
-		- SVM Linear with C : 4160
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081825-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-081825-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 35bb0793..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081825-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2242 +0,0 @@
-2016-09-06 08:18:25,832 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:18:25,832 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00014275 Gbytes /!\ 
-2016-09-06 08:18:30,846 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 08:18:30,849 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 08:18:30,899 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:30,899 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:18:30,900 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:30,900 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:30,900 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:18:30,900 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:30,900 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:30,900 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:30,900 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:30,900 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:30,901 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:30,901 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:30,901 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:30,901 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:30,961 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:30,961 DEBUG: Start:	 Training
-2016-09-06 08:18:30,963 DEBUG: Info:	 Time for Training: 0.0640490055084[s]
-2016-09-06 08:18:30,963 DEBUG: Done:	 Training
-2016-09-06 08:18:30,963 DEBUG: Start:	 Predicting
-2016-09-06 08:18:30,966 DEBUG: Done:	 Predicting
-2016-09-06 08:18:30,966 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:30,967 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:30,967 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:18:30,967 INFO: Done:	 Result Analysis
-2016-09-06 08:18:30,988 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:30,988 DEBUG: Start:	 Training
-2016-09-06 08:18:30,992 DEBUG: Info:	 Time for Training: 0.0932528972626[s]
-2016-09-06 08:18:30,992 DEBUG: Done:	 Training
-2016-09-06 08:18:30,992 DEBUG: Start:	 Predicting
-2016-09-06 08:18:30,995 DEBUG: Done:	 Predicting
-2016-09-06 08:18:30,995 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:30,997 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:30,997 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:18:30,997 INFO: Done:	 Result Analysis
-2016-09-06 08:18:31,158 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,158 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,159 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:18:31,159 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:18:31,159 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:31,159 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:31,160 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,160 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,160 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,160 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,160 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:31,160 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:31,160 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,160 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,244 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:31,244 DEBUG: Start:	 Training
-2016-09-06 08:18:31,245 DEBUG: Info:	 Time for Training: 0.0878131389618[s]
-2016-09-06 08:18:31,245 DEBUG: Done:	 Training
-2016-09-06 08:18:31,245 DEBUG: Start:	 Predicting
-2016-09-06 08:18:31,255 DEBUG: Done:	 Predicting
-2016-09-06 08:18:31,255 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:31,257 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:31,257 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:18:31,257 INFO: Done:	 Result Analysis
-2016-09-06 08:18:31,473 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:31,473 DEBUG: Start:	 Training
-2016-09-06 08:18:31,484 DEBUG: Info:	 Time for Training: 0.32683300972[s]
-2016-09-06 08:18:31,484 DEBUG: Done:	 Training
-2016-09-06 08:18:31,484 DEBUG: Start:	 Predicting
-2016-09-06 08:18:31,487 DEBUG: Done:	 Predicting
-2016-09-06 08:18:31,487 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:31,488 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:31,489 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.919047619048
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:18:31,489 INFO: Done:	 Result Analysis
-2016-09-06 08:18:31,608 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,608 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,608 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:18:31,608 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:18:31,608 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:31,608 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:31,609 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,609 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,609 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,609 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,609 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:31,609 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:31,609 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,609 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,683 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:31,684 DEBUG: Start:	 Training
-2016-09-06 08:18:31,684 DEBUG: Info:	 Time for Training: 0.0768630504608[s]
-2016-09-06 08:18:31,684 DEBUG: Done:	 Training
-2016-09-06 08:18:31,685 DEBUG: Start:	 Predicting
-2016-09-06 08:18:31,695 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:31,695 DEBUG: Start:	 Training
-2016-09-06 08:18:31,707 DEBUG: Done:	 Predicting
-2016-09-06 08:18:31,707 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:31,709 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:31,709 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:18:31,709 INFO: Done:	 Result Analysis
-2016-09-06 08:18:31,721 DEBUG: Info:	 Time for Training: 0.11314201355[s]
-2016-09-06 08:18:31,721 DEBUG: Done:	 Training
-2016-09-06 08:18:31,721 DEBUG: Start:	 Predicting
-2016-09-06 08:18:31,724 DEBUG: Done:	 Predicting
-2016-09-06 08:18:31,724 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:31,725 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:31,725 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:31,725 INFO: Done:	 Result Analysis
-2016-09-06 08:18:31,863 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,863 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,863 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:18:31,863 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:18:31,864 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:31,864 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:31,865 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,865 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,865 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,865 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,865 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:31,865 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:31,865 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,865 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,982 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:31,982 DEBUG: Start:	 Training
-2016-09-06 08:18:31,993 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:31,993 DEBUG: Start:	 Training
-2016-09-06 08:18:32,006 DEBUG: Info:	 Time for Training: 0.143626928329[s]
-2016-09-06 08:18:32,006 DEBUG: Done:	 Training
-2016-09-06 08:18:32,006 DEBUG: Start:	 Predicting
-2016-09-06 08:18:32,014 DEBUG: Done:	 Predicting
-2016-09-06 08:18:32,014 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:32,015 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:32,016 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,016 INFO: Done:	 Result Analysis
-2016-09-06 08:18:32,018 DEBUG: Info:	 Time for Training: 0.155956029892[s]
-2016-09-06 08:18:32,018 DEBUG: Done:	 Training
-2016-09-06 08:18:32,018 DEBUG: Start:	 Predicting
-2016-09-06 08:18:32,022 DEBUG: Done:	 Predicting
-2016-09-06 08:18:32,022 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:32,023 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:32,023 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,023 INFO: Done:	 Result Analysis
-2016-09-06 08:18:32,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,112 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:18:32,113 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:32,113 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:18:32,113 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:32,113 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,113 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,114 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:32,114 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,114 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,114 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,114 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:32,114 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,181 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:32,181 DEBUG: Start:	 Training
-2016-09-06 08:18:32,184 DEBUG: Info:	 Time for Training: 0.0724370479584[s]
-2016-09-06 08:18:32,184 DEBUG: Done:	 Training
-2016-09-06 08:18:32,184 DEBUG: Start:	 Predicting
-2016-09-06 08:18:32,187 DEBUG: Done:	 Predicting
-2016-09-06 08:18:32,187 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:32,188 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:32,188 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,188 INFO: Done:	 Result Analysis
-2016-09-06 08:18:32,202 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:32,202 DEBUG: Start:	 Training
-2016-09-06 08:18:32,206 DEBUG: Info:	 Time for Training: 0.0945439338684[s]
-2016-09-06 08:18:32,206 DEBUG: Done:	 Training
-2016-09-06 08:18:32,206 DEBUG: Start:	 Predicting
-2016-09-06 08:18:32,209 DEBUG: Done:	 Predicting
-2016-09-06 08:18:32,209 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:32,211 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:32,211 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,211 INFO: Done:	 Result Analysis
-2016-09-06 08:18:32,363 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,363 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,364 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:18:32,364 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:18:32,364 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:32,364 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:32,365 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,365 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,365 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,365 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,365 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:32,365 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:32,365 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,365 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,420 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:32,421 DEBUG: Start:	 Training
-2016-09-06 08:18:32,421 DEBUG: Info:	 Time for Training: 0.0582938194275[s]
-2016-09-06 08:18:32,421 DEBUG: Done:	 Training
-2016-09-06 08:18:32,421 DEBUG: Start:	 Predicting
-2016-09-06 08:18:32,428 DEBUG: Done:	 Predicting
-2016-09-06 08:18:32,429 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:32,430 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:32,430 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 36
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,430 INFO: Done:	 Result Analysis
-2016-09-06 08:18:32,646 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:32,646 DEBUG: Start:	 Training
-2016-09-06 08:18:32,657 DEBUG: Info:	 Time for Training: 0.294118881226[s]
-2016-09-06 08:18:32,657 DEBUG: Done:	 Training
-2016-09-06 08:18:32,657 DEBUG: Start:	 Predicting
-2016-09-06 08:18:32,660 DEBUG: Done:	 Predicting
-2016-09-06 08:18:32,660 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:32,661 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:32,661 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.895238095238
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,662 INFO: Done:	 Result Analysis
-2016-09-06 08:18:32,818 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,818 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,819 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:18:32,819 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:18:32,819 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:32,819 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:32,819 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,819 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,820 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,820 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,820 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:32,820 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:32,820 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,820 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,905 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:32,905 DEBUG: Start:	 Training
-2016-09-06 08:18:32,906 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:32,906 DEBUG: Start:	 Training
-2016-09-06 08:18:32,907 DEBUG: Info:	 Time for Training: 0.089103937149[s]
-2016-09-06 08:18:32,907 DEBUG: Done:	 Training
-2016-09-06 08:18:32,907 DEBUG: Start:	 Predicting
-2016-09-06 08:18:32,924 DEBUG: Info:	 Time for Training: 0.106693983078[s]
-2016-09-06 08:18:32,924 DEBUG: Done:	 Training
-2016-09-06 08:18:32,925 DEBUG: Start:	 Predicting
-2016-09-06 08:18:32,925 DEBUG: Done:	 Predicting
-2016-09-06 08:18:32,925 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:32,927 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:32,927 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,927 INFO: Done:	 Result Analysis
-2016-09-06 08:18:32,928 DEBUG: Done:	 Predicting
-2016-09-06 08:18:32,928 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:32,929 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:32,929 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,929 INFO: Done:	 Result Analysis
-2016-09-06 08:18:33,067 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,067 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,067 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:18:33,067 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:18:33,067 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:33,067 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:33,068 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:33,068 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:33,068 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:33,068 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:33,068 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:33,068 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:33,068 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,068 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,157 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:33,157 DEBUG: Start:	 Training
-2016-09-06 08:18:33,159 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:33,160 DEBUG: Start:	 Training
-2016-09-06 08:18:33,174 DEBUG: Info:	 Time for Training: 0.10836482048[s]
-2016-09-06 08:18:33,174 DEBUG: Done:	 Training
-2016-09-06 08:18:33,174 DEBUG: Start:	 Predicting
-2016-09-06 08:18:33,176 DEBUG: Info:	 Time for Training: 0.109962940216[s]
-2016-09-06 08:18:33,176 DEBUG: Done:	 Training
-2016-09-06 08:18:33,176 DEBUG: Start:	 Predicting
-2016-09-06 08:18:33,180 DEBUG: Done:	 Predicting
-2016-09-06 08:18:33,180 DEBUG: Done:	 Predicting
-2016-09-06 08:18:33,180 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:33,180 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:33,181 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:33,181 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:33,181 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,181 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,181 INFO: Done:	 Result Analysis
-2016-09-06 08:18:33,181 INFO: Done:	 Result Analysis
-2016-09-06 08:18:33,314 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,314 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,315 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:18:33,315 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:33,315 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:18:33,315 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:33,316 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:33,316 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:33,316 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:33,316 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:33,316 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:33,316 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:33,316 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,316 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,380 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:33,380 DEBUG: Start:	 Training
-2016-09-06 08:18:33,383 DEBUG: Info:	 Time for Training: 0.0689311027527[s]
-2016-09-06 08:18:33,383 DEBUG: Done:	 Training
-2016-09-06 08:18:33,383 DEBUG: Start:	 Predicting
-2016-09-06 08:18:33,385 DEBUG: Done:	 Predicting
-2016-09-06 08:18:33,385 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:33,386 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:33,386 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,387 INFO: Done:	 Result Analysis
-2016-09-06 08:18:33,406 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:33,406 DEBUG: Start:	 Training
-2016-09-06 08:18:33,410 DEBUG: Info:	 Time for Training: 0.0963981151581[s]
-2016-09-06 08:18:33,410 DEBUG: Done:	 Training
-2016-09-06 08:18:33,410 DEBUG: Start:	 Predicting
-2016-09-06 08:18:33,413 DEBUG: Done:	 Predicting
-2016-09-06 08:18:33,413 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:33,415 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:33,415 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,415 INFO: Done:	 Result Analysis
-2016-09-06 08:18:33,566 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,566 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,567 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:18:33,567 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:18:33,567 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:33,567 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:33,568 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:33,568 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:33,568 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:33,568 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:33,568 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:33,568 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:33,568 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,568 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,621 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:33,622 DEBUG: Start:	 Training
-2016-09-06 08:18:33,622 DEBUG: Info:	 Time for Training: 0.0565719604492[s]
-2016-09-06 08:18:33,622 DEBUG: Done:	 Training
-2016-09-06 08:18:33,622 DEBUG: Start:	 Predicting
-2016-09-06 08:18:33,630 DEBUG: Done:	 Predicting
-2016-09-06 08:18:33,630 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:33,631 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:33,631 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 36
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,631 INFO: Done:	 Result Analysis
-2016-09-06 08:18:33,865 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:33,865 DEBUG: Start:	 Training
-2016-09-06 08:18:33,896 DEBUG: Info:	 Time for Training: 0.330600976944[s]
-2016-09-06 08:18:33,896 DEBUG: Done:	 Training
-2016-09-06 08:18:33,896 DEBUG: Start:	 Predicting
-2016-09-06 08:18:33,901 DEBUG: Done:	 Predicting
-2016-09-06 08:18:33,901 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:33,902 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:33,902 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.942857142857
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 12, max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.942857142857
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,902 INFO: Done:	 Result Analysis
-2016-09-06 08:18:34,022 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,022 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,022 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:18:34,022 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:18:34,023 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:34,023 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:34,024 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:34,024 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:34,024 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:34,024 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:34,024 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:34,024 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:34,024 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,024 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,140 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:34,141 DEBUG: Start:	 Training
-2016-09-06 08:18:34,142 DEBUG: Info:	 Time for Training: 0.120800018311[s]
-2016-09-06 08:18:34,142 DEBUG: Done:	 Training
-2016-09-06 08:18:34,142 DEBUG: Start:	 Predicting
-2016-09-06 08:18:34,153 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:34,153 DEBUG: Start:	 Training
-2016-09-06 08:18:34,154 DEBUG: Done:	 Predicting
-2016-09-06 08:18:34,154 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:34,156 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:34,156 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,156 INFO: Done:	 Result Analysis
-2016-09-06 08:18:34,178 DEBUG: Info:	 Time for Training: 0.157228946686[s]
-2016-09-06 08:18:34,178 DEBUG: Done:	 Training
-2016-09-06 08:18:34,178 DEBUG: Start:	 Predicting
-2016-09-06 08:18:34,182 DEBUG: Done:	 Predicting
-2016-09-06 08:18:34,182 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:34,183 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:34,183 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,183 INFO: Done:	 Result Analysis
-2016-09-06 08:18:34,272 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,272 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:18:34,272 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,272 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:34,273 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:18:34,273 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:34,273 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:34,273 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:34,273 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:34,274 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:34,274 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:34,274 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,274 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:34,274 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,355 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:34,355 DEBUG: Start:	 Training
-2016-09-06 08:18:34,373 DEBUG: Info:	 Time for Training: 0.100815057755[s]
-2016-09-06 08:18:34,373 DEBUG: Done:	 Training
-2016-09-06 08:18:34,373 DEBUG: Start:	 Predicting
-2016-09-06 08:18:34,374 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:34,374 DEBUG: Start:	 Training
-2016-09-06 08:18:34,378 DEBUG: Done:	 Predicting
-2016-09-06 08:18:34,379 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:34,380 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:34,380 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,380 INFO: Done:	 Result Analysis
-2016-09-06 08:18:34,392 DEBUG: Info:	 Time for Training: 0.12112402916[s]
-2016-09-06 08:18:34,393 DEBUG: Done:	 Training
-2016-09-06 08:18:34,393 DEBUG: Start:	 Predicting
-2016-09-06 08:18:34,397 DEBUG: Done:	 Predicting
-2016-09-06 08:18:34,397 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:34,398 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:34,398 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,399 INFO: Done:	 Result Analysis
-2016-09-06 08:18:34,526 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,527 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:18:34,527 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:34,528 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:34,528 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:34,528 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:34,528 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,528 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,528 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:18:34,529 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:34,529 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:34,530 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:34,530 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:34,530 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,591 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:34,591 DEBUG: Start:	 Training
-2016-09-06 08:18:34,593 DEBUG: Info:	 Time for Training: 0.0652930736542[s]
-2016-09-06 08:18:34,593 DEBUG: Done:	 Training
-2016-09-06 08:18:34,593 DEBUG: Start:	 Predicting
-2016-09-06 08:18:34,595 DEBUG: Done:	 Predicting
-2016-09-06 08:18:34,596 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:34,597 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:34,597 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,597 INFO: Done:	 Result Analysis
-2016-09-06 08:18:34,614 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:34,614 DEBUG: Start:	 Training
-2016-09-06 08:18:34,618 DEBUG: Info:	 Time for Training: 0.0917420387268[s]
-2016-09-06 08:18:34,618 DEBUG: Done:	 Training
-2016-09-06 08:18:34,618 DEBUG: Start:	 Predicting
-2016-09-06 08:18:34,621 DEBUG: Done:	 Predicting
-2016-09-06 08:18:34,621 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:34,622 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:34,622 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,623 INFO: Done:	 Result Analysis
-2016-09-06 08:18:34,780 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,780 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,781 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:18:34,781 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:18:34,781 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:34,781 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:34,782 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:34,782 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:34,782 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:34,782 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:34,782 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:34,782 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:34,783 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,783 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,863 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:34,864 DEBUG: Start:	 Training
-2016-09-06 08:18:34,864 DEBUG: Info:	 Time for Training: 0.0847318172455[s]
-2016-09-06 08:18:34,864 DEBUG: Done:	 Training
-2016-09-06 08:18:34,864 DEBUG: Start:	 Predicting
-2016-09-06 08:18:34,871 DEBUG: Done:	 Predicting
-2016-09-06 08:18:34,872 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:34,873 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:34,873 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,873 INFO: Done:	 Result Analysis
-2016-09-06 08:18:35,108 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:35,109 DEBUG: Start:	 Training
-2016-09-06 08:18:35,140 DEBUG: Info:	 Time for Training: 0.360179901123[s]
-2016-09-06 08:18:35,140 DEBUG: Done:	 Training
-2016-09-06 08:18:35,140 DEBUG: Start:	 Predicting
-2016-09-06 08:18:35,145 DEBUG: Done:	 Predicting
-2016-09-06 08:18:35,145 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:35,146 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:35,146 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 12, max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.895238095238
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:18:35,146 INFO: Done:	 Result Analysis
-2016-09-06 08:18:35,224 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:35,224 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:18:35,224 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:35,224 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:35,224 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:18:35,224 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:18:35,225 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:35,225 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:35,225 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:35,225 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:35,225 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:35,225 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:35,225 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:18:35,226 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:18:35,305 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:35,306 DEBUG: Start:	 Training
-2016-09-06 08:18:35,306 DEBUG: Info:	 Time for Training: 0.0829739570618[s]
-2016-09-06 08:18:35,306 DEBUG: Done:	 Training
-2016-09-06 08:18:35,307 DEBUG: Start:	 Predicting
-2016-09-06 08:18:35,308 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:18:35,308 DEBUG: Start:	 Training
-2016-09-06 08:18:35,317 DEBUG: Done:	 Predicting
-2016-09-06 08:18:35,317 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:35,319 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:35,319 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:18:35,319 INFO: Done:	 Result Analysis
-2016-09-06 08:18:35,334 DEBUG: Info:	 Time for Training: 0.110707998276[s]
-2016-09-06 08:18:35,334 DEBUG: Done:	 Training
-2016-09-06 08:18:35,334 DEBUG: Start:	 Predicting
-2016-09-06 08:18:35,338 DEBUG: Done:	 Predicting
-2016-09-06 08:18:35,338 DEBUG: Start:	 Getting Results
-2016-09-06 08:18:35,339 DEBUG: Done:	 Getting Results
-2016-09-06 08:18:35,339 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:35,339 INFO: Done:	 Result Analysis
-2016-09-06 08:18:35,619 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:35,620 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:18:35,620 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:35,620 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:35,620 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:18:35,620 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:18:35,621 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:18:35,621 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:18:35,621 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:18:35,621 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:18:35,622 INFO: Info:	 Shape of View3 :(300, 11)
-2016-09-06 08:18:35,622 INFO: Info:	 Shape of View3 :(300, 11)
-2016-09-06 08:18:35,622 INFO: Done:	 Read Database Files
-2016-09-06 08:18:35,622 INFO: Done:	 Read Database Files
-2016-09-06 08:18:35,622 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:18:35,622 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:18:35,626 INFO: Done:	 Determine validation split
-2016-09-06 08:18:35,626 INFO: Done:	 Determine validation split
-2016-09-06 08:18:35,626 INFO: Start:	 Determine 5 folds
-2016-09-06 08:18:35,626 INFO: Start:	 Determine 5 folds
-2016-09-06 08:18:35,632 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:18:35,632 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:18:35,632 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:18:35,632 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:18:35,632 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:18:35,632 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:18:35,632 INFO: Done:	 Determine folds
-2016-09-06 08:18:35,632 INFO: Done:	 Determine folds
-2016-09-06 08:18:35,632 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:18:35,632 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 08:18:35,633 INFO: Start:	 Classification
-2016-09-06 08:18:35,633 INFO: Start:	 Classification
-2016-09-06 08:18:35,633 INFO: 	Start:	 Fold number 1
-2016-09-06 08:18:35,633 INFO: 	Start:	 Fold number 1
-2016-09-06 08:18:35,664 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:18:35,672 DEBUG: 			View 0 : 0.502793296089
-2016-09-06 08:18:35,679 DEBUG: 			View 1 : 0.575418994413
-2016-09-06 08:18:35,686 DEBUG: 			View 2 : 0.446927374302
-2016-09-06 08:18:35,694 DEBUG: 			View 3 : 0.558659217877
-2016-09-06 08:18:35,697 INFO: 	Start: 	 Classification
-2016-09-06 08:18:35,727 DEBUG: 			 Best view : 		View2
-2016-09-06 08:18:35,755 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:18:35,756 INFO: 	Start:	 Fold number 2
-2016-09-06 08:18:35,814 INFO: 	Start: 	 Classification
-2016-09-06 08:18:35,815 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:18:35,822 DEBUG: 			View 0 : 0.743016759777
-2016-09-06 08:18:35,829 DEBUG: 			View 1 : 0.698324022346
-2016-09-06 08:18:35,837 DEBUG: 			View 2 : 0.620111731844
-2016-09-06 08:18:35,845 DEBUG: 			View 3 : 0.63687150838
-2016-09-06 08:18:35,851 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:18:35,851 INFO: 	Start:	 Fold number 3
-2016-09-06 08:18:35,884 DEBUG: 			 Best view : 		View0
-2016-09-06 08:18:35,909 INFO: 	Start: 	 Classification
-2016-09-06 08:18:35,945 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:18:35,945 INFO: 	Start:	 Fold number 4
-2016-09-06 08:18:36,004 INFO: 	Start: 	 Classification
-2016-09-06 08:18:36,039 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:18:36,039 INFO: 	Start:	 Fold number 5
-2016-09-06 08:18:36,044 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:18:36,056 DEBUG: 			View 0 : 0.743016759777
-2016-09-06 08:18:36,069 DEBUG: 			View 1 : 0.698324022346
-2016-09-06 08:18:36,081 DEBUG: 			View 2 : 0.620111731844
-2016-09-06 08:18:36,091 DEBUG: 			View 3 : 0.63687150838
-2016-09-06 08:18:36,119 INFO: 	Start: 	 Classification
-2016-09-06 08:18:36,133 DEBUG: 			 Best view : 		View0
-2016-09-06 08:18:36,154 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:18:36,154 INFO: Done:	 Classification
-2016-09-06 08:18:36,155 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:18:36,155 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:18:36,159 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 96.0592946781
-	-On Test : 48.7804878049
-	-On Validation : 80.6741573034
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 41
-		- Decision Tree with max_depth : 9
-		- Random Forest with num_esimators : 12, max_depth : 5
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:36,160 INFO: Done:	 Result Analysis
-2016-09-06 08:18:36,350 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:18:36,358 DEBUG: 			View 0 : 0.653631284916
-2016-09-06 08:18:36,365 DEBUG: 			View 1 : 0.731843575419
-2016-09-06 08:18:36,372 DEBUG: 			View 2 : 0.569832402235
-2016-09-06 08:18:36,379 DEBUG: 			View 3 : 0.664804469274
-2016-09-06 08:18:36,422 DEBUG: 			 Best view : 		View1
-2016-09-06 08:18:36,702 INFO: 	Start: 	 Classification
-2016-09-06 08:18:37,164 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:18:37,164 INFO: 	Start:	 Fold number 2
-2016-09-06 08:18:37,194 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:18:37,200 DEBUG: 			View 0 : 0.5
-2016-09-06 08:18:37,207 DEBUG: 			View 1 : 0.522222222222
-2016-09-06 08:18:37,214 DEBUG: 			View 2 : 0.533333333333
-2016-09-06 08:18:37,220 DEBUG: 			View 3 : 0.477777777778
-2016-09-06 08:18:37,251 DEBUG: 			 Best view : 		View2
-2016-09-06 08:18:37,330 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:18:37,337 DEBUG: 			View 0 : 0.705555555556
-2016-09-06 08:18:37,344 DEBUG: 			View 1 : 0.65
-2016-09-06 08:18:37,351 DEBUG: 			View 2 : 0.738888888889
-2016-09-06 08:18:37,358 DEBUG: 			View 3 : 0.705555555556
-2016-09-06 08:18:37,396 DEBUG: 			 Best view : 		View2
-2016-09-06 08:18:37,543 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:18:37,550 DEBUG: 			View 0 : 0.705555555556
-2016-09-06 08:18:37,557 DEBUG: 			View 1 : 0.65
-2016-09-06 08:18:37,564 DEBUG: 			View 2 : 0.738888888889
-2016-09-06 08:18:37,572 DEBUG: 			View 3 : 0.705555555556
-2016-09-06 08:18:37,612 DEBUG: 			 Best view : 		View2
-2016-09-06 08:18:37,827 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:18:37,834 DEBUG: 			View 0 : 0.7
-2016-09-06 08:18:37,841 DEBUG: 			View 1 : 0.655555555556
-2016-09-06 08:18:37,848 DEBUG: 			View 2 : 0.683333333333
-2016-09-06 08:18:37,855 DEBUG: 			View 3 : 0.688888888889
-2016-09-06 08:18:37,898 DEBUG: 			 Best view : 		View3
-2016-09-06 08:18:38,178 INFO: 	Start: 	 Classification
-2016-09-06 08:18:38,641 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:18:38,642 INFO: 	Start:	 Fold number 3
-2016-09-06 08:18:38,672 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:18:38,678 DEBUG: 			View 0 : 0.48087431694
-2016-09-06 08:18:38,685 DEBUG: 			View 1 : 0.453551912568
-2016-09-06 08:18:38,692 DEBUG: 			View 2 : 0.546448087432
-2016-09-06 08:18:38,699 DEBUG: 			View 3 : 0.469945355191
-2016-09-06 08:18:38,731 DEBUG: 			 Best view : 		View3
-2016-09-06 08:18:38,811 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:18:38,818 DEBUG: 			View 0 : 0.748633879781
-2016-09-06 08:18:38,826 DEBUG: 			View 1 : 0.737704918033
-2016-09-06 08:18:38,833 DEBUG: 			View 2 : 0.715846994536
-2016-09-06 08:18:38,840 DEBUG: 			View 3 : 0.644808743169
-2016-09-06 08:18:38,879 DEBUG: 			 Best view : 		View0
-2016-09-06 08:18:39,031 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:18:39,038 DEBUG: 			View 0 : 0.748633879781
-2016-09-06 08:18:39,046 DEBUG: 			View 1 : 0.737704918033
-2016-09-06 08:18:39,053 DEBUG: 			View 2 : 0.715846994536
-2016-09-06 08:18:39,060 DEBUG: 			View 3 : 0.644808743169
-2016-09-06 08:18:39,101 DEBUG: 			 Best view : 		View0
-2016-09-06 08:18:39,320 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:18:39,327 DEBUG: 			View 0 : 0.715846994536
-2016-09-06 08:18:39,335 DEBUG: 			View 1 : 0.601092896175
-2016-09-06 08:18:39,342 DEBUG: 			View 2 : 0.628415300546
-2016-09-06 08:18:39,349 DEBUG: 			View 3 : 0.677595628415
-2016-09-06 08:18:39,393 DEBUG: 			 Best view : 		View0
-2016-09-06 08:18:39,677 INFO: 	Start: 	 Classification
-2016-09-06 08:18:40,150 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:18:40,150 INFO: 	Start:	 Fold number 4
-2016-09-06 08:18:40,179 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:18:40,187 DEBUG: 			View 0 : 0.565934065934
-2016-09-06 08:18:40,193 DEBUG: 			View 1 : 0.538461538462
-2016-09-06 08:18:40,200 DEBUG: 			View 2 : 0.510989010989
-2016-09-06 08:18:40,207 DEBUG: 			View 3 : 0.494505494505
-2016-09-06 08:18:40,239 DEBUG: 			 Best view : 		View0
-2016-09-06 08:18:40,319 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:18:40,326 DEBUG: 			View 0 : 0.725274725275
-2016-09-06 08:18:40,334 DEBUG: 			View 1 : 0.653846153846
-2016-09-06 08:18:40,341 DEBUG: 			View 2 : 0.697802197802
-2016-09-06 08:18:40,348 DEBUG: 			View 3 : 0.71978021978
-2016-09-06 08:18:40,386 DEBUG: 			 Best view : 		View0
-2016-09-06 08:18:40,534 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:18:40,541 DEBUG: 			View 0 : 0.725274725275
-2016-09-06 08:18:40,548 DEBUG: 			View 1 : 0.653846153846
-2016-09-06 08:18:40,556 DEBUG: 			View 2 : 0.697802197802
-2016-09-06 08:18:40,563 DEBUG: 			View 3 : 0.71978021978
-2016-09-06 08:18:40,604 DEBUG: 			 Best view : 		View0
-2016-09-06 08:18:40,820 INFO: 	Start: 	 Classification
-2016-09-06 08:18:41,170 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:18:41,170 INFO: 	Start:	 Fold number 5
-2016-09-06 08:18:41,200 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:18:41,207 DEBUG: 			View 0 : 0.521739130435
-2016-09-06 08:18:41,214 DEBUG: 			View 1 : 0.478260869565
-2016-09-06 08:18:41,221 DEBUG: 			View 2 : 0.521739130435
-2016-09-06 08:18:41,228 DEBUG: 			View 3 : 0.467391304348
-2016-09-06 08:18:41,260 DEBUG: 			 Best view : 		View3
-2016-09-06 08:18:41,340 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:18:41,348 DEBUG: 			View 0 : 0.673913043478
-2016-09-06 08:18:41,356 DEBUG: 			View 1 : 0.663043478261
-2016-09-06 08:18:41,363 DEBUG: 			View 2 : 0.635869565217
-2016-09-06 08:18:41,370 DEBUG: 			View 3 : 0.684782608696
-2016-09-06 08:18:41,409 DEBUG: 			 Best view : 		View3
-2016-09-06 08:18:41,559 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:18:41,566 DEBUG: 			View 0 : 0.673913043478
-2016-09-06 08:18:41,573 DEBUG: 			View 1 : 0.663043478261
-2016-09-06 08:18:41,581 DEBUG: 			View 2 : 0.635869565217
-2016-09-06 08:18:41,588 DEBUG: 			View 3 : 0.684782608696
-2016-09-06 08:18:41,630 DEBUG: 			 Best view : 		View3
-2016-09-06 08:18:41,848 INFO: 	Start: 	 Classification
-2016-09-06 08:18:42,201 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:18:42,201 INFO: Done:	 Classification
-2016-09-06 08:18:42,202 INFO: Info:	 Time for Classification: 6[s]
-2016-09-06 08:18:42,202 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 08:18:44,442 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 72.594545944
-	-On Test : 51.2195121951
-	-On Validation : 68.7640449438Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 13), View1 of shape (300, 12), View2 of shape (300, 17), View3 of shape (300, 11)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:06        0:00:00
-	          Total        0:00:19        0:00:02
-	So a total classification time of 0:00:06.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.264245810056
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.270391061453
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.225698324022
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.249720670391
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.261111111111
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.247777777778
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.269444444444
-			- Percentage of time chosen : 0.3
-		- On View3 : 
-			- Mean average Accuracy : 0.257777777778
-			- Percentage of time chosen : 0.1
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.269398907104
-			- Percentage of time chosen : 0.9
-		- On View1 : 
-			- Mean average Accuracy : 0.253005464481
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.260655737705
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.243715846995
-			- Percentage of time chosen : 0.1
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.201648351648
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.184615384615
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.190659340659
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.193406593407
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.186956521739
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.180434782609
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.179347826087
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.183695652174
-			- Percentage of time chosen : 0.3
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 49.1620111732
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 47.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 49.7267759563
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View3
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 74.3016759777
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.8888888889
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 74.8633879781
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 72.5274725275
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 68.4782608696
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View3
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 74.3016759777
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.8888888889
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 74.8633879781
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 72.5274725275
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 67.3913043478
-			Accuracy on test : 0.0
-			Accuracy on validation : 64.0449438202
-			Selected View : View3
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 74.3016759777
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View1
-		 Fold 2
-			Accuracy on train : 73.8888888889
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View3
-		 Fold 3
-			Accuracy on train : 74.8633879781
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 49.1620111732
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 47.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 49.7267759563
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
-2016-09-06 08:18:44,625 INFO: Done:	 Result Analysis
-2016-09-06 08:18:44,692 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:44,692 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:44,693 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:18:44,693 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:44,694 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:44,694 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:18:44,695 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:18:44,695 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:18:44,695 INFO: Info:	 Shape of View3 :(300, 11)
-2016-09-06 08:18:44,695 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:18:44,695 INFO: Done:	 Read Database Files
-2016-09-06 08:18:44,695 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:18:44,696 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:18:44,696 INFO: Info:	 Shape of View3 :(300, 11)
-2016-09-06 08:18:44,696 INFO: Done:	 Read Database Files
-2016-09-06 08:18:44,697 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:18:44,699 INFO: Done:	 Determine validation split
-2016-09-06 08:18:44,700 INFO: Start:	 Determine 5 folds
-2016-09-06 08:18:44,701 INFO: Done:	 Determine validation split
-2016-09-06 08:18:44,701 INFO: Start:	 Determine 5 folds
-2016-09-06 08:18:44,707 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:18:44,707 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:18:44,707 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:18:44,707 INFO: Done:	 Determine folds
-2016-09-06 08:18:44,708 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:18:44,708 INFO: Start:	 Classification
-2016-09-06 08:18:44,708 INFO: 	Start:	 Fold number 1
-2016-09-06 08:18:44,708 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:18:44,708 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:18:44,709 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:18:44,709 INFO: Done:	 Determine folds
-2016-09-06 08:18:44,709 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:18:44,709 INFO: Start:	 Classification
-2016-09-06 08:18:44,709 INFO: 	Start:	 Fold number 1
-2016-09-06 08:18:44,771 INFO: 	Start: 	 Classification
-2016-09-06 08:18:44,797 INFO: 	Start: 	 Classification
-2016-09-06 08:18:44,835 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:18:44,835 INFO: 	Start:	 Fold number 2
-2016-09-06 08:18:44,855 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:18:44,855 INFO: 	Start:	 Fold number 2
-2016-09-06 08:18:44,914 INFO: 	Start: 	 Classification
-2016-09-06 08:18:44,916 INFO: 	Start: 	 Classification
-2016-09-06 08:18:44,952 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:18:44,952 INFO: 	Start:	 Fold number 3
-2016-09-06 08:18:44,997 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:18:44,997 INFO: 	Start:	 Fold number 3
-2016-09-06 08:18:45,032 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,057 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,069 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:18:45,070 INFO: 	Start:	 Fold number 4
-2016-09-06 08:18:45,140 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:18:45,140 INFO: 	Start:	 Fold number 4
-2016-09-06 08:18:45,150 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,187 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:18:45,187 INFO: 	Start:	 Fold number 5
-2016-09-06 08:18:45,200 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,267 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,283 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:18:45,283 INFO: 	Start:	 Fold number 5
-2016-09-06 08:18:45,304 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:18:45,304 INFO: Done:	 Classification
-2016-09-06 08:18:45,304 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:18:45,304 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:18:45,309 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 94.6159403923
-	-On Test : 47.8048780488
-	-On Validation : 83.1460674157
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 41
-		- Decision Tree with max_depth : 9
-		- Random Forest with num_esimators : 12, max_depth : 5
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:45,309 INFO: Done:	 Result Analysis
-2016-09-06 08:18:45,341 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,420 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:18:45,420 INFO: Done:	 Classification
-2016-09-06 08:18:45,420 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:18:45,420 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:18:45,424 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 86.9895666159
-	-On Test : 49.2682926829
-	-On Validation : 76.404494382
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 41
-		- Decision Tree with max_depth : 9
-		- Random Forest with num_esimators : 12, max_depth : 5
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:45,424 INFO: Done:	 Result Analysis
-2016-09-06 08:18:45,544 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:45,545 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:45,545 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:45,545 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:45,546 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:18:45,546 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:18:45,546 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:18:45,547 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:18:45,547 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:18:45,547 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:18:45,547 INFO: Info:	 Shape of View3 :(300, 11)
-2016-09-06 08:18:45,547 INFO: Done:	 Read Database Files
-2016-09-06 08:18:45,548 INFO: Info:	 Shape of View3 :(300, 11)
-2016-09-06 08:18:45,548 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:18:45,548 INFO: Done:	 Read Database Files
-2016-09-06 08:18:45,548 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:18:45,552 INFO: Done:	 Determine validation split
-2016-09-06 08:18:45,552 INFO: Done:	 Determine validation split
-2016-09-06 08:18:45,552 INFO: Start:	 Determine 5 folds
-2016-09-06 08:18:45,552 INFO: Start:	 Determine 5 folds
-2016-09-06 08:18:45,558 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:18:45,559 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:18:45,559 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:18:45,559 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:18:45,559 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:18:45,559 INFO: Done:	 Determine folds
-2016-09-06 08:18:45,559 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:18:45,559 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:18:45,559 INFO: Done:	 Determine folds
-2016-09-06 08:18:45,559 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:18:45,559 INFO: Start:	 Classification
-2016-09-06 08:18:45,559 INFO: Start:	 Classification
-2016-09-06 08:18:45,559 INFO: 	Start:	 Fold number 1
-2016-09-06 08:18:45,559 INFO: 	Start:	 Fold number 1
-2016-09-06 08:18:45,585 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,610 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:18:45,611 INFO: 	Start:	 Fold number 2
-2016-09-06 08:18:45,623 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,634 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,659 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:18:45,659 INFO: 	Start:	 Fold number 3
-2016-09-06 08:18:45,680 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:18:45,681 INFO: 	Start:	 Fold number 2
-2016-09-06 08:18:45,682 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,709 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:18:45,709 INFO: 	Start:	 Fold number 4
-2016-09-06 08:18:45,732 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,748 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,758 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:18:45,758 INFO: 	Start:	 Fold number 5
-2016-09-06 08:18:45,780 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,795 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:18:45,795 INFO: 	Start:	 Fold number 3
-2016-09-06 08:18:45,806 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:18:45,806 INFO: Done:	 Classification
-2016-09-06 08:18:45,806 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:18:45,806 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:18:45,810 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 49.756097561
-	-On Validation : 85.8426966292
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:45,811 INFO: Done:	 Result Analysis
-2016-09-06 08:18:45,860 INFO: 	Start: 	 Classification
-2016-09-06 08:18:45,902 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:18:45,902 INFO: 	Start:	 Fold number 4
-2016-09-06 08:18:45,964 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,007 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:18:46,007 INFO: 	Start:	 Fold number 5
-2016-09-06 08:18:46,068 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,111 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:18:46,111 INFO: Done:	 Classification
-2016-09-06 08:18:46,111 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:18:46,111 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:18:46,116 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 49.4786031123
-	-On Test : 46.8292682927
-	-On Validation : 47.6404494382
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 41
-		- Decision Tree with max_depth : 9
-		- Random Forest with num_esimators : 12, max_depth : 5
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:46,116 INFO: Done:	 Result Analysis
-2016-09-06 08:18:46,193 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:46,193 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:46,193 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:46,193 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:46,194 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:18:46,194 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:18:46,194 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:18:46,194 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:18:46,195 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:18:46,195 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:18:46,195 INFO: Info:	 Shape of View3 :(300, 11)
-2016-09-06 08:18:46,195 INFO: Done:	 Read Database Files
-2016-09-06 08:18:46,195 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:18:46,196 INFO: Info:	 Shape of View3 :(300, 11)
-2016-09-06 08:18:46,196 INFO: Done:	 Read Database Files
-2016-09-06 08:18:46,196 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:18:46,199 INFO: Done:	 Determine validation split
-2016-09-06 08:18:46,200 INFO: Start:	 Determine 5 folds
-2016-09-06 08:18:46,201 INFO: Done:	 Determine validation split
-2016-09-06 08:18:46,201 INFO: Start:	 Determine 5 folds
-2016-09-06 08:18:46,206 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:18:46,206 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:18:46,206 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:18:46,206 INFO: Done:	 Determine folds
-2016-09-06 08:18:46,206 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:18:46,206 INFO: Start:	 Classification
-2016-09-06 08:18:46,207 INFO: 	Start:	 Fold number 1
-2016-09-06 08:18:46,209 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:18:46,210 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:18:46,210 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:18:46,210 INFO: Done:	 Determine folds
-2016-09-06 08:18:46,210 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:18:46,210 INFO: Start:	 Classification
-2016-09-06 08:18:46,210 INFO: 	Start:	 Fold number 1
-2016-09-06 08:18:46,225 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,231 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,249 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:18:46,249 INFO: 	Start:	 Fold number 2
-2016-09-06 08:18:46,262 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:18:46,262 INFO: 	Start:	 Fold number 2
-2016-09-06 08:18:46,267 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,282 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,292 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:18:46,292 INFO: 	Start:	 Fold number 3
-2016-09-06 08:18:46,310 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,315 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:18:46,315 INFO: 	Start:	 Fold number 3
-2016-09-06 08:18:46,335 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:18:46,335 INFO: 	Start:	 Fold number 4
-2016-09-06 08:18:46,335 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,353 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,368 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:18:46,369 INFO: 	Start:	 Fold number 4
-2016-09-06 08:18:46,378 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:18:46,378 INFO: 	Start:	 Fold number 5
-2016-09-06 08:18:46,388 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,395 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,420 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:18:46,420 INFO: Done:	 Classification
-2016-09-06 08:18:46,420 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:18:46,420 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:18:46,420 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:18:46,420 INFO: 	Start:	 Fold number 5
-2016-09-06 08:18:46,425 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 59.5604395604
-	-On Test : 52.1951219512
-	-On Validation : 61.1235955056
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Decision Tree with max_depth : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:46,425 INFO: Done:	 Result Analysis
-2016-09-06 08:18:46,438 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,468 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:18:46,468 INFO: Done:	 Classification
-2016-09-06 08:18:46,468 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:18:46,469 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:18:46,474 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 42.9268292683
-	-On Validation : 82.9213483146
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- K nearest Neighbors with  n_neighbors: 1.0
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:46,474 INFO: Done:	 Result Analysis
-2016-09-06 08:18:46,543 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:46,543 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:46,543 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:46,543 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:46,544 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:18:46,544 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:18:46,545 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:18:46,545 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:18:46,545 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:18:46,545 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:18:46,546 INFO: Info:	 Shape of View3 :(300, 11)
-2016-09-06 08:18:46,546 INFO: Info:	 Shape of View3 :(300, 11)
-2016-09-06 08:18:46,546 INFO: Done:	 Read Database Files
-2016-09-06 08:18:46,546 INFO: Done:	 Read Database Files
-2016-09-06 08:18:46,546 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:18:46,546 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:18:46,554 INFO: Done:	 Determine validation split
-2016-09-06 08:18:46,554 INFO: Done:	 Determine validation split
-2016-09-06 08:18:46,554 INFO: Start:	 Determine 5 folds
-2016-09-06 08:18:46,554 INFO: Start:	 Determine 5 folds
-2016-09-06 08:18:46,564 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:18:46,565 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:18:46,565 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:18:46,565 INFO: Done:	 Determine folds
-2016-09-06 08:18:46,565 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:18:46,565 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:18:46,565 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:18:46,565 INFO: Start:	 Classification
-2016-09-06 08:18:46,565 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:18:46,565 INFO: 	Start:	 Fold number 1
-2016-09-06 08:18:46,565 INFO: Done:	 Determine folds
-2016-09-06 08:18:46,566 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:18:46,566 INFO: Start:	 Classification
-2016-09-06 08:18:46,566 INFO: 	Start:	 Fold number 1
-2016-09-06 08:18:46,595 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,669 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:18:46,669 INFO: 	Start:	 Fold number 2
-2016-09-06 08:18:46,677 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,687 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,718 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:18:46,718 INFO: 	Start:	 Fold number 2
-2016-09-06 08:18:46,718 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:18:46,718 INFO: 	Start:	 Fold number 3
-2016-09-06 08:18:46,742 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,779 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:18:46,779 INFO: 	Start:	 Fold number 4
-2016-09-06 08:18:46,801 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,820 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,830 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:18:46,831 INFO: 	Start:	 Fold number 5
-2016-09-06 08:18:46,851 INFO: 	Start: 	 Classification
-2016-09-06 08:18:46,862 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:18:46,862 INFO: 	Start:	 Fold number 3
-2016-09-06 08:18:46,879 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:18:46,880 INFO: Done:	 Classification
-2016-09-06 08:18:46,880 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:18:46,880 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:18:46,886 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 71.312383569
-	-On Test : 51.2195121951
-	-On Validation : 67.191011236
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:46,886 INFO: Done:	 Result Analysis
-2016-09-06 08:18:46,970 INFO: 	Start: 	 Classification
-2016-09-06 08:18:47,009 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:18:47,010 INFO: 	Start:	 Fold number 4
-2016-09-06 08:18:47,113 INFO: 	Start: 	 Classification
-2016-09-06 08:18:47,152 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:18:47,152 INFO: 	Start:	 Fold number 5
-2016-09-06 08:18:47,256 INFO: 	Start: 	 Classification
-2016-09-06 08:18:47,291 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:18:47,291 INFO: Done:	 Classification
-2016-09-06 08:18:47,291 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:18:47,292 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:18:47,296 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 98.2414484051
-	-On Test : 47.8048780488
-	-On Validation : 84.9438202247
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:47,296 INFO: Done:	 Result Analysis
-2016-09-06 08:18:47,389 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:47,389 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:47,390 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 08:18:47,390 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 08:18:47,391 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 08:18:47,392 INFO: Info:	 Shape of View3 :(300, 11)
-2016-09-06 08:18:47,392 INFO: Done:	 Read Database Files
-2016-09-06 08:18:47,392 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:18:47,396 INFO: Done:	 Determine validation split
-2016-09-06 08:18:47,396 INFO: Start:	 Determine 5 folds
-2016-09-06 08:18:47,402 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:18:47,403 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:18:47,403 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:18:47,403 INFO: Done:	 Determine folds
-2016-09-06 08:18:47,403 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:18:47,403 INFO: Start:	 Classification
-2016-09-06 08:18:47,403 INFO: 	Start:	 Fold number 1
-2016-09-06 08:18:47,439 INFO: 	Start: 	 Classification
-2016-09-06 08:18:47,466 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:18:47,466 INFO: 	Start:	 Fold number 2
-2016-09-06 08:18:47,501 INFO: 	Start: 	 Classification
-2016-09-06 08:18:47,528 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:18:47,528 INFO: 	Start:	 Fold number 3
-2016-09-06 08:18:47,560 INFO: 	Start: 	 Classification
-2016-09-06 08:18:47,586 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:18:47,587 INFO: 	Start:	 Fold number 4
-2016-09-06 08:18:47,621 INFO: 	Start: 	 Classification
-2016-09-06 08:18:47,648 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:18:47,648 INFO: 	Start:	 Fold number 5
-2016-09-06 08:18:47,681 INFO: 	Start: 	 Classification
-2016-09-06 08:18:47,707 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:18:47,707 INFO: Done:	 Classification
-2016-09-06 08:18:47,707 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:18:47,708 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:18:47,712 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 74.6749581183
-	-On Test : 49.2682926829
-	-On Validation : 64.2696629213
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SVM Linear with C : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:47,712 INFO: Done:	 Result Analysis
-2016-09-06 08:18:47,937 DEBUG: Start:	 Deleting 2 temporary datasets for multiprocessing
-2016-09-06 08:18:47,958 DEBUG: Start:	 Deleting datasets for multiprocessing
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9371af07..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dd41522c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ea041e8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4047539e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.919047619048
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 41fd5e1d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cd402864..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0204aa1d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f0d54f95..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0b7ed2ef..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 36
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75c6cd72..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.895238095238
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 49756f90..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d1ea6aec..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dfbdb1e8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 35649489..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 61a36d64..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1c6372ec..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5dde2441..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 36
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7479ea6b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.942857142857
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 12, max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.942857142857
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a800add9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fcc37ad3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e7f1ec3e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fcea7365..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4d603ad2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 98ac7c2f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e2004831..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c06c6d19..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 11745c30..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5da0e453..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 12, max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.895238095238
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cb0c0680..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 341b76a7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1700
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081836Results-Fusion-LateFusion-BayesianInference-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081836Results-Fusion-LateFusion-BayesianInference-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5a72c769..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081836Results-Fusion-LateFusion-BayesianInference-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 96.0592946781
-	-On Test : 48.7804878049
-	-On Validation : 80.6741573034
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 41
-		- Decision Tree with max_depth : 9
-		- Random Forest with num_esimators : 12, max_depth : 5
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081844Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-081844Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081844Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081844Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index fc73caf0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081844Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,225 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 72.594545944
-	-On Test : 51.2195121951
-	-On Validation : 68.7640449438Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 13), View1 of shape (300, 12), View2 of shape (300, 17), View3 of shape (300, 11)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:06        0:00:00
-	          Total        0:00:19        0:00:02
-	So a total classification time of 0:00:06.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.264245810056
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.270391061453
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.225698324022
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.249720670391
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.261111111111
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.247777777778
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.269444444444
-			- Percentage of time chosen : 0.3
-		- On View3 : 
-			- Mean average Accuracy : 0.257777777778
-			- Percentage of time chosen : 0.1
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.269398907104
-			- Percentage of time chosen : 0.9
-		- On View1 : 
-			- Mean average Accuracy : 0.253005464481
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.260655737705
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.243715846995
-			- Percentage of time chosen : 0.1
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.201648351648
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.184615384615
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.190659340659
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.193406593407
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.186956521739
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.180434782609
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.179347826087
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.183695652174
-			- Percentage of time chosen : 0.3
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 49.1620111732
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 47.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 49.7267759563
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View3
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 74.3016759777
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.8888888889
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 74.8633879781
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 72.5274725275
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 68.4782608696
-			Accuracy on test : 0.0
-			Accuracy on validation : 65.1685393258
-			Selected View : View3
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 74.3016759777
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 73.8888888889
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 74.8633879781
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 72.5274725275
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 67.3913043478
-			Accuracy on test : 0.0
-			Accuracy on validation : 64.0449438202
-			Selected View : View3
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 74.3016759777
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View1
-		 Fold 2
-			Accuracy on train : 73.8888888889
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View3
-		 Fold 3
-			Accuracy on train : 74.8633879781
-			Accuracy on test : 0.0
-			Accuracy on validation : 68.5393258427
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 49.1620111732
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 47.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 49.7267759563
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index db765822..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 49.756097561
-	-On Validation : 85.8426966292
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-MajorityVoting-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-MajorityVoting-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3dcda106..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-MajorityVoting-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 86.9895666159
-	-On Test : 49.2682926829
-	-On Validation : 76.404494382
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 41
-		- Decision Tree with max_depth : 9
-		- Random Forest with num_esimators : 12, max_depth : 5
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-SVMForLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-SVMForLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 86dffb90..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-SVMForLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 94.6159403923
-	-On Test : 47.8048780488
-	-On Validation : 83.1460674157
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 41
-		- Decision Tree with max_depth : 9
-		- Random Forest with num_esimators : 12, max_depth : 5
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 883fdbdc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 59.5604395604
-	-On Test : 52.1951219512
-	-On Validation : 61.1235955056
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Decision Tree with max_depth : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index bd75d96f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 42.9268292683
-	-On Validation : 82.9213483146
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- K nearest Neighbors with  n_neighbors: 1.0
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 208bafc5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 71.312383569
-	-On Test : 51.2195121951
-	-On Validation : 67.191011236
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-LateFusion-WeightedLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-LateFusion-WeightedLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 4eda1dc2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-LateFusion-WeightedLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 49.4786031123
-	-On Test : 46.8292682927
-	-On Validation : 47.6404494382
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 41
-		- Decision Tree with max_depth : 9
-		- Random Forest with num_esimators : 12, max_depth : 5
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 1add7ec9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 98.2414484051
-	-On Test : 47.8048780488
-	-On Validation : 84.9438202247
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3a40e1e0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 74.6749581183
-	-On Test : 49.2682926829
-	-On Validation : 64.2696629213
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SVM Linear with C : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-081940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 3bcfb455..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2265 +0,0 @@
-2016-09-06 08:19:40,653 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:19:40,653 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00015446875 Gbytes /!\ 
-2016-09-06 08:19:45,665 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 08:19:45,667 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 08:19:45,719 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:45,719 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:45,719 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:19:45,719 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:19:45,719 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:45,719 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:45,720 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:45,720 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:45,720 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:45,720 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:45,720 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:45,720 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:45,720 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:45,720 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:45,773 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:45,774 DEBUG: Start:	 Training
-2016-09-06 08:19:45,775 DEBUG: Info:	 Time for Training: 0.0563549995422[s]
-2016-09-06 08:19:45,775 DEBUG: Done:	 Training
-2016-09-06 08:19:45,775 DEBUG: Start:	 Predicting
-2016-09-06 08:19:45,777 DEBUG: Done:	 Predicting
-2016-09-06 08:19:45,777 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:45,778 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:45,779 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.847619047619
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.847619047619
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:19:45,779 INFO: Done:	 Result Analysis
-2016-09-06 08:19:45,809 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:45,809 DEBUG: Start:	 Training
-2016-09-06 08:19:45,812 DEBUG: Info:	 Time for Training: 0.093691110611[s]
-2016-09-06 08:19:45,812 DEBUG: Done:	 Training
-2016-09-06 08:19:45,812 DEBUG: Start:	 Predicting
-2016-09-06 08:19:45,816 DEBUG: Done:	 Predicting
-2016-09-06 08:19:45,816 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:45,818 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:45,818 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:19:45,818 INFO: Done:	 Result Analysis
-2016-09-06 08:19:45,972 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:45,972 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:19:45,972 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:45,973 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:45,973 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:45,973 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:45,974 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:45,974 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:45,974 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:19:45,974 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:45,975 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:45,976 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:45,976 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:45,976 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:46,026 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:46,026 DEBUG: Start:	 Training
-2016-09-06 08:19:46,027 DEBUG: Info:	 Time for Training: 0.0553469657898[s]
-2016-09-06 08:19:46,027 DEBUG: Done:	 Training
-2016-09-06 08:19:46,027 DEBUG: Start:	 Predicting
-2016-09-06 08:19:46,032 DEBUG: Done:	 Predicting
-2016-09-06 08:19:46,032 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:46,033 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:46,033 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:19:46,033 INFO: Done:	 Result Analysis
-2016-09-06 08:19:46,585 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:46,585 DEBUG: Start:	 Training
-2016-09-06 08:19:46,655 DEBUG: Info:	 Time for Training: 0.682506084442[s]
-2016-09-06 08:19:46,656 DEBUG: Done:	 Training
-2016-09-06 08:19:46,656 DEBUG: Start:	 Predicting
-2016-09-06 08:19:46,664 DEBUG: Done:	 Predicting
-2016-09-06 08:19:46,664 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:46,665 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:46,665 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 25, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:19:46,676 INFO: Done:	 Result Analysis
-2016-09-06 08:19:46,823 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:46,823 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:46,824 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:19:46,824 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:19:46,824 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:46,824 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:46,824 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:46,824 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:46,825 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:46,825 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:46,825 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:46,825 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:46,825 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:46,825 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:46,908 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:46,908 DEBUG: Start:	 Training
-2016-09-06 08:19:46,909 DEBUG: Info:	 Time for Training: 0.0860087871552[s]
-2016-09-06 08:19:46,909 DEBUG: Done:	 Training
-2016-09-06 08:19:46,909 DEBUG: Start:	 Predicting
-2016-09-06 08:19:46,916 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:46,916 DEBUG: Start:	 Training
-2016-09-06 08:19:46,929 DEBUG: Done:	 Predicting
-2016-09-06 08:19:46,929 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:46,930 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:46,930 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:19:46,931 INFO: Done:	 Result Analysis
-2016-09-06 08:19:46,935 DEBUG: Info:	 Time for Training: 0.112031936646[s]
-2016-09-06 08:19:46,935 DEBUG: Done:	 Training
-2016-09-06 08:19:46,935 DEBUG: Start:	 Predicting
-2016-09-06 08:19:46,939 DEBUG: Done:	 Predicting
-2016-09-06 08:19:46,939 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:46,940 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:46,940 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:19:46,941 INFO: Done:	 Result Analysis
-2016-09-06 08:19:47,070 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,071 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:19:47,071 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:47,071 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,071 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:19:47,071 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:47,071 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:47,071 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:47,072 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:47,072 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,072 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:47,072 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:47,073 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:47,073 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,150 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:47,150 DEBUG: Start:	 Training
-2016-09-06 08:19:47,166 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:47,166 DEBUG: Start:	 Training
-2016-09-06 08:19:47,167 DEBUG: Info:	 Time for Training: 0.0974969863892[s]
-2016-09-06 08:19:47,167 DEBUG: Done:	 Training
-2016-09-06 08:19:47,168 DEBUG: Start:	 Predicting
-2016-09-06 08:19:47,173 DEBUG: Done:	 Predicting
-2016-09-06 08:19:47,173 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:47,174 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:47,174 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:19:47,174 INFO: Done:	 Result Analysis
-2016-09-06 08:19:47,186 DEBUG: Info:	 Time for Training: 0.115417003632[s]
-2016-09-06 08:19:47,186 DEBUG: Done:	 Training
-2016-09-06 08:19:47,186 DEBUG: Start:	 Predicting
-2016-09-06 08:19:47,189 DEBUG: Done:	 Predicting
-2016-09-06 08:19:47,189 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:47,190 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:47,191 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:19:47,191 INFO: Done:	 Result Analysis
-2016-09-06 08:19:47,321 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,321 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,322 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:19:47,322 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:19:47,322 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:47,322 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:47,323 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:47,323 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:47,323 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:47,323 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:47,323 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:47,323 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:47,323 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,323 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,415 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:47,415 DEBUG: Start:	 Training
-2016-09-06 08:19:47,418 DEBUG: Info:	 Time for Training: 0.0981721878052[s]
-2016-09-06 08:19:47,418 DEBUG: Done:	 Training
-2016-09-06 08:19:47,418 DEBUG: Start:	 Predicting
-2016-09-06 08:19:47,422 DEBUG: Done:	 Predicting
-2016-09-06 08:19:47,422 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:47,424 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:47,424 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.790476190476
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.790476190476
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:19:47,424 INFO: Done:	 Result Analysis
-2016-09-06 08:19:47,447 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:47,447 DEBUG: Start:	 Training
-2016-09-06 08:19:47,451 DEBUG: Info:	 Time for Training: 0.131391048431[s]
-2016-09-06 08:19:47,451 DEBUG: Done:	 Training
-2016-09-06 08:19:47,452 DEBUG: Start:	 Predicting
-2016-09-06 08:19:47,454 DEBUG: Done:	 Predicting
-2016-09-06 08:19:47,455 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:47,456 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:47,456 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:19:47,457 INFO: Done:	 Result Analysis
-2016-09-06 08:19:47,569 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,569 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,569 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:19:47,569 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:19:47,569 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:47,569 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:47,571 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:47,571 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:47,571 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:47,571 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:47,571 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:47,571 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:47,571 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,571 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,655 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:47,655 DEBUG: Start:	 Training
-2016-09-06 08:19:47,656 DEBUG: Info:	 Time for Training: 0.0877361297607[s]
-2016-09-06 08:19:47,656 DEBUG: Done:	 Training
-2016-09-06 08:19:47,656 DEBUG: Start:	 Predicting
-2016-09-06 08:19:47,666 DEBUG: Done:	 Predicting
-2016-09-06 08:19:47,666 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:47,668 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:47,668 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 30
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:19:47,668 INFO: Done:	 Result Analysis
-2016-09-06 08:19:48,180 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:48,180 DEBUG: Start:	 Training
-2016-09-06 08:19:48,248 DEBUG: Info:	 Time for Training: 0.679942131042[s]
-2016-09-06 08:19:48,248 DEBUG: Done:	 Training
-2016-09-06 08:19:48,248 DEBUG: Start:	 Predicting
-2016-09-06 08:19:48,256 DEBUG: Done:	 Predicting
-2016-09-06 08:19:48,256 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:48,257 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:48,257 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 25, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,257 INFO: Done:	 Result Analysis
-2016-09-06 08:19:48,317 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,317 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,317 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:19:48,317 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:19:48,318 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:48,318 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:48,319 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,319 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,319 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,319 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,319 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:48,319 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:48,319 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,319 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,435 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:48,435 DEBUG: Start:	 Training
-2016-09-06 08:19:48,436 DEBUG: Info:	 Time for Training: 0.119863986969[s]
-2016-09-06 08:19:48,436 DEBUG: Done:	 Training
-2016-09-06 08:19:48,436 DEBUG: Start:	 Predicting
-2016-09-06 08:19:48,446 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:48,446 DEBUG: Start:	 Training
-2016-09-06 08:19:48,450 DEBUG: Done:	 Predicting
-2016-09-06 08:19:48,450 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:48,452 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:48,452 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,452 INFO: Done:	 Result Analysis
-2016-09-06 08:19:48,473 DEBUG: Info:	 Time for Training: 0.156780004501[s]
-2016-09-06 08:19:48,473 DEBUG: Done:	 Training
-2016-09-06 08:19:48,473 DEBUG: Start:	 Predicting
-2016-09-06 08:19:48,477 DEBUG: Done:	 Predicting
-2016-09-06 08:19:48,477 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:48,478 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:48,478 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,478 INFO: Done:	 Result Analysis
-2016-09-06 08:19:48,562 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,562 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,563 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:19:48,563 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:19:48,563 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:48,563 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:48,563 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,563 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,563 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,563 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,564 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:48,564 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:48,564 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,564 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,649 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:48,650 DEBUG: Start:	 Training
-2016-09-06 08:19:48,656 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:48,657 DEBUG: Start:	 Training
-2016-09-06 08:19:48,669 DEBUG: Info:	 Time for Training: 0.107176065445[s]
-2016-09-06 08:19:48,669 DEBUG: Done:	 Training
-2016-09-06 08:19:48,669 DEBUG: Start:	 Predicting
-2016-09-06 08:19:48,675 DEBUG: Done:	 Predicting
-2016-09-06 08:19:48,675 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:48,676 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:48,677 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,677 INFO: Done:	 Result Analysis
-2016-09-06 08:19:48,678 DEBUG: Info:	 Time for Training: 0.116644859314[s]
-2016-09-06 08:19:48,678 DEBUG: Done:	 Training
-2016-09-06 08:19:48,679 DEBUG: Start:	 Predicting
-2016-09-06 08:19:48,683 DEBUG: Done:	 Predicting
-2016-09-06 08:19:48,683 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:48,684 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:48,684 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5574
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,684 INFO: Done:	 Result Analysis
-2016-09-06 08:19:48,808 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,808 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,809 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:19:48,809 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:19:48,809 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:48,809 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:48,809 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,809 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,809 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,809 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,810 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:48,810 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:48,810 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,810 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,871 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:48,871 DEBUG: Start:	 Training
-2016-09-06 08:19:48,873 DEBUG: Info:	 Time for Training: 0.0652639865875[s]
-2016-09-06 08:19:48,873 DEBUG: Done:	 Training
-2016-09-06 08:19:48,873 DEBUG: Start:	 Predicting
-2016-09-06 08:19:48,876 DEBUG: Done:	 Predicting
-2016-09-06 08:19:48,876 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:48,877 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:48,877 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.785714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.785714285714
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,877 INFO: Done:	 Result Analysis
-2016-09-06 08:19:48,901 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:48,901 DEBUG: Start:	 Training
-2016-09-06 08:19:48,906 DEBUG: Info:	 Time for Training: 0.0979499816895[s]
-2016-09-06 08:19:48,906 DEBUG: Done:	 Training
-2016-09-06 08:19:48,906 DEBUG: Start:	 Predicting
-2016-09-06 08:19:48,909 DEBUG: Done:	 Predicting
-2016-09-06 08:19:48,909 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:48,911 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:48,911 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,911 INFO: Done:	 Result Analysis
-2016-09-06 08:19:49,054 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:49,054 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:49,054 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:19:49,054 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:19:49,055 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:49,055 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:49,055 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:49,055 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:49,055 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:49,055 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:49,055 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:49,055 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:49,055 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:49,055 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:49,109 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:49,109 DEBUG: Start:	 Training
-2016-09-06 08:19:49,110 DEBUG: Info:	 Time for Training: 0.0561349391937[s]
-2016-09-06 08:19:49,110 DEBUG: Done:	 Training
-2016-09-06 08:19:49,110 DEBUG: Start:	 Predicting
-2016-09-06 08:19:49,117 DEBUG: Done:	 Predicting
-2016-09-06 08:19:49,117 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:49,118 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:49,118 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 08:19:49,119 INFO: Done:	 Result Analysis
-2016-09-06 08:19:49,654 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:49,654 DEBUG: Start:	 Training
-2016-09-06 08:19:49,677 DEBUG: Info:	 Time for Training: 0.623654842377[s]
-2016-09-06 08:19:49,678 DEBUG: Done:	 Training
-2016-09-06 08:19:49,678 DEBUG: Start:	 Predicting
-2016-09-06 08:19:49,684 DEBUG: Done:	 Predicting
-2016-09-06 08:19:49,684 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:49,685 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:49,685 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:19:49,685 INFO: Done:	 Result Analysis
-2016-09-06 08:19:49,802 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:49,802 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:19:49,803 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:49,803 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:49,803 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:49,803 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:49,804 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:49,804 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:49,804 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:19:49,804 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:49,804 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:49,804 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:49,805 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:49,805 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:49,887 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:49,887 DEBUG: Start:	 Training
-2016-09-06 08:19:49,888 DEBUG: Info:	 Time for Training: 0.0861909389496[s]
-2016-09-06 08:19:49,888 DEBUG: Done:	 Training
-2016-09-06 08:19:49,888 DEBUG: Start:	 Predicting
-2016-09-06 08:19:49,906 DEBUG: Done:	 Predicting
-2016-09-06 08:19:49,906 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:49,907 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:49,907 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:49,907 INFO: Done:	 Result Analysis
-2016-09-06 08:19:49,908 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:49,908 DEBUG: Start:	 Training
-2016-09-06 08:19:49,930 DEBUG: Info:	 Time for Training: 0.126912117004[s]
-2016-09-06 08:19:49,930 DEBUG: Done:	 Training
-2016-09-06 08:19:49,930 DEBUG: Start:	 Predicting
-2016-09-06 08:19:49,934 DEBUG: Done:	 Predicting
-2016-09-06 08:19:49,934 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:49,935 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:49,935 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:19:49,936 INFO: Done:	 Result Analysis
-2016-09-06 08:19:50,055 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,055 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,055 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:19:50,055 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:19:50,055 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:50,055 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:50,056 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:50,056 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:50,056 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:50,056 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:50,056 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:50,056 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:50,056 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,056 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,137 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:50,138 DEBUG: Start:	 Training
-2016-09-06 08:19:50,144 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:50,144 DEBUG: Start:	 Training
-2016-09-06 08:19:50,155 DEBUG: Info:	 Time for Training: 0.100615978241[s]
-2016-09-06 08:19:50,155 DEBUG: Done:	 Training
-2016-09-06 08:19:50,155 DEBUG: Start:	 Predicting
-2016-09-06 08:19:50,161 DEBUG: Done:	 Predicting
-2016-09-06 08:19:50,161 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:50,162 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:50,162 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:19:50,163 INFO: Done:	 Result Analysis
-2016-09-06 08:19:50,164 DEBUG: Info:	 Time for Training: 0.109777927399[s]
-2016-09-06 08:19:50,164 DEBUG: Done:	 Training
-2016-09-06 08:19:50,165 DEBUG: Start:	 Predicting
-2016-09-06 08:19:50,169 DEBUG: Done:	 Predicting
-2016-09-06 08:19:50,169 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:50,170 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:50,170 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5574
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:19:50,170 INFO: Done:	 Result Analysis
-2016-09-06 08:19:50,299 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,299 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,300 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:19:50,300 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:19:50,300 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:50,300 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:50,301 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:50,301 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:50,301 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:50,301 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:50,301 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:50,301 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:50,301 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,301 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,364 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:50,365 DEBUG: Start:	 Training
-2016-09-06 08:19:50,367 DEBUG: Info:	 Time for Training: 0.0675768852234[s]
-2016-09-06 08:19:50,367 DEBUG: Done:	 Training
-2016-09-06 08:19:50,367 DEBUG: Start:	 Predicting
-2016-09-06 08:19:50,369 DEBUG: Done:	 Predicting
-2016-09-06 08:19:50,369 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:50,370 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:50,370 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.819047619048
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.819047619048
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:19:50,371 INFO: Done:	 Result Analysis
-2016-09-06 08:19:50,395 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:50,395 DEBUG: Start:	 Training
-2016-09-06 08:19:50,400 DEBUG: Info:	 Time for Training: 0.100778102875[s]
-2016-09-06 08:19:50,400 DEBUG: Done:	 Training
-2016-09-06 08:19:50,400 DEBUG: Start:	 Predicting
-2016-09-06 08:19:50,403 DEBUG: Done:	 Predicting
-2016-09-06 08:19:50,403 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:50,405 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:50,405 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:19:50,405 INFO: Done:	 Result Analysis
-2016-09-06 08:19:50,554 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,554 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:19:50,554 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,554 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:50,555 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:19:50,555 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:50,555 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:50,556 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:50,556 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:50,556 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:50,556 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:50,556 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:50,556 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,556 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,612 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:50,612 DEBUG: Start:	 Training
-2016-09-06 08:19:50,612 DEBUG: Info:	 Time for Training: 0.0590949058533[s]
-2016-09-06 08:19:50,613 DEBUG: Done:	 Training
-2016-09-06 08:19:50,613 DEBUG: Start:	 Predicting
-2016-09-06 08:19:50,620 DEBUG: Done:	 Predicting
-2016-09-06 08:19:50,620 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:50,621 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:50,621 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 30
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:19:50,622 INFO: Done:	 Result Analysis
-2016-09-06 08:19:51,138 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:51,138 DEBUG: Start:	 Training
-2016-09-06 08:19:51,207 DEBUG: Info:	 Time for Training: 0.653259992599[s]
-2016-09-06 08:19:51,207 DEBUG: Done:	 Training
-2016-09-06 08:19:51,207 DEBUG: Start:	 Predicting
-2016-09-06 08:19:51,215 DEBUG: Done:	 Predicting
-2016-09-06 08:19:51,215 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:51,216 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:51,216 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 25, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:19:51,216 INFO: Done:	 Result Analysis
-2016-09-06 08:19:51,302 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:51,302 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:51,302 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:19:51,302 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:19:51,302 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:51,302 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:19:51,303 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:51,303 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:51,303 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:51,303 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:51,303 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:51,303 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:19:51,303 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:51,303 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:19:51,397 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:51,397 DEBUG: Start:	 Training
-2016-09-06 08:19:51,398 DEBUG: Info:	 Time for Training: 0.0974688529968[s]
-2016-09-06 08:19:51,398 DEBUG: Done:	 Training
-2016-09-06 08:19:51,399 DEBUG: Start:	 Predicting
-2016-09-06 08:19:51,415 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:19:51,415 DEBUG: Start:	 Training
-2016-09-06 08:19:51,417 DEBUG: Done:	 Predicting
-2016-09-06 08:19:51,418 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:51,419 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:51,419 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:51,419 INFO: Done:	 Result Analysis
-2016-09-06 08:19:51,437 DEBUG: Info:	 Time for Training: 0.136286020279[s]
-2016-09-06 08:19:51,437 DEBUG: Done:	 Training
-2016-09-06 08:19:51,437 DEBUG: Start:	 Predicting
-2016-09-06 08:19:51,442 DEBUG: Done:	 Predicting
-2016-09-06 08:19:51,442 DEBUG: Start:	 Getting Results
-2016-09-06 08:19:51,443 DEBUG: Done:	 Getting Results
-2016-09-06 08:19:51,444 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:51,444 INFO: Done:	 Result Analysis
-2016-09-06 08:19:51,702 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:19:51,703 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:19:51,703 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 08:19:51,703 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:19:51,703 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:19:51,703 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 08:19:51,704 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 08:19:51,704 INFO: Info:	 Shape of View2 :(300, 16)
-2016-09-06 08:19:51,704 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 08:19:51,704 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 08:19:51,705 INFO: Info:	 Shape of View2 :(300, 16)
-2016-09-06 08:19:51,705 INFO: Done:	 Read Database Files
-2016-09-06 08:19:51,705 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:19:51,705 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 08:19:51,705 INFO: Done:	 Read Database Files
-2016-09-06 08:19:51,706 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:19:51,709 INFO: Done:	 Determine validation split
-2016-09-06 08:19:51,709 INFO: Start:	 Determine 5 folds
-2016-09-06 08:19:51,710 INFO: Done:	 Determine validation split
-2016-09-06 08:19:51,710 INFO: Start:	 Determine 5 folds
-2016-09-06 08:19:51,716 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:19:51,717 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:19:51,717 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:19:51,717 INFO: Done:	 Determine folds
-2016-09-06 08:19:51,717 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:19:51,717 INFO: Start:	 Classification
-2016-09-06 08:19:51,717 INFO: 	Start:	 Fold number 1
-2016-09-06 08:19:51,719 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:19:51,720 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:19:51,720 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:19:51,720 INFO: Done:	 Determine folds
-2016-09-06 08:19:51,720 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 08:19:51,720 INFO: Start:	 Classification
-2016-09-06 08:19:51,720 INFO: 	Start:	 Fold number 1
-2016-09-06 08:19:51,747 INFO: 	Start: 	 Classification
-2016-09-06 08:19:51,753 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:19:51,761 DEBUG: 			View 0 : 0.562162162162
-2016-09-06 08:19:51,769 DEBUG: 			View 1 : 0.545945945946
-2016-09-06 08:19:51,777 DEBUG: 			View 2 : 0.491891891892
-2016-09-06 08:19:51,784 DEBUG: 			View 3 : 0.524324324324
-2016-09-06 08:19:51,784 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:19:51,784 INFO: 	Start:	 Fold number 2
-2016-09-06 08:19:51,816 INFO: 	Start: 	 Classification
-2016-09-06 08:19:51,818 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:51,849 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:19:51,850 INFO: 	Start:	 Fold number 3
-2016-09-06 08:19:51,881 INFO: 	Start: 	 Classification
-2016-09-06 08:19:51,905 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:19:51,911 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:19:51,911 INFO: 	Start:	 Fold number 4
-2016-09-06 08:19:51,912 DEBUG: 			View 0 : 0.691891891892
-2016-09-06 08:19:51,920 DEBUG: 			View 1 : 0.724324324324
-2016-09-06 08:19:51,928 DEBUG: 			View 2 : 0.643243243243
-2016-09-06 08:19:51,936 DEBUG: 			View 3 : 0.632432432432
-2016-09-06 08:19:51,941 INFO: 	Start: 	 Classification
-2016-09-06 08:19:51,972 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:19:51,972 INFO: 	Start:	 Fold number 5
-2016-09-06 08:19:51,975 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:52,002 INFO: 	Start: 	 Classification
-2016-09-06 08:19:52,034 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:19:52,034 INFO: Done:	 Classification
-2016-09-06 08:19:52,035 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:19:52,035 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:19:52,039 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 56.7715963199
-	-On Test : 55.6097560976
-	-On Validation : 55.2808988764
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:19:52,039 INFO: Done:	 Result Analysis
-2016-09-06 08:19:52,138 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:19:52,153 DEBUG: 			View 0 : 0.691891891892
-2016-09-06 08:19:52,161 DEBUG: 			View 1 : 0.724324324324
-2016-09-06 08:19:52,169 DEBUG: 			View 2 : 0.643243243243
-2016-09-06 08:19:52,176 DEBUG: 			View 3 : 0.632432432432
-2016-09-06 08:19:52,219 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:52,442 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:19:52,449 DEBUG: 			View 0 : 0.675675675676
-2016-09-06 08:19:52,457 DEBUG: 			View 1 : 0.627027027027
-2016-09-06 08:19:52,464 DEBUG: 			View 2 : 0.659459459459
-2016-09-06 08:19:52,472 DEBUG: 			View 3 : 0.616216216216
-2016-09-06 08:19:52,517 DEBUG: 			 Best view : 		View0
-2016-09-06 08:19:52,810 INFO: 	Start: 	 Classification
-2016-09-06 08:19:53,293 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:19:53,293 INFO: 	Start:	 Fold number 2
-2016-09-06 08:19:53,323 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:19:53,332 DEBUG: 			View 0 : 0.477272727273
-2016-09-06 08:19:53,340 DEBUG: 			View 1 : 0.585227272727
-2016-09-06 08:19:53,347 DEBUG: 			View 2 : 0.539772727273
-2016-09-06 08:19:53,355 DEBUG: 			View 3 : 0.539772727273
-2016-09-06 08:19:53,387 DEBUG: 			 Best view : 		View0
-2016-09-06 08:19:53,465 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:19:53,472 DEBUG: 			View 0 : 0.727272727273
-2016-09-06 08:19:53,480 DEBUG: 			View 1 : 0.744318181818
-2016-09-06 08:19:53,487 DEBUG: 			View 2 : 0.698863636364
-2016-09-06 08:19:53,494 DEBUG: 			View 3 : 0.630681818182
-2016-09-06 08:19:53,531 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:53,675 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:19:53,682 DEBUG: 			View 0 : 0.727272727273
-2016-09-06 08:19:53,689 DEBUG: 			View 1 : 0.744318181818
-2016-09-06 08:19:53,697 DEBUG: 			View 2 : 0.664772727273
-2016-09-06 08:19:53,704 DEBUG: 			View 3 : 0.653409090909
-2016-09-06 08:19:53,744 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:53,957 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:19:53,964 DEBUG: 			View 0 : 0.659090909091
-2016-09-06 08:19:53,972 DEBUG: 			View 1 : 0.727272727273
-2016-09-06 08:19:53,979 DEBUG: 			View 2 : 0.698863636364
-2016-09-06 08:19:53,986 DEBUG: 			View 3 : 0.653409090909
-2016-09-06 08:19:54,029 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:54,306 INFO: 	Start: 	 Classification
-2016-09-06 08:19:54,773 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:19:54,773 INFO: 	Start:	 Fold number 3
-2016-09-06 08:19:54,805 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:19:54,812 DEBUG: 			View 0 : 0.448087431694
-2016-09-06 08:19:54,819 DEBUG: 			View 1 : 0.448087431694
-2016-09-06 08:19:54,825 DEBUG: 			View 2 : 0.448087431694
-2016-09-06 08:19:54,833 DEBUG: 			View 3 : 0.448087431694
-2016-09-06 08:19:54,833 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 08:19:54,869 DEBUG: 			 Best view : 		View0
-2016-09-06 08:19:54,950 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:19:54,957 DEBUG: 			View 0 : 0.688524590164
-2016-09-06 08:19:54,965 DEBUG: 			View 1 : 0.75956284153
-2016-09-06 08:19:54,973 DEBUG: 			View 2 : 0.672131147541
-2016-09-06 08:19:54,981 DEBUG: 			View 3 : 0.72131147541
-2016-09-06 08:19:55,020 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:55,172 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:19:55,179 DEBUG: 			View 0 : 0.688524590164
-2016-09-06 08:19:55,187 DEBUG: 			View 1 : 0.75956284153
-2016-09-06 08:19:55,195 DEBUG: 			View 2 : 0.672131147541
-2016-09-06 08:19:55,203 DEBUG: 			View 3 : 0.72131147541
-2016-09-06 08:19:55,245 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:55,465 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:19:55,473 DEBUG: 			View 0 : 0.672131147541
-2016-09-06 08:19:55,480 DEBUG: 			View 1 : 0.765027322404
-2016-09-06 08:19:55,488 DEBUG: 			View 2 : 0.639344262295
-2016-09-06 08:19:55,495 DEBUG: 			View 3 : 0.606557377049
-2016-09-06 08:19:55,540 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:55,830 INFO: 	Start: 	 Classification
-2016-09-06 08:19:56,307 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:19:56,307 INFO: 	Start:	 Fold number 4
-2016-09-06 08:19:56,339 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:19:56,345 DEBUG: 			View 0 : 0.537634408602
-2016-09-06 08:19:56,352 DEBUG: 			View 1 : 0.537634408602
-2016-09-06 08:19:56,359 DEBUG: 			View 2 : 0.537634408602
-2016-09-06 08:19:56,365 DEBUG: 			View 3 : 0.537634408602
-2016-09-06 08:19:56,398 DEBUG: 			 Best view : 		View0
-2016-09-06 08:19:56,481 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:19:56,488 DEBUG: 			View 0 : 0.682795698925
-2016-09-06 08:19:56,495 DEBUG: 			View 1 : 0.720430107527
-2016-09-06 08:19:56,503 DEBUG: 			View 2 : 0.661290322581
-2016-09-06 08:19:56,511 DEBUG: 			View 3 : 0.698924731183
-2016-09-06 08:19:56,549 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:56,704 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:19:56,711 DEBUG: 			View 0 : 0.682795698925
-2016-09-06 08:19:56,719 DEBUG: 			View 1 : 0.720430107527
-2016-09-06 08:19:56,726 DEBUG: 			View 2 : 0.661290322581
-2016-09-06 08:19:56,734 DEBUG: 			View 3 : 0.698924731183
-2016-09-06 08:19:56,776 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:57,000 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:19:57,008 DEBUG: 			View 0 : 0.731182795699
-2016-09-06 08:19:57,015 DEBUG: 			View 1 : 0.677419354839
-2016-09-06 08:19:57,023 DEBUG: 			View 2 : 0.655913978495
-2016-09-06 08:19:57,031 DEBUG: 			View 3 : 0.688172043011
-2016-09-06 08:19:57,075 DEBUG: 			 Best view : 		View0
-2016-09-06 08:19:57,369 INFO: 	Start: 	 Classification
-2016-09-06 08:19:57,852 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:19:57,852 INFO: 	Start:	 Fold number 5
-2016-09-06 08:19:57,882 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:19:57,889 DEBUG: 			View 0 : 0.541436464088
-2016-09-06 08:19:57,896 DEBUG: 			View 1 : 0.541436464088
-2016-09-06 08:19:57,903 DEBUG: 			View 2 : 0.541436464088
-2016-09-06 08:19:57,910 DEBUG: 			View 3 : 0.541436464088
-2016-09-06 08:19:57,941 DEBUG: 			 Best view : 		View0
-2016-09-06 08:19:58,021 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:19:58,029 DEBUG: 			View 0 : 0.685082872928
-2016-09-06 08:19:58,036 DEBUG: 			View 1 : 0.696132596685
-2016-09-06 08:19:58,044 DEBUG: 			View 2 : 0.701657458564
-2016-09-06 08:19:58,052 DEBUG: 			View 3 : 0.668508287293
-2016-09-06 08:19:58,090 DEBUG: 			 Best view : 		View2
-2016-09-06 08:19:58,240 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:19:58,247 DEBUG: 			View 0 : 0.685082872928
-2016-09-06 08:19:58,254 DEBUG: 			View 1 : 0.696132596685
-2016-09-06 08:19:58,262 DEBUG: 			View 2 : 0.701657458564
-2016-09-06 08:19:58,269 DEBUG: 			View 3 : 0.668508287293
-2016-09-06 08:19:58,310 DEBUG: 			 Best view : 		View2
-2016-09-06 08:19:58,531 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:19:58,538 DEBUG: 			View 0 : 0.662983425414
-2016-09-06 08:19:58,546 DEBUG: 			View 1 : 0.707182320442
-2016-09-06 08:19:58,553 DEBUG: 			View 2 : 0.618784530387
-2016-09-06 08:19:58,561 DEBUG: 			View 3 : 0.690607734807
-2016-09-06 08:19:58,605 DEBUG: 			 Best view : 		View1
-2016-09-06 08:19:58,894 INFO: 	Start: 	 Classification
-2016-09-06 08:19:59,367 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:19:59,367 INFO: Done:	 Classification
-2016-09-06 08:19:59,367 INFO: Info:	 Time for Classification: 7[s]
-2016-09-06 08:19:59,367 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 08:20:01,913 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 73.0058582753
-	-On Test : 48.7804878049
-	-On Validation : 68.0898876404Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 8), View1 of shape (300, 16), View2 of shape (300, 16), View3 of shape (300, 18)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:07        0:00:00
-	          Total        0:00:20        0:00:02
-	So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.262162162162
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.262162162162
-			- Percentage of time chosen : 0.3
-		- On View2 : 
-			- Mean average Accuracy : 0.243783783784
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.240540540541
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.259090909091
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.280113636364
-			- Percentage of time chosen : 0.3
-		- On View2 : 
-			- Mean average Accuracy : 0.260227272727
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.247727272727
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.249726775956
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.273224043716
-			- Percentage of time chosen : 0.3
-		- On View2 : 
-			- Mean average Accuracy : 0.243169398907
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.249726775956
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.263440860215
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.265591397849
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.251612903226
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.262365591398
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.257458563536
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.26408839779
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.25635359116
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.256906077348
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 46.4864864865
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View1
-		 Fold 2
-			Accuracy on train : 44.8863636364
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 44.8087431694
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 46.2365591398
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 45.8563535912
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 72.4324324324
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.6741573034
-			Selected View : View1
-		 Fold 2
-			Accuracy on train : 74.4318181818
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.7865168539
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.956284153
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 72.0430107527
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.2921348315
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 70.1657458564
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View2
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 72.4324324324
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.6741573034
-			Selected View : View1
-		 Fold 2
-			Accuracy on train : 74.4318181818
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.7865168539
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.956284153
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 72.0430107527
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.2921348315
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 70.1657458564
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View2
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 72.4324324324
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.6741573034
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 74.4318181818
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.7865168539
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.956284153
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 72.0430107527
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.2921348315
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 70.1657458564
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View1
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 46.4864864865
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 44.8863636364
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 44.8087431694
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 46.2365591398
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 45.8563535912
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-2016-09-06 08:20:02,113 INFO: Done:	 Result Analysis
-2016-09-06 08:20:02,265 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:02,265 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:02,265 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 08:20:02,266 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:02,266 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:02,266 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 08:20:02,266 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 08:20:02,266 INFO: Info:	 Shape of View2 :(300, 16)
-2016-09-06 08:20:02,267 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 08:20:02,267 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 08:20:02,267 INFO: Done:	 Read Database Files
-2016-09-06 08:20:02,267 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:20:02,267 INFO: Info:	 Shape of View2 :(300, 16)
-2016-09-06 08:20:02,268 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 08:20:02,268 INFO: Done:	 Read Database Files
-2016-09-06 08:20:02,268 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:20:02,271 INFO: Done:	 Determine validation split
-2016-09-06 08:20:02,271 INFO: Start:	 Determine 5 folds
-2016-09-06 08:20:02,272 INFO: Done:	 Determine validation split
-2016-09-06 08:20:02,272 INFO: Start:	 Determine 5 folds
-2016-09-06 08:20:02,278 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:20:02,278 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:20:02,278 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:20:02,278 INFO: Done:	 Determine folds
-2016-09-06 08:20:02,278 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:20:02,278 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:20:02,278 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:20:02,278 INFO: Start:	 Classification
-2016-09-06 08:20:02,278 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:20:02,278 INFO: 	Start:	 Fold number 1
-2016-09-06 08:20:02,279 INFO: Done:	 Determine folds
-2016-09-06 08:20:02,279 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:20:02,279 INFO: Start:	 Classification
-2016-09-06 08:20:02,279 INFO: 	Start:	 Fold number 1
-2016-09-06 08:20:02,310 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,337 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,368 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:20:02,368 INFO: 	Start:	 Fold number 2
-2016-09-06 08:20:02,390 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:20:02,391 INFO: 	Start:	 Fold number 2
-2016-09-06 08:20:02,415 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,419 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,446 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:20:02,446 INFO: 	Start:	 Fold number 3
-2016-09-06 08:20:02,492 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,493 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:20:02,493 INFO: 	Start:	 Fold number 3
-2016-09-06 08:20:02,520 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,522 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:20:02,522 INFO: 	Start:	 Fold number 4
-2016-09-06 08:20:02,567 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,593 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:20:02,593 INFO: 	Start:	 Fold number 4
-2016-09-06 08:20:02,597 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:20:02,597 INFO: 	Start:	 Fold number 5
-2016-09-06 08:20:02,621 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,643 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,675 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:20:02,675 INFO: Done:	 Classification
-2016-09-06 08:20:02,675 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:20:02,675 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:20:02,680 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 58.9879948287
-	-On Test : 55.1219512195
-	-On Validation : 57.3033707865
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:02,680 INFO: Done:	 Result Analysis
-2016-09-06 08:20:02,694 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:20:02,694 INFO: 	Start:	 Fold number 5
-2016-09-06 08:20:02,720 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,792 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:20:02,792 INFO: Done:	 Classification
-2016-09-06 08:20:02,792 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:20:02,792 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:20:02,796 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 54.932173067
-	-On Test : 55.6097560976
-	-On Validation : 57.3033707865
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:02,796 INFO: Done:	 Result Analysis
-2016-09-06 08:20:02,918 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:02,918 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:02,918 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:02,919 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:02,919 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 08:20:02,919 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 08:20:02,920 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 08:20:02,920 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 08:20:02,921 INFO: Info:	 Shape of View2 :(300, 16)
-2016-09-06 08:20:02,921 INFO: Info:	 Shape of View2 :(300, 16)
-2016-09-06 08:20:02,921 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 08:20:02,921 INFO: Done:	 Read Database Files
-2016-09-06 08:20:02,921 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 08:20:02,922 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:20:02,922 INFO: Done:	 Read Database Files
-2016-09-06 08:20:02,922 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:20:02,926 INFO: Done:	 Determine validation split
-2016-09-06 08:20:02,926 INFO: Done:	 Determine validation split
-2016-09-06 08:20:02,926 INFO: Start:	 Determine 5 folds
-2016-09-06 08:20:02,926 INFO: Start:	 Determine 5 folds
-2016-09-06 08:20:02,932 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:20:02,932 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:20:02,932 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:20:02,933 INFO: Done:	 Determine folds
-2016-09-06 08:20:02,933 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:20:02,933 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:20:02,933 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:20:02,933 INFO: Start:	 Classification
-2016-09-06 08:20:02,933 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:20:02,933 INFO: 	Start:	 Fold number 1
-2016-09-06 08:20:02,933 INFO: Done:	 Determine folds
-2016-09-06 08:20:02,933 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:20:02,933 INFO: Start:	 Classification
-2016-09-06 08:20:02,934 INFO: 	Start:	 Fold number 1
-2016-09-06 08:20:02,961 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,964 INFO: 	Start: 	 Classification
-2016-09-06 08:20:02,989 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:20:02,990 INFO: 	Start:	 Fold number 2
-2016-09-06 08:20:03,014 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:20:03,014 INFO: 	Start:	 Fold number 2
-2016-09-06 08:20:03,015 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,040 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:20:03,040 INFO: 	Start:	 Fold number 3
-2016-09-06 08:20:03,043 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,066 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,077 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:20:03,077 INFO: 	Start:	 Fold number 3
-2016-09-06 08:20:03,094 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:20:03,094 INFO: 	Start:	 Fold number 4
-2016-09-06 08:20:03,107 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,120 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,141 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:20:03,141 INFO: 	Start:	 Fold number 4
-2016-09-06 08:20:03,147 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:20:03,147 INFO: 	Start:	 Fold number 5
-2016-09-06 08:20:03,169 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,173 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,199 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:20:03,199 INFO: Done:	 Classification
-2016-09-06 08:20:03,199 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:20:03,199 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:20:03,201 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:20:03,202 INFO: 	Start:	 Fold number 5
-2016-09-06 08:20:03,203 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 54.6341463415
-	-On Validation : 84.7191011236
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:03,203 INFO: Done:	 Result Analysis
-2016-09-06 08:20:03,228 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,259 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:20:03,259 INFO: Done:	 Classification
-2016-09-06 08:20:03,259 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:20:03,259 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:20:03,264 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 31.9649680433
-	-On Test : 36.0975609756
-	-On Validation : 31.2359550562
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:03,264 INFO: Done:	 Result Analysis
-2016-09-06 08:20:03,365 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:03,365 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:03,365 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:03,365 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 08:20:03,366 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:03,366 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 08:20:03,366 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 08:20:03,366 INFO: Info:	 Shape of View2 :(300, 16)
-2016-09-06 08:20:03,367 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 08:20:03,367 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 08:20:03,367 INFO: Done:	 Read Database Files
-2016-09-06 08:20:03,367 INFO: Info:	 Shape of View2 :(300, 16)
-2016-09-06 08:20:03,367 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:20:03,368 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 08:20:03,368 INFO: Done:	 Read Database Files
-2016-09-06 08:20:03,368 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:20:03,371 INFO: Done:	 Determine validation split
-2016-09-06 08:20:03,371 INFO: Start:	 Determine 5 folds
-2016-09-06 08:20:03,371 INFO: Done:	 Determine validation split
-2016-09-06 08:20:03,372 INFO: Start:	 Determine 5 folds
-2016-09-06 08:20:03,378 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:20:03,378 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:20:03,378 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:20:03,378 INFO: Done:	 Determine folds
-2016-09-06 08:20:03,379 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:20:03,379 INFO: Start:	 Classification
-2016-09-06 08:20:03,379 INFO: 	Start:	 Fold number 1
-2016-09-06 08:20:03,380 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:20:03,380 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:20:03,380 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:20:03,380 INFO: Done:	 Determine folds
-2016-09-06 08:20:03,381 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:20:03,381 INFO: Start:	 Classification
-2016-09-06 08:20:03,381 INFO: 	Start:	 Fold number 1
-2016-09-06 08:20:03,398 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,403 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,429 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:20:03,430 INFO: 	Start:	 Fold number 2
-2016-09-06 08:20:03,432 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:20:03,432 INFO: 	Start:	 Fold number 2
-2016-09-06 08:20:03,447 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,453 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,477 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:20:03,477 INFO: 	Start:	 Fold number 3
-2016-09-06 08:20:03,483 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:20:03,483 INFO: 	Start:	 Fold number 3
-2016-09-06 08:20:03,495 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,503 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,527 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:20:03,527 INFO: 	Start:	 Fold number 4
-2016-09-06 08:20:03,533 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:20:03,533 INFO: 	Start:	 Fold number 4
-2016-09-06 08:20:03,544 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,555 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,575 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:20:03,575 INFO: 	Start:	 Fold number 5
-2016-09-06 08:20:03,585 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:20:03,585 INFO: 	Start:	 Fold number 5
-2016-09-06 08:20:03,592 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,605 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,622 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:20:03,622 INFO: Done:	 Classification
-2016-09-06 08:20:03,623 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:20:03,623 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:20:03,627 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 52.1951219512
-	-On Validation : 81.797752809
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- K nearest Neighbors with  n_neighbors: 1.0
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:03,627 INFO: Done:	 Result Analysis
-2016-09-06 08:20:03,633 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:20:03,633 INFO: Done:	 Classification
-2016-09-06 08:20:03,633 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:20:03,633 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:20:03,638 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 59.6237187794
-	-On Test : 49.756097561
-	-On Validation : 57.5280898876
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Decision Tree with max_depth : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:03,638 INFO: Done:	 Result Analysis
-2016-09-06 08:20:03,717 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:03,717 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:03,718 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 08:20:03,718 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:03,718 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 08:20:03,718 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:03,719 INFO: Info:	 Shape of View2 :(300, 16)
-2016-09-06 08:20:03,719 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 08:20:03,719 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 08:20:03,719 INFO: Done:	 Read Database Files
-2016-09-06 08:20:03,719 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:20:03,720 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 08:20:03,721 INFO: Info:	 Shape of View2 :(300, 16)
-2016-09-06 08:20:03,722 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 08:20:03,722 INFO: Done:	 Read Database Files
-2016-09-06 08:20:03,722 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:20:03,724 INFO: Done:	 Determine validation split
-2016-09-06 08:20:03,724 INFO: Start:	 Determine 5 folds
-2016-09-06 08:20:03,729 INFO: Done:	 Determine validation split
-2016-09-06 08:20:03,729 INFO: Start:	 Determine 5 folds
-2016-09-06 08:20:03,732 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:20:03,732 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:20:03,733 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:20:03,733 INFO: Done:	 Determine folds
-2016-09-06 08:20:03,733 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:20:03,733 INFO: Start:	 Classification
-2016-09-06 08:20:03,733 INFO: 	Start:	 Fold number 1
-2016-09-06 08:20:03,743 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:20:03,743 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:20:03,743 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:20:03,743 INFO: Done:	 Determine folds
-2016-09-06 08:20:03,743 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:20:03,744 INFO: Start:	 Classification
-2016-09-06 08:20:03,744 INFO: 	Start:	 Fold number 1
-2016-09-06 08:20:03,756 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,805 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:20:03,805 INFO: 	Start:	 Fold number 2
-2016-09-06 08:20:03,835 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,866 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,906 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:20:03,906 INFO: 	Start:	 Fold number 3
-2016-09-06 08:20:03,921 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:20:03,921 INFO: 	Start:	 Fold number 2
-2016-09-06 08:20:03,941 INFO: 	Start: 	 Classification
-2016-09-06 08:20:03,991 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:20:03,992 INFO: 	Start:	 Fold number 4
-2016-09-06 08:20:04,026 INFO: 	Start: 	 Classification
-2016-09-06 08:20:04,074 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:20:04,074 INFO: 	Start:	 Fold number 5
-2016-09-06 08:20:04,081 INFO: 	Start: 	 Classification
-2016-09-06 08:20:04,106 INFO: 	Start: 	 Classification
-2016-09-06 08:20:04,139 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:20:04,140 INFO: 	Start:	 Fold number 3
-2016-09-06 08:20:04,154 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:20:04,155 INFO: Done:	 Classification
-2016-09-06 08:20:04,155 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:20:04,155 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:20:04,163 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 70.4896592488
-	-On Test : 49.756097561
-	-On Validation : 67.6404494382
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:04,163 INFO: Done:	 Result Analysis
-2016-09-06 08:20:04,252 INFO: 	Start: 	 Classification
-2016-09-06 08:20:04,292 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:20:04,292 INFO: 	Start:	 Fold number 4
-2016-09-06 08:20:04,403 INFO: 	Start: 	 Classification
-2016-09-06 08:20:04,438 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:20:04,438 INFO: 	Start:	 Fold number 5
-2016-09-06 08:20:04,525 INFO: 	Start: 	 Classification
-2016-09-06 08:20:04,559 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:20:04,559 INFO: Done:	 Classification
-2016-09-06 08:20:04,559 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:20:04,559 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:20:04,564 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 98.9068726244
-	-On Test : 50.243902439
-	-On Validation : 84.9438202247
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:04,564 INFO: Done:	 Result Analysis
-2016-09-06 08:20:04,667 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:04,668 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:04,668 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 08:20:04,669 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 08:20:04,669 INFO: Info:	 Shape of View2 :(300, 16)
-2016-09-06 08:20:04,670 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 08:20:04,670 INFO: Done:	 Read Database Files
-2016-09-06 08:20:04,671 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:20:04,676 INFO: Done:	 Determine validation split
-2016-09-06 08:20:04,676 INFO: Start:	 Determine 5 folds
-2016-09-06 08:20:04,683 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 08:20:04,683 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 08:20:04,683 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 08:20:04,683 INFO: Done:	 Determine folds
-2016-09-06 08:20:04,684 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:20:04,684 INFO: Start:	 Classification
-2016-09-06 08:20:04,684 INFO: 	Start:	 Fold number 1
-2016-09-06 08:20:04,725 INFO: 	Start: 	 Classification
-2016-09-06 08:20:04,755 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:20:04,755 INFO: 	Start:	 Fold number 2
-2016-09-06 08:20:04,798 INFO: 	Start: 	 Classification
-2016-09-06 08:20:04,833 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:20:04,833 INFO: 	Start:	 Fold number 3
-2016-09-06 08:20:04,874 INFO: 	Start: 	 Classification
-2016-09-06 08:20:04,907 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:20:04,907 INFO: 	Start:	 Fold number 4
-2016-09-06 08:20:04,947 INFO: 	Start: 	 Classification
-2016-09-06 08:20:04,979 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:20:04,979 INFO: 	Start:	 Fold number 5
-2016-09-06 08:20:05,014 INFO: 	Start: 	 Classification
-2016-09-06 08:20:05,056 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:20:05,057 INFO: Done:	 Classification
-2016-09-06 08:20:05,057 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:20:05,057 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:20:05,062 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 77.7279485987
-	-On Test : 47.8048780488
-	-On Validation : 70.1123595506
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SVM Linear with C : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:05,063 INFO: Done:	 Result Analysis
-2016-09-06 08:20:05,318 DEBUG: Start:	 Deleting 2 temporary datasets for multiprocessing
-2016-09-06 08:20:05,319 DEBUG: Start:	 Deleting datasets for multiprocessing
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1612f21c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1c35c0ee..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.847619047619
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.847619047619
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9b99cb3b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0b4dfa5e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 25, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index de87038f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0597ef41..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e89e6124..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 581b506b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.790476190476
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.790476190476
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 575ec7ab..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 30
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 97b41d5b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a38dc962..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0ce94693..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 70684b07..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.785714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.785714285714
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b62ada2b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 25, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1bd958e2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 66681f80..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6e8a8b21..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5574
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 30604daa..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cc7e6837..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8fee3976..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cf3d4588..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0f90ba1b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ea57a76b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8ff0bf29..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.819047619048
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 5
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.819047619048
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7e46e3e5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 30
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2455c24b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5574
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a3e078d9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8917d3eb..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 25, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d4c5de22..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5c1ebfae..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7056
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081952Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081952Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index c2b439ff..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081952Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 56.7715963199
-	-On Test : 55.6097560976
-	-On Validation : 55.2808988764
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082001Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-082001Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082001Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082001Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5f9c24d4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082001Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,235 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 73.0058582753
-	-On Test : 48.7804878049
-	-On Validation : 68.0898876404Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 8), View1 of shape (300, 16), View2 of shape (300, 16), View3 of shape (300, 18)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:07        0:00:00
-	          Total        0:00:20        0:00:02
-	So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.262162162162
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.262162162162
-			- Percentage of time chosen : 0.3
-		- On View2 : 
-			- Mean average Accuracy : 0.243783783784
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.240540540541
-			- Percentage of time chosen : 0.0
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.259090909091
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.280113636364
-			- Percentage of time chosen : 0.3
-		- On View2 : 
-			- Mean average Accuracy : 0.260227272727
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.247727272727
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.249726775956
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.273224043716
-			- Percentage of time chosen : 0.3
-		- On View2 : 
-			- Mean average Accuracy : 0.243169398907
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.249726775956
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.263440860215
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.265591397849
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.251612903226
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.262365591398
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.257458563536
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.26408839779
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.25635359116
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.256906077348
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 46.4864864865
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View1
-		 Fold 2
-			Accuracy on train : 44.8863636364
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 44.8087431694
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 46.2365591398
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 45.8563535912
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 72.4324324324
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.6741573034
-			Selected View : View1
-		 Fold 2
-			Accuracy on train : 74.4318181818
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.7865168539
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.956284153
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 72.0430107527
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.2921348315
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 70.1657458564
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View2
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 72.4324324324
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.6741573034
-			Selected View : View1
-		 Fold 2
-			Accuracy on train : 74.4318181818
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.7865168539
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.956284153
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 72.0430107527
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.2921348315
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 70.1657458564
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View2
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 72.4324324324
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.6741573034
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 74.4318181818
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.7865168539
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.956284153
-			Accuracy on test : 0.0
-			Accuracy on validation : 67.4157303371
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 72.0430107527
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.2921348315
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 70.1657458564
-			Accuracy on test : 0.0
-			Accuracy on validation : 75.2808988764
-			Selected View : View1
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 46.4864864865
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 44.8863636364
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 44.8087431694
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 46.2365591398
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 45.8563535912
-			Accuracy on test : 0.0
-			Accuracy on validation : 43.8202247191
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index ee39e0b3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 54.932173067
-	-On Test : 55.6097560976
-	-On Validation : 57.3033707865
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 32117b2b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 58.9879948287
-	-On Test : 55.1219512195
-	-On Validation : 57.3033707865
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 48aa919d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 54.6341463415
-	-On Validation : 84.7191011236
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 4de9aa3d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 59.6237187794
-	-On Test : 49.756097561
-	-On Validation : 57.5280898876
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Decision Tree with max_depth : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 8198fc8a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 52.1951219512
-	-On Validation : 81.797752809
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- K nearest Neighbors with  n_neighbors: 1.0
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index c17fa56c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 31.9649680433
-	-On Test : 36.0975609756
-	-On Validation : 31.2359550562
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 2a8fb89b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 98.9068726244
-	-On Test : 50.243902439
-	-On Validation : 84.9438202247
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index be905ac8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 70.4896592488
-	-On Test : 49.756097561
-	-On Validation : 67.6404494382
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082005Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082005Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3c818560..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082005Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 77.7279485987
-	-On Test : 47.8048780488
-	-On Validation : 70.1123595506
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SVM Linear with C : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082145-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-082145-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index a34af1e4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082145-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2265 +0,0 @@
-2016-09-06 08:21:45,928 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:21:45,928 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00013571875 Gbytes /!\ 
-2016-09-06 08:21:50,942 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 08:21:50,945 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 08:21:50,999 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:50,999 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:50,999 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:21:50,999 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:21:50,999 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:50,999 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:51,000 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,000 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,000 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,000 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,001 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:51,001 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:51,001 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,001 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,064 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:51,064 DEBUG: Start:	 Training
-2016-09-06 08:21:51,067 DEBUG: Info:	 Time for Training: 0.068393945694[s]
-2016-09-06 08:21:51,067 DEBUG: Done:	 Training
-2016-09-06 08:21:51,067 DEBUG: Start:	 Predicting
-2016-09-06 08:21:51,069 DEBUG: Done:	 Predicting
-2016-09-06 08:21:51,070 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:51,071 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:51,071 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,071 INFO: Done:	 Result Analysis
-2016-09-06 08:21:51,106 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:51,106 DEBUG: Start:	 Training
-2016-09-06 08:21:51,111 DEBUG: Info:	 Time for Training: 0.112962007523[s]
-2016-09-06 08:21:51,111 DEBUG: Done:	 Training
-2016-09-06 08:21:51,111 DEBUG: Start:	 Predicting
-2016-09-06 08:21:51,114 DEBUG: Done:	 Predicting
-2016-09-06 08:21:51,114 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:51,115 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:51,116 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,116 INFO: Done:	 Result Analysis
-2016-09-06 08:21:51,252 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:51,252 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:21:51,252 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:51,252 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:51,253 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:21:51,253 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:51,254 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,254 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,254 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,254 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:51,254 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,254 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,254 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:51,254 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,340 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:51,341 DEBUG: Start:	 Training
-2016-09-06 08:21:51,342 DEBUG: Info:	 Time for Training: 0.0906808376312[s]
-2016-09-06 08:21:51,342 DEBUG: Done:	 Training
-2016-09-06 08:21:51,342 DEBUG: Start:	 Predicting
-2016-09-06 08:21:51,354 DEBUG: Done:	 Predicting
-2016-09-06 08:21:51,354 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:51,356 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:51,356 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.642857142857
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,356 INFO: Done:	 Result Analysis
-2016-09-06 08:21:51,671 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:51,671 DEBUG: Start:	 Training
-2016-09-06 08:21:51,698 DEBUG: Info:	 Time for Training: 0.446767807007[s]
-2016-09-06 08:21:51,698 DEBUG: Done:	 Training
-2016-09-06 08:21:51,698 DEBUG: Start:	 Predicting
-2016-09-06 08:21:51,702 DEBUG: Done:	 Predicting
-2016-09-06 08:21:51,703 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:51,704 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:51,704 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 10, max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,704 INFO: Done:	 Result Analysis
-2016-09-06 08:21:51,794 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:51,794 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:51,794 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:21:51,794 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:21:51,794 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:51,794 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:51,795 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,795 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,795 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,795 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,795 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:51,795 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:51,795 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,795 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,891 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:51,892 DEBUG: Start:	 Training
-2016-09-06 08:21:51,893 DEBUG: Info:	 Time for Training: 0.0996568202972[s]
-2016-09-06 08:21:51,893 DEBUG: Done:	 Training
-2016-09-06 08:21:51,893 DEBUG: Start:	 Predicting
-2016-09-06 08:21:51,895 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:51,896 DEBUG: Start:	 Training
-2016-09-06 08:21:51,919 DEBUG: Info:	 Time for Training: 0.125552892685[s]
-2016-09-06 08:21:51,919 DEBUG: Done:	 Training
-2016-09-06 08:21:51,919 DEBUG: Start:	 Predicting
-2016-09-06 08:21:51,921 DEBUG: Done:	 Predicting
-2016-09-06 08:21:51,922 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:51,923 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:51,923 DEBUG: Done:	 Predicting
-2016-09-06 08:21:51,923 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,923 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:51,923 INFO: Done:	 Result Analysis
-2016-09-06 08:21:51,924 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:51,924 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.457142857143
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,924 INFO: Done:	 Result Analysis
-2016-09-06 08:21:52,041 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,041 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,041 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:21:52,041 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:52,041 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:21:52,042 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:52,042 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:52,042 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:52,042 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:52,042 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:52,042 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:52,042 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:52,042 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,042 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,126 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:52,126 DEBUG: Start:	 Training
-2016-09-06 08:21:52,141 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:52,141 DEBUG: Start:	 Training
-2016-09-06 08:21:52,146 DEBUG: Info:	 Time for Training: 0.105118989944[s]
-2016-09-06 08:21:52,146 DEBUG: Done:	 Training
-2016-09-06 08:21:52,146 DEBUG: Start:	 Predicting
-2016-09-06 08:21:52,153 DEBUG: Done:	 Predicting
-2016-09-06 08:21:52,153 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:52,154 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:52,154 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,154 INFO: Done:	 Result Analysis
-2016-09-06 08:21:52,161 DEBUG: Info:	 Time for Training: 0.120436906815[s]
-2016-09-06 08:21:52,161 DEBUG: Done:	 Training
-2016-09-06 08:21:52,161 DEBUG: Start:	 Predicting
-2016-09-06 08:21:52,166 DEBUG: Done:	 Predicting
-2016-09-06 08:21:52,166 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:52,167 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:52,167 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8526
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,167 INFO: Done:	 Result Analysis
-2016-09-06 08:21:52,284 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,284 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,284 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:21:52,284 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:21:52,284 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:52,284 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:52,285 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:52,285 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:52,285 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:52,285 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:52,285 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:52,285 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:52,285 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,285 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,339 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:52,339 DEBUG: Start:	 Training
-2016-09-06 08:21:52,341 DEBUG: Info:	 Time for Training: 0.0580468177795[s]
-2016-09-06 08:21:52,341 DEBUG: Done:	 Training
-2016-09-06 08:21:52,341 DEBUG: Start:	 Predicting
-2016-09-06 08:21:52,345 DEBUG: Done:	 Predicting
-2016-09-06 08:21:52,345 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:52,347 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:52,347 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,347 INFO: Done:	 Result Analysis
-2016-09-06 08:21:52,369 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:52,369 DEBUG: Start:	 Training
-2016-09-06 08:21:52,373 DEBUG: Info:	 Time for Training: 0.0902559757233[s]
-2016-09-06 08:21:52,373 DEBUG: Done:	 Training
-2016-09-06 08:21:52,374 DEBUG: Start:	 Predicting
-2016-09-06 08:21:52,377 DEBUG: Done:	 Predicting
-2016-09-06 08:21:52,377 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:52,378 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:52,378 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,379 INFO: Done:	 Result Analysis
-2016-09-06 08:21:52,529 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,529 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,529 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:21:52,529 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:21:52,529 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:52,529 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:52,530 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:52,530 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:52,530 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:52,530 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:52,530 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:52,530 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:52,530 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,530 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,584 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:52,584 DEBUG: Start:	 Training
-2016-09-06 08:21:52,584 DEBUG: Info:	 Time for Training: 0.0556619167328[s]
-2016-09-06 08:21:52,584 DEBUG: Done:	 Training
-2016-09-06 08:21:52,584 DEBUG: Start:	 Predicting
-2016-09-06 08:21:52,592 DEBUG: Done:	 Predicting
-2016-09-06 08:21:52,592 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:52,593 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:52,593 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,593 INFO: Done:	 Result Analysis
-2016-09-06 08:21:52,896 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:52,896 DEBUG: Start:	 Training
-2016-09-06 08:21:52,923 DEBUG: Info:	 Time for Training: 0.394659996033[s]
-2016-09-06 08:21:52,923 DEBUG: Done:	 Training
-2016-09-06 08:21:52,924 DEBUG: Start:	 Predicting
-2016-09-06 08:21:52,928 DEBUG: Done:	 Predicting
-2016-09-06 08:21:52,928 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:52,929 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:52,929 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 10, max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,929 INFO: Done:	 Result Analysis
-2016-09-06 08:21:53,082 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,082 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,082 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:21:53,082 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:21:53,083 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:53,083 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:53,083 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:53,083 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:53,083 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:53,083 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:53,083 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:53,083 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:53,083 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,083 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,174 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:53,174 DEBUG: Start:	 Training
-2016-09-06 08:21:53,175 DEBUG: Info:	 Time for Training: 0.09352684021[s]
-2016-09-06 08:21:53,175 DEBUG: Done:	 Training
-2016-09-06 08:21:53,175 DEBUG: Start:	 Predicting
-2016-09-06 08:21:53,192 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:53,192 DEBUG: Start:	 Training
-2016-09-06 08:21:53,197 DEBUG: Done:	 Predicting
-2016-09-06 08:21:53,197 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:53,198 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:53,199 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,199 INFO: Done:	 Result Analysis
-2016-09-06 08:21:53,211 DEBUG: Info:	 Time for Training: 0.129293203354[s]
-2016-09-06 08:21:53,211 DEBUG: Done:	 Training
-2016-09-06 08:21:53,211 DEBUG: Start:	 Predicting
-2016-09-06 08:21:53,214 DEBUG: Done:	 Predicting
-2016-09-06 08:21:53,215 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:53,216 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:53,216 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,216 INFO: Done:	 Result Analysis
-2016-09-06 08:21:53,328 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,328 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,328 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:21:53,328 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:21:53,328 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:53,328 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:53,329 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:53,329 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:53,329 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:53,329 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:53,329 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:53,329 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:53,329 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,329 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,427 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:53,428 DEBUG: Start:	 Training
-2016-09-06 08:21:53,436 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:53,437 DEBUG: Start:	 Training
-2016-09-06 08:21:53,453 DEBUG: Info:	 Time for Training: 0.125381946564[s]
-2016-09-06 08:21:53,453 DEBUG: Done:	 Training
-2016-09-06 08:21:53,453 DEBUG: Start:	 Predicting
-2016-09-06 08:21:53,461 DEBUG: Done:	 Predicting
-2016-09-06 08:21:53,461 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:53,462 DEBUG: Info:	 Time for Training: 0.134202957153[s]
-2016-09-06 08:21:53,462 DEBUG: Done:	 Training
-2016-09-06 08:21:53,462 DEBUG: Start:	 Predicting
-2016-09-06 08:21:53,463 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:53,463 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,463 INFO: Done:	 Result Analysis
-2016-09-06 08:21:53,467 DEBUG: Done:	 Predicting
-2016-09-06 08:21:53,468 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:53,469 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:53,469 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,470 INFO: Done:	 Result Analysis
-2016-09-06 08:21:53,586 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,586 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:21:53,586 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:53,586 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,587 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:53,587 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:53,587 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:21:53,587 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:53,587 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,587 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:53,588 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:53,588 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:53,588 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:53,588 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,660 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:53,660 DEBUG: Start:	 Training
-2016-09-06 08:21:53,663 DEBUG: Info:	 Time for Training: 0.0775690078735[s]
-2016-09-06 08:21:53,663 DEBUG: Done:	 Training
-2016-09-06 08:21:53,663 DEBUG: Start:	 Predicting
-2016-09-06 08:21:53,666 DEBUG: Done:	 Predicting
-2016-09-06 08:21:53,666 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:53,667 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:53,667 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,667 INFO: Done:	 Result Analysis
-2016-09-06 08:21:53,716 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:53,717 DEBUG: Start:	 Training
-2016-09-06 08:21:53,724 DEBUG: Info:	 Time for Training: 0.138279914856[s]
-2016-09-06 08:21:53,724 DEBUG: Done:	 Training
-2016-09-06 08:21:53,724 DEBUG: Start:	 Predicting
-2016-09-06 08:21:53,729 DEBUG: Done:	 Predicting
-2016-09-06 08:21:53,729 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:53,731 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:53,731 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,732 INFO: Done:	 Result Analysis
-2016-09-06 08:21:53,836 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,837 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,837 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:21:53,837 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:21:53,837 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:53,838 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:53,838 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:53,838 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:53,838 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:53,839 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:53,839 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,839 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:53,839 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:53,839 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,895 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:53,895 DEBUG: Start:	 Training
-2016-09-06 08:21:53,895 DEBUG: Info:	 Time for Training: 0.0599460601807[s]
-2016-09-06 08:21:53,896 DEBUG: Done:	 Training
-2016-09-06 08:21:53,896 DEBUG: Start:	 Predicting
-2016-09-06 08:21:53,904 DEBUG: Done:	 Predicting
-2016-09-06 08:21:53,904 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:53,905 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:53,905 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,905 INFO: Done:	 Result Analysis
-2016-09-06 08:21:54,238 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:54,238 DEBUG: Start:	 Training
-2016-09-06 08:21:54,266 DEBUG: Info:	 Time for Training: 0.42955493927[s]
-2016-09-06 08:21:54,266 DEBUG: Done:	 Training
-2016-09-06 08:21:54,266 DEBUG: Start:	 Predicting
-2016-09-06 08:21:54,270 DEBUG: Done:	 Predicting
-2016-09-06 08:21:54,271 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:54,272 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:54,272 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 10, max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,272 INFO: Done:	 Result Analysis
-2016-09-06 08:21:54,378 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,378 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,379 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:21:54,379 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:21:54,379 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:54,379 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:54,380 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:54,380 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:54,380 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:54,380 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:54,380 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:54,380 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:54,380 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,380 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,489 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:54,489 DEBUG: Start:	 Training
-2016-09-06 08:21:54,490 DEBUG: Info:	 Time for Training: 0.112372159958[s]
-2016-09-06 08:21:54,490 DEBUG: Done:	 Training
-2016-09-06 08:21:54,490 DEBUG: Start:	 Predicting
-2016-09-06 08:21:54,505 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:54,505 DEBUG: Start:	 Training
-2016-09-06 08:21:54,512 DEBUG: Done:	 Predicting
-2016-09-06 08:21:54,512 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:54,513 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:54,513 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,513 INFO: Done:	 Result Analysis
-2016-09-06 08:21:54,531 DEBUG: Info:	 Time for Training: 0.153424978256[s]
-2016-09-06 08:21:54,531 DEBUG: Done:	 Training
-2016-09-06 08:21:54,531 DEBUG: Start:	 Predicting
-2016-09-06 08:21:54,535 DEBUG: Done:	 Predicting
-2016-09-06 08:21:54,535 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:54,536 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:54,536 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,537 INFO: Done:	 Result Analysis
-2016-09-06 08:21:54,626 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,626 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,626 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:21:54,626 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:21:54,626 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:54,626 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:54,627 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:54,627 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:54,628 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:54,628 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:54,628 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:54,628 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:54,628 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,628 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,713 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:54,713 DEBUG: Start:	 Training
-2016-09-06 08:21:54,724 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:54,724 DEBUG: Start:	 Training
-2016-09-06 08:21:54,732 DEBUG: Info:	 Time for Training: 0.107496976852[s]
-2016-09-06 08:21:54,733 DEBUG: Done:	 Training
-2016-09-06 08:21:54,733 DEBUG: Start:	 Predicting
-2016-09-06 08:21:54,739 DEBUG: Done:	 Predicting
-2016-09-06 08:21:54,739 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:54,740 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:54,740 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,741 INFO: Done:	 Result Analysis
-2016-09-06 08:21:54,743 DEBUG: Info:	 Time for Training: 0.118160963058[s]
-2016-09-06 08:21:54,743 DEBUG: Done:	 Training
-2016-09-06 08:21:54,743 DEBUG: Start:	 Predicting
-2016-09-06 08:21:54,748 DEBUG: Done:	 Predicting
-2016-09-06 08:21:54,748 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:54,749 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:54,749 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,749 INFO: Done:	 Result Analysis
-2016-09-06 08:21:54,871 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,871 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,872 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:21:54,872 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:21:54,872 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:54,872 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:54,872 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:54,872 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:54,872 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:54,872 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:54,872 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:54,872 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:54,872 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,872 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,925 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:54,925 DEBUG: Start:	 Training
-2016-09-06 08:21:54,927 DEBUG: Info:	 Time for Training: 0.0561249256134[s]
-2016-09-06 08:21:54,927 DEBUG: Done:	 Training
-2016-09-06 08:21:54,927 DEBUG: Start:	 Predicting
-2016-09-06 08:21:54,930 DEBUG: Done:	 Predicting
-2016-09-06 08:21:54,930 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:54,931 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:54,932 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,932 INFO: Done:	 Result Analysis
-2016-09-06 08:21:54,952 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:54,953 DEBUG: Start:	 Training
-2016-09-06 08:21:54,956 DEBUG: Info:	 Time for Training: 0.0851340293884[s]
-2016-09-06 08:21:54,956 DEBUG: Done:	 Training
-2016-09-06 08:21:54,956 DEBUG: Start:	 Predicting
-2016-09-06 08:21:54,959 DEBUG: Done:	 Predicting
-2016-09-06 08:21:54,959 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:54,961 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:54,961 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,961 INFO: Done:	 Result Analysis
-2016-09-06 08:21:55,013 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:55,014 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:21:55,014 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:55,015 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:55,015 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:55,015 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:55,015 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:55,015 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:55,016 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:21:55,016 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:55,017 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:55,017 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:55,018 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:55,018 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:55,069 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:55,070 DEBUG: Start:	 Training
-2016-09-06 08:21:55,070 DEBUG: Info:	 Time for Training: 0.0571830272675[s]
-2016-09-06 08:21:55,070 DEBUG: Done:	 Training
-2016-09-06 08:21:55,070 DEBUG: Start:	 Predicting
-2016-09-06 08:21:55,077 DEBUG: Done:	 Predicting
-2016-09-06 08:21:55,077 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:55,078 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:55,078 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:21:55,078 INFO: Done:	 Result Analysis
-2016-09-06 08:21:55,421 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:55,421 DEBUG: Start:	 Training
-2016-09-06 08:21:55,449 DEBUG: Info:	 Time for Training: 0.434818983078[s]
-2016-09-06 08:21:55,449 DEBUG: Done:	 Training
-2016-09-06 08:21:55,450 DEBUG: Start:	 Predicting
-2016-09-06 08:21:55,455 DEBUG: Done:	 Predicting
-2016-09-06 08:21:55,455 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:55,457 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:55,457 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 10, max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:21:55,457 INFO: Done:	 Result Analysis
-2016-09-06 08:21:55,566 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:55,566 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:55,567 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:21:55,567 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:21:55,567 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:55,567 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 08:21:55,568 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:55,568 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:55,568 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:55,568 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:55,568 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:55,568 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 08:21:55,569 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:55,569 DEBUG: Start:	 RandomSearch best settings with 2 iterations
-2016-09-06 08:21:55,688 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:55,689 DEBUG: Start:	 Training
-2016-09-06 08:21:55,690 DEBUG: Info:	 Time for Training: 0.12415099144[s]
-2016-09-06 08:21:55,690 DEBUG: Done:	 Training
-2016-09-06 08:21:55,690 DEBUG: Start:	 Predicting
-2016-09-06 08:21:55,695 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 08:21:55,695 DEBUG: Start:	 Training
-2016-09-06 08:21:55,713 DEBUG: Info:	 Time for Training: 0.146976232529[s]
-2016-09-06 08:21:55,713 DEBUG: Done:	 Training
-2016-09-06 08:21:55,713 DEBUG: Start:	 Predicting
-2016-09-06 08:21:55,713 DEBUG: Done:	 Predicting
-2016-09-06 08:21:55,713 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:55,714 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:55,714 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:55,715 INFO: Done:	 Result Analysis
-2016-09-06 08:21:55,716 DEBUG: Done:	 Predicting
-2016-09-06 08:21:55,716 DEBUG: Start:	 Getting Results
-2016-09-06 08:21:55,717 DEBUG: Done:	 Getting Results
-2016-09-06 08:21:55,717 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:55,718 INFO: Done:	 Result Analysis
-2016-09-06 08:21:55,958 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:21:55,958 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:21:55,959 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:21:55,959 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:21:55,959 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 08:21:55,959 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 08:21:55,960 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 08:21:55,960 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 08:21:55,961 INFO: Info:	 Shape of View2 :(300, 18)
-2016-09-06 08:21:55,961 INFO: Info:	 Shape of View2 :(300, 18)
-2016-09-06 08:21:55,961 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 08:21:55,961 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 08:21:55,961 INFO: Done:	 Read Database Files
-2016-09-06 08:21:55,962 INFO: Done:	 Read Database Files
-2016-09-06 08:21:55,962 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:21:55,962 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:21:55,967 INFO: Done:	 Determine validation split
-2016-09-06 08:21:55,967 INFO: Done:	 Determine validation split
-2016-09-06 08:21:55,967 INFO: Start:	 Determine 5 folds
-2016-09-06 08:21:55,967 INFO: Start:	 Determine 5 folds
-2016-09-06 08:21:55,975 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:21:55,975 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:21:55,975 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:21:55,975 INFO: Done:	 Determine folds
-2016-09-06 08:21:55,975 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 08:21:55,975 INFO: Start:	 Classification
-2016-09-06 08:21:55,975 INFO: 	Start:	 Fold number 1
-2016-09-06 08:21:55,977 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:21:55,977 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:21:55,977 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:21:55,977 INFO: Done:	 Determine folds
-2016-09-06 08:21:55,977 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:21:55,977 INFO: Start:	 Classification
-2016-09-06 08:21:55,977 INFO: 	Start:	 Fold number 1
-2016-09-06 08:21:56,010 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:21:56,019 DEBUG: 			View 0 : 0.5
-2016-09-06 08:21:56,027 DEBUG: 			View 1 : 0.522222222222
-2016-09-06 08:21:56,035 DEBUG: 			View 2 : 0.522222222222
-2016-09-06 08:21:56,042 DEBUG: 			View 3 : 0.555555555556
-2016-09-06 08:21:56,051 INFO: 	Start: 	 Classification
-2016-09-06 08:21:56,077 DEBUG: 			 Best view : 		View3
-2016-09-06 08:21:56,131 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:21:56,132 INFO: 	Start:	 Fold number 2
-2016-09-06 08:21:56,163 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:21:56,171 DEBUG: 			View 0 : 0.727777777778
-2016-09-06 08:21:56,178 DEBUG: 			View 1 : 0.655555555556
-2016-09-06 08:21:56,186 DEBUG: 			View 2 : 0.716666666667
-2016-09-06 08:21:56,193 DEBUG: 			View 3 : 0.65
-2016-09-06 08:21:56,201 INFO: 	Start: 	 Classification
-2016-09-06 08:21:56,233 DEBUG: 			 Best view : 		View0
-2016-09-06 08:21:56,282 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:21:56,283 INFO: 	Start:	 Fold number 3
-2016-09-06 08:21:56,350 INFO: 	Start: 	 Classification
-2016-09-06 08:21:56,408 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:21:56,422 DEBUG: 			View 0 : 0.727777777778
-2016-09-06 08:21:56,429 DEBUG: 			View 1 : 0.677777777778
-2016-09-06 08:21:56,436 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:21:56,436 INFO: 	Start:	 Fold number 4
-2016-09-06 08:21:56,437 DEBUG: 			View 2 : 0.716666666667
-2016-09-06 08:21:56,445 DEBUG: 			View 3 : 0.65
-2016-09-06 08:21:56,487 DEBUG: 			 Best view : 		View0
-2016-09-06 08:21:56,504 INFO: 	Start: 	 Classification
-2016-09-06 08:21:56,585 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:21:56,585 INFO: 	Start:	 Fold number 5
-2016-09-06 08:21:56,653 INFO: 	Start: 	 Classification
-2016-09-06 08:21:56,712 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:21:56,720 DEBUG: 			View 0 : 0.694444444444
-2016-09-06 08:21:56,728 DEBUG: 			View 1 : 0.627777777778
-2016-09-06 08:21:56,733 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:21:56,733 INFO: Done:	 Classification
-2016-09-06 08:21:56,733 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:21:56,734 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:21:56,735 DEBUG: 			View 2 : 0.672222222222
-2016-09-06 08:21:56,739 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 47.619047619
-	-On Validation : 85.5555555556
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 43
-		- Random Forest with num_esimators : 10, max_depth : 21
-		- SVM Linear with C : 8282
-		- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:21:56,740 INFO: Done:	 Result Analysis
-2016-09-06 08:21:56,743 DEBUG: 			View 3 : 0.666666666667
-2016-09-06 08:21:56,786 DEBUG: 			 Best view : 		View2
-2016-09-06 08:21:57,080 INFO: 	Start: 	 Classification
-2016-09-06 08:21:57,551 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:21:57,552 INFO: 	Start:	 Fold number 2
-2016-09-06 08:21:57,580 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:21:57,588 DEBUG: 			View 0 : 0.534482758621
-2016-09-06 08:21:57,594 DEBUG: 			View 1 : 0.511494252874
-2016-09-06 08:21:57,601 DEBUG: 			View 2 : 0.534482758621
-2016-09-06 08:21:57,607 DEBUG: 			View 3 : 0.477011494253
-2016-09-06 08:21:57,638 DEBUG: 			 Best view : 		View0
-2016-09-06 08:21:57,715 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:21:57,722 DEBUG: 			View 0 : 0.741379310345
-2016-09-06 08:21:57,729 DEBUG: 			View 1 : 0.609195402299
-2016-09-06 08:21:57,736 DEBUG: 			View 2 : 0.718390804598
-2016-09-06 08:21:57,743 DEBUG: 			View 3 : 0.649425287356
-2016-09-06 08:21:57,779 DEBUG: 			 Best view : 		View0
-2016-09-06 08:21:57,922 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:21:57,930 DEBUG: 			View 0 : 0.741379310345
-2016-09-06 08:21:57,936 DEBUG: 			View 1 : 0.609195402299
-2016-09-06 08:21:57,943 DEBUG: 			View 2 : 0.718390804598
-2016-09-06 08:21:57,950 DEBUG: 			View 3 : 0.649425287356
-2016-09-06 08:21:57,990 DEBUG: 			 Best view : 		View0
-2016-09-06 08:21:58,199 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:21:58,207 DEBUG: 			View 0 : 0.735632183908
-2016-09-06 08:21:58,213 DEBUG: 			View 1 : 0.603448275862
-2016-09-06 08:21:58,220 DEBUG: 			View 2 : 0.649425287356
-2016-09-06 08:21:58,227 DEBUG: 			View 3 : 0.637931034483
-2016-09-06 08:21:58,268 DEBUG: 			 Best view : 		View0
-2016-09-06 08:21:58,541 INFO: 	Start: 	 Classification
-2016-09-06 08:21:59,002 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:21:59,002 INFO: 	Start:	 Fold number 3
-2016-09-06 08:21:59,032 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:21:59,039 DEBUG: 			View 0 : 0.489010989011
-2016-09-06 08:21:59,045 DEBUG: 			View 1 : 0.489010989011
-2016-09-06 08:21:59,052 DEBUG: 			View 2 : 0.521978021978
-2016-09-06 08:21:59,059 DEBUG: 			View 3 : 0.554945054945
-2016-09-06 08:21:59,090 DEBUG: 			 Best view : 		View1
-2016-09-06 08:21:59,170 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:21:59,177 DEBUG: 			View 0 : 0.637362637363
-2016-09-06 08:21:59,184 DEBUG: 			View 1 : 0.659340659341
-2016-09-06 08:21:59,192 DEBUG: 			View 2 : 0.565934065934
-2016-09-06 08:21:59,199 DEBUG: 			View 3 : 0.648351648352
-2016-09-06 08:21:59,237 DEBUG: 			 Best view : 		View1
-2016-09-06 08:21:59,385 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:21:59,392 DEBUG: 			View 0 : 0.637362637363
-2016-09-06 08:21:59,399 DEBUG: 			View 1 : 0.659340659341
-2016-09-06 08:21:59,407 DEBUG: 			View 2 : 0.565934065934
-2016-09-06 08:21:59,414 DEBUG: 			View 3 : 0.648351648352
-2016-09-06 08:21:59,455 DEBUG: 			 Best view : 		View1
-2016-09-06 08:21:59,671 INFO: 	Start: 	 Classification
-2016-09-06 08:22:00,026 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:22:00,026 INFO: 	Start:	 Fold number 4
-2016-09-06 08:22:00,057 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:22:00,064 DEBUG: 			View 0 : 0.559782608696
-2016-09-06 08:22:00,071 DEBUG: 			View 1 : 0.538043478261
-2016-09-06 08:22:00,078 DEBUG: 			View 2 : 0.510869565217
-2016-09-06 08:22:00,085 DEBUG: 			View 3 : 0.516304347826
-2016-09-06 08:22:00,117 DEBUG: 			 Best view : 		View0
-2016-09-06 08:22:00,199 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:22:00,207 DEBUG: 			View 0 : 0.717391304348
-2016-09-06 08:22:00,214 DEBUG: 			View 1 : 0.679347826087
-2016-09-06 08:22:00,221 DEBUG: 			View 2 : 0.663043478261
-2016-09-06 08:22:00,228 DEBUG: 			View 3 : 0.663043478261
-2016-09-06 08:22:00,267 DEBUG: 			 Best view : 		View0
-2016-09-06 08:22:00,419 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:22:00,427 DEBUG: 			View 0 : 0.717391304348
-2016-09-06 08:22:00,434 DEBUG: 			View 1 : 0.679347826087
-2016-09-06 08:22:00,442 DEBUG: 			View 2 : 0.663043478261
-2016-09-06 08:22:00,449 DEBUG: 			View 3 : 0.663043478261
-2016-09-06 08:22:00,490 DEBUG: 			 Best view : 		View0
-2016-09-06 08:22:00,711 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:22:00,719 DEBUG: 			View 0 : 0.679347826087
-2016-09-06 08:22:00,726 DEBUG: 			View 1 : 0.646739130435
-2016-09-06 08:22:00,734 DEBUG: 			View 2 : 0.608695652174
-2016-09-06 08:22:00,741 DEBUG: 			View 3 : 0.592391304348
-2016-09-06 08:22:00,784 DEBUG: 			 Best view : 		View1
-2016-09-06 08:22:01,075 INFO: 	Start: 	 Classification
-2016-09-06 08:22:01,552 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:22:01,553 INFO: 	Start:	 Fold number 5
-2016-09-06 08:22:01,582 DEBUG: 		Start:	 Iteration 1
-2016-09-06 08:22:01,590 DEBUG: 			View 0 : 0.469273743017
-2016-09-06 08:22:01,596 DEBUG: 			View 1 : 0.513966480447
-2016-09-06 08:22:01,603 DEBUG: 			View 2 : 0.54748603352
-2016-09-06 08:22:01,609 DEBUG: 			View 3 : 0.586592178771
-2016-09-06 08:22:01,641 DEBUG: 			 Best view : 		View0
-2016-09-06 08:22:01,720 DEBUG: 		Start:	 Iteration 2
-2016-09-06 08:22:01,727 DEBUG: 			View 0 : 0.703910614525
-2016-09-06 08:22:01,734 DEBUG: 			View 1 : 0.703910614525
-2016-09-06 08:22:01,742 DEBUG: 			View 2 : 0.664804469274
-2016-09-06 08:22:01,748 DEBUG: 			View 3 : 0.642458100559
-2016-09-06 08:22:01,786 DEBUG: 			 Best view : 		View0
-2016-09-06 08:22:01,933 DEBUG: 		Start:	 Iteration 3
-2016-09-06 08:22:01,941 DEBUG: 			View 0 : 0.703910614525
-2016-09-06 08:22:01,948 DEBUG: 			View 1 : 0.703910614525
-2016-09-06 08:22:01,955 DEBUG: 			View 2 : 0.664804469274
-2016-09-06 08:22:01,962 DEBUG: 			View 3 : 0.642458100559
-2016-09-06 08:22:02,002 DEBUG: 			 Best view : 		View0
-2016-09-06 08:22:02,216 DEBUG: 		Start:	 Iteration 4
-2016-09-06 08:22:02,223 DEBUG: 			View 0 : 0.642458100559
-2016-09-06 08:22:02,230 DEBUG: 			View 1 : 0.642458100559
-2016-09-06 08:22:02,238 DEBUG: 			View 2 : 0.536312849162
-2016-09-06 08:22:02,245 DEBUG: 			View 3 : 0.653631284916
-2016-09-06 08:22:02,288 DEBUG: 			 Best view : 		View0
-2016-09-06 08:22:02,569 INFO: 	Start: 	 Classification
-2016-09-06 08:22:03,036 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:22:03,036 INFO: Done:	 Classification
-2016-09-06 08:22:03,036 INFO: Info:	 Time for Classification: 7[s]
-2016-09-06 08:22:03,036 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 08:22:05,430 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 70.9959933267
-	-On Test : 48.0952380952
-	-On Validation : 70.0Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 19), View1 of shape (300, 7), View2 of shape (300, 18), View3 of shape (300, 6)
-	-5 folds
-	- Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:06        0:00:00
-	          Total        0:00:19        0:00:02
-	So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.265
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.248333333333
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.262777777778
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.252222222222
-			- Percentage of time chosen : 0.1
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.275287356322
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.233333333333
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.262068965517
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.241379310345
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.176373626374
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.180769230769
-			- Percentage of time chosen : 0.3
-		- On View2 : 
-			- Mean average Accuracy : 0.165384615385
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.185164835165
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.267391304348
-			- Percentage of time chosen : 0.9
-		- On View1 : 
-			- Mean average Accuracy : 0.254347826087
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.244565217391
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.24347826087
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.251955307263
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.256424581006
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.241340782123
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.25251396648
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 47.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 46.5517241379
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 47.2527472527
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 47.8260869565
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 46.3687150838
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 72.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 77.7777777778
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 74.1379310345
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 65.9340659341
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.1111111111
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 71.7391304348
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 70.3910614525
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 72.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 77.7777777778
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 74.1379310345
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 65.9340659341
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.1111111111
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 71.7391304348
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 70.3910614525
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 72.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 77.7777777778
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 74.1379310345
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 47.2527472527
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 71.7391304348
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.0
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 70.3910614525
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.0
-			Selected View : View0
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 47.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 46.5517241379
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 47.8260869565
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 46.3687150838
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-2016-09-06 08:22:05,624 INFO: Done:	 Result Analysis
-2016-09-06 08:22:05,722 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:05,723 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:05,723 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:05,723 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:05,723 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 08:22:05,724 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 08:22:05,724 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 08:22:05,724 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 08:22:05,724 INFO: Info:	 Shape of View2 :(300, 18)
-2016-09-06 08:22:05,725 INFO: Info:	 Shape of View2 :(300, 18)
-2016-09-06 08:22:05,725 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 08:22:05,725 INFO: Done:	 Read Database Files
-2016-09-06 08:22:05,725 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:22:05,725 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 08:22:05,725 INFO: Done:	 Read Database Files
-2016-09-06 08:22:05,725 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:22:05,729 INFO: Done:	 Determine validation split
-2016-09-06 08:22:05,729 INFO: Done:	 Determine validation split
-2016-09-06 08:22:05,730 INFO: Start:	 Determine 5 folds
-2016-09-06 08:22:05,730 INFO: Start:	 Determine 5 folds
-2016-09-06 08:22:05,737 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:22:05,737 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:22:05,737 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:22:05,737 INFO: Done:	 Determine folds
-2016-09-06 08:22:05,738 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:22:05,738 INFO: Start:	 Classification
-2016-09-06 08:22:05,738 INFO: 	Start:	 Fold number 1
-2016-09-06 08:22:05,742 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:22:05,742 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:22:05,742 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:22:05,742 INFO: Done:	 Determine folds
-2016-09-06 08:22:05,742 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:22:05,742 INFO: Start:	 Classification
-2016-09-06 08:22:05,743 INFO: 	Start:	 Fold number 1
-2016-09-06 08:22:05,814 INFO: 	Start: 	 Classification
-2016-09-06 08:22:05,835 INFO: 	Start: 	 Classification
-2016-09-06 08:22:05,876 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:22:05,876 INFO: 	Start:	 Fold number 2
-2016-09-06 08:22:05,900 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:22:05,900 INFO: 	Start:	 Fold number 2
-2016-09-06 08:22:05,968 INFO: 	Start: 	 Classification
-2016-09-06 08:22:05,968 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,009 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:22:06,009 INFO: 	Start:	 Fold number 3
-2016-09-06 08:22:06,054 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:22:06,054 INFO: 	Start:	 Fold number 3
-2016-09-06 08:22:06,101 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,124 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,142 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:22:06,142 INFO: 	Start:	 Fold number 4
-2016-09-06 08:22:06,211 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:22:06,212 INFO: 	Start:	 Fold number 4
-2016-09-06 08:22:06,233 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,274 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:22:06,274 INFO: 	Start:	 Fold number 5
-2016-09-06 08:22:06,279 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,364 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:22:06,364 INFO: 	Start:	 Fold number 5
-2016-09-06 08:22:06,367 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,408 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:22:06,408 INFO: Done:	 Classification
-2016-09-06 08:22:06,408 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:22:06,408 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:22:06,413 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 51.9047619048
-	-On Validation : 86.6666666667
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 43
-		- Random Forest with num_esimators : 10, max_depth : 21
-		- SVM Linear with C : 8282
-		- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:06,413 INFO: Done:	 Result Analysis
-2016-09-06 08:22:06,432 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,514 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:22:06,514 INFO: Done:	 Classification
-2016-09-06 08:22:06,515 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:22:06,515 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:22:06,519 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 99.2288882289
-	-On Test : 44.7619047619
-	-On Validation : 88.2222222222
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 43
-		- Random Forest with num_esimators : 10, max_depth : 21
-		- SVM Linear with C : 8282
-		- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:06,520 INFO: Done:	 Result Analysis
-2016-09-06 08:22:06,667 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:06,667 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:06,667 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:06,668 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:06,668 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 08:22:06,668 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 08:22:06,669 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 08:22:06,669 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 08:22:06,669 INFO: Info:	 Shape of View2 :(300, 18)
-2016-09-06 08:22:06,669 INFO: Info:	 Shape of View2 :(300, 18)
-2016-09-06 08:22:06,670 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 08:22:06,670 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 08:22:06,670 INFO: Done:	 Read Database Files
-2016-09-06 08:22:06,670 INFO: Done:	 Read Database Files
-2016-09-06 08:22:06,670 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:22:06,670 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:22:06,675 INFO: Done:	 Determine validation split
-2016-09-06 08:22:06,675 INFO: Done:	 Determine validation split
-2016-09-06 08:22:06,675 INFO: Start:	 Determine 5 folds
-2016-09-06 08:22:06,675 INFO: Start:	 Determine 5 folds
-2016-09-06 08:22:06,684 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:22:06,684 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:22:06,684 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:22:06,685 INFO: Done:	 Determine folds
-2016-09-06 08:22:06,685 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:22:06,685 INFO: Start:	 Classification
-2016-09-06 08:22:06,685 INFO: 	Start:	 Fold number 1
-2016-09-06 08:22:06,688 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:22:06,688 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:22:06,688 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:22:06,688 INFO: Done:	 Determine folds
-2016-09-06 08:22:06,688 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:22:06,688 INFO: Start:	 Classification
-2016-09-06 08:22:06,689 INFO: 	Start:	 Fold number 1
-2016-09-06 08:22:06,715 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,740 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:22:06,740 INFO: 	Start:	 Fold number 2
-2016-09-06 08:22:06,760 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,768 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,795 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:22:06,796 INFO: 	Start:	 Fold number 3
-2016-09-06 08:22:06,807 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:22:06,807 INFO: 	Start:	 Fold number 2
-2016-09-06 08:22:06,820 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,847 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:22:06,847 INFO: 	Start:	 Fold number 4
-2016-09-06 08:22:06,872 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,878 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,898 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:22:06,898 INFO: 	Start:	 Fold number 5
-2016-09-06 08:22:06,921 INFO: 	Start: 	 Classification
-2016-09-06 08:22:06,924 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:22:06,924 INFO: 	Start:	 Fold number 3
-2016-09-06 08:22:06,947 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:22:06,947 INFO: Done:	 Classification
-2016-09-06 08:22:06,947 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:22:06,947 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:22:06,951 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 48.5714285714
-	-On Validation : 85.1111111111
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:06,951 INFO: Done:	 Result Analysis
-2016-09-06 08:22:06,992 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,038 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:22:07,038 INFO: 	Start:	 Fold number 4
-2016-09-06 08:22:07,106 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,149 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:22:07,149 INFO: 	Start:	 Fold number 5
-2016-09-06 08:22:07,214 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,257 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:22:07,257 INFO: Done:	 Classification
-2016-09-06 08:22:07,257 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:22:07,257 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:22:07,263 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 8.28366620128
-	-On Test : 0.952380952381
-	-On Validation : 6.66666666667
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 43
-		- Random Forest with num_esimators : 10, max_depth : 21
-		- SVM Linear with C : 8282
-		- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:07,263 INFO: Done:	 Result Analysis
-2016-09-06 08:22:07,423 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:07,423 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:07,424 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:07,424 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:07,424 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 08:22:07,424 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 08:22:07,425 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 08:22:07,425 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 08:22:07,425 INFO: Info:	 Shape of View2 :(300, 18)
-2016-09-06 08:22:07,425 INFO: Info:	 Shape of View2 :(300, 18)
-2016-09-06 08:22:07,425 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 08:22:07,426 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 08:22:07,426 INFO: Done:	 Read Database Files
-2016-09-06 08:22:07,426 INFO: Done:	 Read Database Files
-2016-09-06 08:22:07,426 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:22:07,426 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:22:07,429 INFO: Done:	 Determine validation split
-2016-09-06 08:22:07,430 INFO: Done:	 Determine validation split
-2016-09-06 08:22:07,430 INFO: Start:	 Determine 5 folds
-2016-09-06 08:22:07,430 INFO: Start:	 Determine 5 folds
-2016-09-06 08:22:07,438 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:22:07,438 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:22:07,438 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:22:07,438 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:22:07,438 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:22:07,438 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:22:07,438 INFO: Done:	 Determine folds
-2016-09-06 08:22:07,438 INFO: Done:	 Determine folds
-2016-09-06 08:22:07,438 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:22:07,438 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:22:07,438 INFO: Start:	 Classification
-2016-09-06 08:22:07,438 INFO: Start:	 Classification
-2016-09-06 08:22:07,438 INFO: 	Start:	 Fold number 1
-2016-09-06 08:22:07,438 INFO: 	Start:	 Fold number 1
-2016-09-06 08:22:07,457 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,457 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,485 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:22:07,485 INFO: 	Start:	 Fold number 2
-2016-09-06 08:22:07,487 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:22:07,487 INFO: 	Start:	 Fold number 2
-2016-09-06 08:22:07,504 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,515 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,532 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:22:07,533 INFO: 	Start:	 Fold number 3
-2016-09-06 08:22:07,549 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,553 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:22:07,554 INFO: 	Start:	 Fold number 3
-2016-09-06 08:22:07,576 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,578 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:22:07,578 INFO: 	Start:	 Fold number 4
-2016-09-06 08:22:07,595 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,604 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:22:07,604 INFO: 	Start:	 Fold number 4
-2016-09-06 08:22:07,626 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:22:07,626 INFO: 	Start:	 Fold number 5
-2016-09-06 08:22:07,629 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,644 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,661 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:22:07,662 INFO: 	Start:	 Fold number 5
-2016-09-06 08:22:07,673 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:22:07,673 INFO: Done:	 Classification
-2016-09-06 08:22:07,673 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:22:07,673 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:22:07,678 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 47.1428571429
-	-On Validation : 88.4444444444
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- K nearest Neighbors with  n_neighbors: 1.0
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:07,678 INFO: Done:	 Result Analysis
-2016-09-06 08:22:07,682 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,710 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:22:07,710 INFO: Done:	 Classification
-2016-09-06 08:22:07,710 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:22:07,710 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:22:07,716 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 59.5363054247
-	-On Test : 48.5714285714
-	-On Validation : 55.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Decision Tree with max_depth : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:07,716 INFO: Done:	 Result Analysis
-2016-09-06 08:22:07,866 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:07,866 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:07,866 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:07,866 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:07,867 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 08:22:07,867 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 08:22:07,867 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 08:22:07,867 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 08:22:07,868 INFO: Info:	 Shape of View2 :(300, 18)
-2016-09-06 08:22:07,868 INFO: Info:	 Shape of View2 :(300, 18)
-2016-09-06 08:22:07,868 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 08:22:07,868 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 08:22:07,868 INFO: Done:	 Read Database Files
-2016-09-06 08:22:07,868 INFO: Done:	 Read Database Files
-2016-09-06 08:22:07,869 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:22:07,869 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:22:07,872 INFO: Done:	 Determine validation split
-2016-09-06 08:22:07,873 INFO: Start:	 Determine 5 folds
-2016-09-06 08:22:07,873 INFO: Done:	 Determine validation split
-2016-09-06 08:22:07,873 INFO: Start:	 Determine 5 folds
-2016-09-06 08:22:07,881 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:22:07,881 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:22:07,881 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:22:07,881 INFO: Done:	 Determine folds
-2016-09-06 08:22:07,881 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:22:07,881 INFO: Start:	 Classification
-2016-09-06 08:22:07,882 INFO: 	Start:	 Fold number 1
-2016-09-06 08:22:07,883 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:22:07,884 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:22:07,884 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:22:07,884 INFO: Done:	 Determine folds
-2016-09-06 08:22:07,884 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:22:07,884 INFO: Start:	 Classification
-2016-09-06 08:22:07,884 INFO: 	Start:	 Fold number 1
-2016-09-06 08:22:07,904 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,958 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:22:07,958 INFO: 	Start:	 Fold number 2
-2016-09-06 08:22:07,976 INFO: 	Start: 	 Classification
-2016-09-06 08:22:07,992 INFO: 	Start: 	 Classification
-2016-09-06 08:22:08,011 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:22:08,011 INFO: 	Start:	 Fold number 3
-2016-09-06 08:22:08,036 INFO: 	Start: 	 Classification
-2016-09-06 08:22:08,040 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:22:08,040 INFO: 	Start:	 Fold number 2
-2016-09-06 08:22:08,067 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:22:08,068 INFO: 	Start:	 Fold number 4
-2016-09-06 08:22:08,091 INFO: 	Start: 	 Classification
-2016-09-06 08:22:08,134 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:22:08,134 INFO: 	Start:	 Fold number 5
-2016-09-06 08:22:08,167 INFO: 	Start: 	 Classification
-2016-09-06 08:22:08,172 INFO: 	Start: 	 Classification
-2016-09-06 08:22:08,217 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:22:08,217 INFO: 	Start:	 Fold number 3
-2016-09-06 08:22:08,222 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:22:08,227 INFO: Done:	 Classification
-2016-09-06 08:22:08,228 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:22:08,228 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:22:08,238 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 70.0117009413
-	-On Test : 49.5238095238
-	-On Validation : 63.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:08,241 INFO: Done:	 Result Analysis
-2016-09-06 08:22:08,350 INFO: 	Start: 	 Classification
-2016-09-06 08:22:08,400 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:22:08,400 INFO: 	Start:	 Fold number 4
-2016-09-06 08:22:08,532 INFO: 	Start: 	 Classification
-2016-09-06 08:22:08,570 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:22:08,571 INFO: 	Start:	 Fold number 5
-2016-09-06 08:22:08,655 INFO: 	Start: 	 Classification
-2016-09-06 08:22:08,689 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:22:08,689 INFO: Done:	 Classification
-2016-09-06 08:22:08,689 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:22:08,689 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:22:08,694 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 98.9016594713
-	-On Test : 47.1428571429
-	-On Validation : 86.2222222222
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:08,694 INFO: Done:	 Result Analysis
-2016-09-06 08:22:08,819 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:08,820 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:08,821 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 08:22:08,821 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 08:22:08,822 INFO: Info:	 Shape of View2 :(300, 18)
-2016-09-06 08:22:08,823 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 08:22:08,823 INFO: Done:	 Read Database Files
-2016-09-06 08:22:08,823 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 08:22:08,828 INFO: Done:	 Determine validation split
-2016-09-06 08:22:08,828 INFO: Start:	 Determine 5 folds
-2016-09-06 08:22:08,841 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 08:22:08,841 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 08:22:08,841 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 08:22:08,841 INFO: Done:	 Determine folds
-2016-09-06 08:22:08,841 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 08:22:08,842 INFO: Start:	 Classification
-2016-09-06 08:22:08,842 INFO: 	Start:	 Fold number 1
-2016-09-06 08:22:08,881 INFO: 	Start: 	 Classification
-2016-09-06 08:22:08,913 INFO: 	Done: 	 Fold number 1
-2016-09-06 08:22:08,913 INFO: 	Start:	 Fold number 2
-2016-09-06 08:22:08,958 INFO: 	Start: 	 Classification
-2016-09-06 08:22:09,000 INFO: 	Done: 	 Fold number 2
-2016-09-06 08:22:09,001 INFO: 	Start:	 Fold number 3
-2016-09-06 08:22:09,035 INFO: 	Start: 	 Classification
-2016-09-06 08:22:09,066 INFO: 	Done: 	 Fold number 3
-2016-09-06 08:22:09,067 INFO: 	Start:	 Fold number 4
-2016-09-06 08:22:09,100 INFO: 	Start: 	 Classification
-2016-09-06 08:22:09,129 INFO: 	Done: 	 Fold number 4
-2016-09-06 08:22:09,129 INFO: 	Start:	 Fold number 5
-2016-09-06 08:22:09,165 INFO: 	Start: 	 Classification
-2016-09-06 08:22:09,195 INFO: 	Done: 	 Fold number 5
-2016-09-06 08:22:09,195 INFO: Done:	 Classification
-2016-09-06 08:22:09,195 INFO: Info:	 Time for Classification: 0[s]
-2016-09-06 08:22:09,195 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 08:22:09,200 INFO: 		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 74.6411370567
-	-On Test : 49.5238095238
-	-On Validation : 67.5555555556
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SVM Linear with C : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:09,200 INFO: Done:	 Result Analysis
-2016-09-06 08:22:09,363 DEBUG: Start:	 Deleting 2 temporary datasets for multiprocessing
-2016-09-06 08:22:09,363 DEBUG: Start:	 Deleting datasets for multiprocessing
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1e1ff6f2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e514a6f6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6af21963..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.642857142857
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ba94ca1a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 10, max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 76fed0d2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6c815dfb..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.457142857143
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ee87e729..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0a445c37..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5a85ac3a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6d85da2a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 10, max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 74dec77b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8526
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a48bd5d1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 95412304..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 501b8083..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ae7647f0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a3229076..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 72b8490a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2e8eb890..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3b739757..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e949584b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index edea8d84..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2a8420c7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 10, max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c5b216bf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1a1a0d9b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db4f16ed..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b766ae96..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c5f4425d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0c89a467..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 10, max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 110eb665..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ebbde3e7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8282
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 2 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082156Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082156Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 70636764..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082156Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 47.619047619
-	-On Validation : 85.5555555556
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 43
-		- Random Forest with num_esimators : 10, max_depth : 21
-		- SVM Linear with C : 8282
-		- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082205Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-082205Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
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z?&ZZLBqRj7MowB<8dM;xxq0LW3+UERC1vG@j~_R43ac&HvPHzx(-YK+{POa0v}Ta*
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zn3$5St?b2%7r}R9D;OCGJ$v@-)aK}AJ~NxvMsEj|_>e13V|Txq+VR5kAgFSby|Vv`
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082205Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082205Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index c3badee1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082205Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,230 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 70.9959933267
-	-On Test : 48.0952380952
-	-On Validation : 70.0Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 19), View1 of shape (300, 7), View2 of shape (300, 18), View3 of shape (300, 6)
-	-5 folds
-	- Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:06        0:00:00
-	          Total        0:00:19        0:00:02
-	So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.265
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.248333333333
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.262777777778
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.252222222222
-			- Percentage of time chosen : 0.1
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.275287356322
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.233333333333
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.262068965517
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.241379310345
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.176373626374
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.180769230769
-			- Percentage of time chosen : 0.3
-		- On View2 : 
-			- Mean average Accuracy : 0.165384615385
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.185164835165
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.267391304348
-			- Percentage of time chosen : 0.9
-		- On View1 : 
-			- Mean average Accuracy : 0.254347826087
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.244565217391
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.24347826087
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.251955307263
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.256424581006
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.241340782123
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.25251396648
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 47.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 46.5517241379
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 47.2527472527
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 47.8260869565
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 46.3687150838
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 72.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 77.7777777778
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 74.1379310345
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 65.9340659341
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.1111111111
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 71.7391304348
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 70.3910614525
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 72.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 77.7777777778
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 74.1379310345
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 65.9340659341
-			Accuracy on test : 0.0
-			Accuracy on validation : 61.1111111111
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 71.7391304348
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 70.3910614525
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 72.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 77.7777777778
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 74.1379310345
-			Accuracy on test : 0.0
-			Accuracy on validation : 71.1111111111
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 47.2527472527
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 71.7391304348
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.0
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 70.3910614525
-			Accuracy on test : 0.0
-			Accuracy on validation : 70.0
-			Selected View : View0
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 47.7777777778
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 46.5517241379
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 47.8260869565
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 46.3687150838
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index b4d6401f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 48.5714285714
-	-On Validation : 85.1111111111
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-MajorityVoting-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-MajorityVoting-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 831a920a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-MajorityVoting-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 99.2288882289
-	-On Test : 44.7619047619
-	-On Validation : 88.2222222222
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 43
-		- Random Forest with num_esimators : 10, max_depth : 21
-		- SVM Linear with C : 8282
-		- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-SVMForLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-SVMForLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 28813893..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-SVMForLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 51.9047619048
-	-On Validation : 86.6666666667
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 43
-		- Random Forest with num_esimators : 10, max_depth : 21
-		- SVM Linear with C : 8282
-		- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index db0c2c18..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 59.5363054247
-	-On Test : 48.5714285714
-	-On Validation : 55.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Decision Tree with max_depth : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index ec9120de..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 47.1428571429
-	-On Validation : 88.4444444444
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- K nearest Neighbors with  n_neighbors: 1.0
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-LateFusion-WeightedLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-LateFusion-WeightedLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index b8150a2f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-LateFusion-WeightedLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 8.28366620128
-	-On Test : 0.952380952381
-	-On Validation : 6.66666666667
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
-	-With monoview classifiers : 
-		- K nearest Neighbors with  n_neighbors: 43
-		- Random Forest with num_esimators : 10, max_depth : 21
-		- SVM Linear with C : 8282
-		- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index e76f9e9f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 98.9016594713
-	-On Test : 47.1428571429
-	-On Validation : 86.2222222222
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 9b3ab32d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 70.0117009413
-	-On Test : 49.5238095238
-	-On Validation : 63.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082209Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082209Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index d845d773..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082209Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 74.6411370567
-	-On Test : 49.5238095238
-	-On Validation : 67.5555555556
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier : 
-		- SVM Linear with C : 1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:00        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index c45e025b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,8 +0,0 @@
-2016-09-06 09:25:57,714 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 09:25:57,717 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 09:25:57,717 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 09:25:57,717 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 09:25:57,717 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 09:25:57,717 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 09:25:57,717 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 09:25:57,717 DEBUG: Start:	 RandomSearch best settings with 30 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 8bd52115..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,15 +0,0 @@
-2016-09-06 10:06:22,879 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:06:22,881 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:06:22,881 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:06:22,881 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:06:22,882 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:06:22,882 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:06:22,882 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:06:22,882 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:06:22,965 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:06:22,965 DEBUG: Start:	 Training
-2016-09-06 10:06:22,970 DEBUG: Info:	 Time for Training: 0.0897569656372[s]
-2016-09-06 10:06:22,971 DEBUG: Done:	 Training
-2016-09-06 10:06:22,971 DEBUG: Start:	 Predicting
-2016-09-06 10:06:22,984 DEBUG: Done:	 Predicting
-2016-09-06 10:06:22,984 DEBUG: Start:	 Getting Results
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index b3758f07..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1310 +0,0 @@
-2016-09-06 10:07:29,943 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:07:29,945 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:29,945 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:29,945 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:29,946 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:29,946 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:29,946 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:29,946 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,025 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:30,025 DEBUG: Start:	 Training
-2016-09-06 10:07:30,029 DEBUG: Info:	 Time for Training: 0.0845639705658[s]
-2016-09-06 10:07:30,029 DEBUG: Done:	 Training
-2016-09-06 10:07:30,029 DEBUG: Start:	 Predicting
-2016-09-06 10:07:30,032 DEBUG: Done:	 Predicting
-2016-09-06 10:07:30,032 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:30,033 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:30,034 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,034 INFO: Done:	 Result Analysis
-2016-09-06 10:07:30,035 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,035 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:30,035 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:30,035 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,035 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,035 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:30,035 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,065 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:30,065 DEBUG: Start:	 Training
-2016-09-06 10:07:30,066 DEBUG: Info:	 Time for Training: 0.0320420265198[s]
-2016-09-06 10:07:30,067 DEBUG: Done:	 Training
-2016-09-06 10:07:30,067 DEBUG: Start:	 Predicting
-2016-09-06 10:07:30,068 DEBUG: Done:	 Predicting
-2016-09-06 10:07:30,068 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:30,069 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:30,069 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.866666666667
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.866666666667
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,070 INFO: Done:	 Result Analysis
-2016-09-06 10:07:30,071 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,071 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:30,071 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:30,071 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,071 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,071 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:30,071 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,113 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:30,113 DEBUG: Start:	 Training
-2016-09-06 10:07:30,113 DEBUG: Info:	 Time for Training: 0.0430898666382[s]
-2016-09-06 10:07:30,113 DEBUG: Done:	 Training
-2016-09-06 10:07:30,114 DEBUG: Start:	 Predicting
-2016-09-06 10:07:30,119 DEBUG: Done:	 Predicting
-2016-09-06 10:07:30,120 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:30,121 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:30,121 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 44
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,121 INFO: Done:	 Result Analysis
-2016-09-06 10:07:30,122 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,122 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:30,122 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:30,122 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,123 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,123 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:30,123 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,425 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:30,425 DEBUG: Start:	 Training
-2016-09-06 10:07:30,470 DEBUG: Info:	 Time for Training: 0.348073959351[s]
-2016-09-06 10:07:30,470 DEBUG: Done:	 Training
-2016-09-06 10:07:30,470 DEBUG: Start:	 Predicting
-2016-09-06 10:07:30,475 DEBUG: Done:	 Predicting
-2016-09-06 10:07:30,475 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:30,476 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:30,476 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.766666666667
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 18, max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.766666666667
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,477 INFO: Done:	 Result Analysis
-2016-09-06 10:07:30,478 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,478 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:30,478 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:30,478 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,478 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,478 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:30,478 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,540 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:30,540 DEBUG: Start:	 Training
-2016-09-06 10:07:30,541 DEBUG: Info:	 Time for Training: 0.0633749961853[s]
-2016-09-06 10:07:30,541 DEBUG: Done:	 Training
-2016-09-06 10:07:30,541 DEBUG: Start:	 Predicting
-2016-09-06 10:07:30,587 DEBUG: Done:	 Predicting
-2016-09-06 10:07:30,587 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:30,589 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:30,590 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,590 INFO: Done:	 Result Analysis
-2016-09-06 10:07:30,592 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,592 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:30,592 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:30,593 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,593 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,593 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:30,593 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,640 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:30,640 DEBUG: Start:	 Training
-2016-09-06 10:07:30,658 DEBUG: Info:	 Time for Training: 0.0669250488281[s]
-2016-09-06 10:07:30,658 DEBUG: Done:	 Training
-2016-09-06 10:07:30,658 DEBUG: Start:	 Predicting
-2016-09-06 10:07:30,661 DEBUG: Done:	 Predicting
-2016-09-06 10:07:30,661 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:30,662 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:30,662 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 491
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,662 INFO: Done:	 Result Analysis
-2016-09-06 10:07:30,663 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,664 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:30,664 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:30,664 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,664 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,664 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:30,664 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,709 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:30,710 DEBUG: Start:	 Training
-2016-09-06 10:07:30,726 DEBUG: Info:	 Time for Training: 0.0627450942993[s]
-2016-09-06 10:07:30,726 DEBUG: Done:	 Training
-2016-09-06 10:07:30,726 DEBUG: Start:	 Predicting
-2016-09-06 10:07:30,729 DEBUG: Done:	 Predicting
-2016-09-06 10:07:30,729 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:30,730 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:30,730 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2405
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,730 INFO: Done:	 Result Analysis
-2016-09-06 10:07:30,731 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,731 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:30,731 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:30,732 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,732 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,732 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:30,732 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,773 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:30,773 DEBUG: Start:	 Training
-2016-09-06 10:07:30,790 DEBUG: Info:	 Time for Training: 0.0591881275177[s]
-2016-09-06 10:07:30,790 DEBUG: Done:	 Training
-2016-09-06 10:07:30,790 DEBUG: Start:	 Predicting
-2016-09-06 10:07:30,794 DEBUG: Done:	 Predicting
-2016-09-06 10:07:30,795 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:30,796 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:30,796 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9676
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,796 INFO: Done:	 Result Analysis
-2016-09-06 10:07:30,797 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,797 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:30,797 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:30,797 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:30,798 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:30,798 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:30,798 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,840 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:30,840 DEBUG: Start:	 Training
-2016-09-06 10:07:30,844 DEBUG: Info:	 Time for Training: 0.0470898151398[s]
-2016-09-06 10:07:30,844 DEBUG: Done:	 Training
-2016-09-06 10:07:30,844 DEBUG: Start:	 Predicting
-2016-09-06 10:07:30,846 DEBUG: Done:	 Predicting
-2016-09-06 10:07:30,846 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:30,848 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:30,848 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,848 INFO: Done:	 Result Analysis
-2016-09-06 10:07:30,849 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,849 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:30,849 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:30,849 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:30,849 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:30,850 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:30,850 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,877 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:30,877 DEBUG: Start:	 Training
-2016-09-06 10:07:30,879 DEBUG: Info:	 Time for Training: 0.0301620960236[s]
-2016-09-06 10:07:30,879 DEBUG: Done:	 Training
-2016-09-06 10:07:30,879 DEBUG: Start:	 Predicting
-2016-09-06 10:07:30,880 DEBUG: Done:	 Predicting
-2016-09-06 10:07:30,880 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:30,882 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:30,882 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,882 INFO: Done:	 Result Analysis
-2016-09-06 10:07:30,883 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,883 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:30,883 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:30,883 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:30,883 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:30,883 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:30,884 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,909 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:30,909 DEBUG: Start:	 Training
-2016-09-06 10:07:30,910 DEBUG: Info:	 Time for Training: 0.0272629261017[s]
-2016-09-06 10:07:30,910 DEBUG: Done:	 Training
-2016-09-06 10:07:30,910 DEBUG: Start:	 Predicting
-2016-09-06 10:07:30,914 DEBUG: Done:	 Predicting
-2016-09-06 10:07:30,914 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:30,916 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:30,916 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,916 INFO: Done:	 Result Analysis
-2016-09-06 10:07:30,917 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,917 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:30,917 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:30,917 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:30,918 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:30,918 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:30,918 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,132 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:31,133 DEBUG: Start:	 Training
-2016-09-06 10:07:31,164 DEBUG: Info:	 Time for Training: 0.247138977051[s]
-2016-09-06 10:07:31,164 DEBUG: Done:	 Training
-2016-09-06 10:07:31,164 DEBUG: Start:	 Predicting
-2016-09-06 10:07:31,168 DEBUG: Done:	 Predicting
-2016-09-06 10:07:31,168 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:31,169 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:31,169 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 12, max_depth : 17
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.980952380952
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,170 INFO: Done:	 Result Analysis
-2016-09-06 10:07:31,171 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,171 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:31,171 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:31,171 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:31,171 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:31,171 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:31,171 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,209 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:31,209 DEBUG: Start:	 Training
-2016-09-06 10:07:31,210 DEBUG: Info:	 Time for Training: 0.0394690036774[s]
-2016-09-06 10:07:31,210 DEBUG: Done:	 Training
-2016-09-06 10:07:31,210 DEBUG: Start:	 Predicting
-2016-09-06 10:07:31,211 DEBUG: Done:	 Predicting
-2016-09-06 10:07:31,212 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:31,213 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:31,213 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,213 INFO: Done:	 Result Analysis
-2016-09-06 10:07:31,214 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,214 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:31,214 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:31,214 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:31,215 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:31,215 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:31,215 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,257 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:31,257 DEBUG: Start:	 Training
-2016-09-06 10:07:31,275 DEBUG: Info:	 Time for Training: 0.061126947403[s]
-2016-09-06 10:07:31,275 DEBUG: Done:	 Training
-2016-09-06 10:07:31,275 DEBUG: Start:	 Predicting
-2016-09-06 10:07:31,277 DEBUG: Done:	 Predicting
-2016-09-06 10:07:31,277 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:31,279 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:31,279 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1250
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,279 INFO: Done:	 Result Analysis
-2016-09-06 10:07:31,280 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,280 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:31,280 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:31,280 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:31,280 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:31,281 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:31,281 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,329 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:31,329 DEBUG: Start:	 Training
-2016-09-06 10:07:31,347 DEBUG: Info:	 Time for Training: 0.0671949386597[s]
-2016-09-06 10:07:31,347 DEBUG: Done:	 Training
-2016-09-06 10:07:31,347 DEBUG: Start:	 Predicting
-2016-09-06 10:07:31,349 DEBUG: Done:	 Predicting
-2016-09-06 10:07:31,349 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:31,351 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:31,351 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 432
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,351 INFO: Done:	 Result Analysis
-2016-09-06 10:07:31,352 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,352 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:31,352 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:31,352 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:31,352 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:31,352 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:31,353 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,393 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:31,393 DEBUG: Start:	 Training
-2016-09-06 10:07:31,409 DEBUG: Info:	 Time for Training: 0.0578751564026[s]
-2016-09-06 10:07:31,410 DEBUG: Done:	 Training
-2016-09-06 10:07:31,410 DEBUG: Start:	 Predicting
-2016-09-06 10:07:31,414 DEBUG: Done:	 Predicting
-2016-09-06 10:07:31,414 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:31,415 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:31,415 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6005
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,415 INFO: Done:	 Result Analysis
-2016-09-06 10:07:31,416 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,416 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:31,417 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:31,417 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,417 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,417 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:31,417 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,461 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:31,461 DEBUG: Start:	 Training
-2016-09-06 10:07:31,465 DEBUG: Info:	 Time for Training: 0.0488121509552[s]
-2016-09-06 10:07:31,465 DEBUG: Done:	 Training
-2016-09-06 10:07:31,465 DEBUG: Start:	 Predicting
-2016-09-06 10:07:31,467 DEBUG: Done:	 Predicting
-2016-09-06 10:07:31,467 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:31,469 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:31,469 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,469 INFO: Done:	 Result Analysis
-2016-09-06 10:07:31,470 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,470 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:31,470 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:31,471 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,471 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,471 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:31,471 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,502 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:31,502 DEBUG: Start:	 Training
-2016-09-06 10:07:31,505 DEBUG: Info:	 Time for Training: 0.0348341464996[s]
-2016-09-06 10:07:31,505 DEBUG: Done:	 Training
-2016-09-06 10:07:31,505 DEBUG: Start:	 Predicting
-2016-09-06 10:07:31,506 DEBUG: Done:	 Predicting
-2016-09-06 10:07:31,506 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:31,507 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:31,507 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,508 INFO: Done:	 Result Analysis
-2016-09-06 10:07:31,509 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,509 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:31,509 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:31,509 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,509 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,509 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:31,509 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,535 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:31,535 DEBUG: Start:	 Training
-2016-09-06 10:07:31,535 DEBUG: Info:	 Time for Training: 0.0270109176636[s]
-2016-09-06 10:07:31,535 DEBUG: Done:	 Training
-2016-09-06 10:07:31,535 DEBUG: Start:	 Predicting
-2016-09-06 10:07:31,540 DEBUG: Done:	 Predicting
-2016-09-06 10:07:31,540 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:31,541 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:31,541 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.628571428571
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,541 INFO: Done:	 Result Analysis
-2016-09-06 10:07:31,542 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,542 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:31,542 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:31,543 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,543 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,543 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:31,543 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,808 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:31,808 DEBUG: Start:	 Training
-2016-09-06 10:07:31,847 DEBUG: Info:	 Time for Training: 0.305594921112[s]
-2016-09-06 10:07:31,848 DEBUG: Done:	 Training
-2016-09-06 10:07:31,848 DEBUG: Start:	 Predicting
-2016-09-06 10:07:31,852 DEBUG: Done:	 Predicting
-2016-09-06 10:07:31,852 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:31,854 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:31,854 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 15, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,854 INFO: Done:	 Result Analysis
-2016-09-06 10:07:31,855 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,855 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:31,855 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:31,855 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,855 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,856 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:31,856 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,893 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:31,894 DEBUG: Start:	 Training
-2016-09-06 10:07:31,894 DEBUG: Info:	 Time for Training: 0.0398690700531[s]
-2016-09-06 10:07:31,895 DEBUG: Done:	 Training
-2016-09-06 10:07:31,895 DEBUG: Start:	 Predicting
-2016-09-06 10:07:31,896 DEBUG: Done:	 Predicting
-2016-09-06 10:07:31,896 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:31,897 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:31,897 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,898 INFO: Done:	 Result Analysis
-2016-09-06 10:07:31,899 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,899 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:31,899 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:31,899 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,899 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,899 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:31,899 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,943 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:31,943 DEBUG: Start:	 Training
-2016-09-06 10:07:31,963 DEBUG: Info:	 Time for Training: 0.0644710063934[s]
-2016-09-06 10:07:31,963 DEBUG: Done:	 Training
-2016-09-06 10:07:31,963 DEBUG: Start:	 Predicting
-2016-09-06 10:07:31,965 DEBUG: Done:	 Predicting
-2016-09-06 10:07:31,965 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:31,967 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:31,967 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6060
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,967 INFO: Done:	 Result Analysis
-2016-09-06 10:07:31,968 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:31,968 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:31,968 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,968 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,969 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:31,969 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,017 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:32,017 DEBUG: Start:	 Training
-2016-09-06 10:07:32,034 DEBUG: Info:	 Time for Training: 0.0663030147552[s]
-2016-09-06 10:07:32,034 DEBUG: Done:	 Training
-2016-09-06 10:07:32,034 DEBUG: Start:	 Predicting
-2016-09-06 10:07:32,037 DEBUG: Done:	 Predicting
-2016-09-06 10:07:32,037 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:32,038 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:32,039 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 569
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,039 INFO: Done:	 Result Analysis
-2016-09-06 10:07:32,040 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,040 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:32,040 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:32,040 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:32,040 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:32,040 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:32,041 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,082 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:32,082 DEBUG: Start:	 Training
-2016-09-06 10:07:32,098 DEBUG: Info:	 Time for Training: 0.0586409568787[s]
-2016-09-06 10:07:32,098 DEBUG: Done:	 Training
-2016-09-06 10:07:32,098 DEBUG: Start:	 Predicting
-2016-09-06 10:07:32,103 DEBUG: Done:	 Predicting
-2016-09-06 10:07:32,103 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:32,104 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:32,104 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1510
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,104 INFO: Done:	 Result Analysis
-2016-09-06 10:07:32,105 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,105 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:32,106 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:32,106 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,106 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,106 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:32,106 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,152 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:32,152 DEBUG: Start:	 Training
-2016-09-06 10:07:32,156 DEBUG: Info:	 Time for Training: 0.0510900020599[s]
-2016-09-06 10:07:32,156 DEBUG: Done:	 Training
-2016-09-06 10:07:32,156 DEBUG: Start:	 Predicting
-2016-09-06 10:07:32,158 DEBUG: Done:	 Predicting
-2016-09-06 10:07:32,158 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:32,160 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:32,160 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,160 INFO: Done:	 Result Analysis
-2016-09-06 10:07:32,161 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,161 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:32,161 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:32,162 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,162 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,162 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:32,162 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,195 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:32,195 DEBUG: Start:	 Training
-2016-09-06 10:07:32,198 DEBUG: Info:	 Time for Training: 0.0367488861084[s]
-2016-09-06 10:07:32,198 DEBUG: Done:	 Training
-2016-09-06 10:07:32,198 DEBUG: Start:	 Predicting
-2016-09-06 10:07:32,199 DEBUG: Done:	 Predicting
-2016-09-06 10:07:32,199 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:32,201 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:32,201 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,201 INFO: Done:	 Result Analysis
-2016-09-06 10:07:32,202 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,202 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:32,202 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:32,202 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,202 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,202 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:32,203 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,228 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:32,228 DEBUG: Start:	 Training
-2016-09-06 10:07:32,229 DEBUG: Info:	 Time for Training: 0.0273449420929[s]
-2016-09-06 10:07:32,229 DEBUG: Done:	 Training
-2016-09-06 10:07:32,229 DEBUG: Start:	 Predicting
-2016-09-06 10:07:32,234 DEBUG: Done:	 Predicting
-2016-09-06 10:07:32,234 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:32,235 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:32,235 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,235 INFO: Done:	 Result Analysis
-2016-09-06 10:07:32,236 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,236 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:32,236 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:32,237 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,237 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,237 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:32,237 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,394 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:32,395 DEBUG: Start:	 Training
-2016-09-06 10:07:32,416 DEBUG: Info:	 Time for Training: 0.180504083633[s]
-2016-09-06 10:07:32,417 DEBUG: Done:	 Training
-2016-09-06 10:07:32,417 DEBUG: Start:	 Predicting
-2016-09-06 10:07:32,420 DEBUG: Done:	 Predicting
-2016-09-06 10:07:32,420 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:32,421 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:32,421 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,421 INFO: Done:	 Result Analysis
-2016-09-06 10:07:32,423 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,423 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:32,423 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:32,423 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,423 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,423 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:32,423 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,461 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:32,461 DEBUG: Start:	 Training
-2016-09-06 10:07:32,462 DEBUG: Info:	 Time for Training: 0.0399260520935[s]
-2016-09-06 10:07:32,462 DEBUG: Done:	 Training
-2016-09-06 10:07:32,462 DEBUG: Start:	 Predicting
-2016-09-06 10:07:32,464 DEBUG: Done:	 Predicting
-2016-09-06 10:07:32,464 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:32,465 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:32,465 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,465 INFO: Done:	 Result Analysis
-2016-09-06 10:07:32,466 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,466 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:32,466 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:32,467 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,467 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,467 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:32,467 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,512 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:32,512 DEBUG: Start:	 Training
-2016-09-06 10:07:32,532 DEBUG: Info:	 Time for Training: 0.0659711360931[s]
-2016-09-06 10:07:32,532 DEBUG: Done:	 Training
-2016-09-06 10:07:32,532 DEBUG: Start:	 Predicting
-2016-09-06 10:07:32,535 DEBUG: Done:	 Predicting
-2016-09-06 10:07:32,535 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:32,536 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:32,536 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3802
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,536 INFO: Done:	 Result Analysis
-2016-09-06 10:07:32,537 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,537 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:32,537 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:32,538 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,538 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,538 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:32,538 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,587 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:32,587 DEBUG: Start:	 Training
-2016-09-06 10:07:32,607 DEBUG: Info:	 Time for Training: 0.0699560642242[s]
-2016-09-06 10:07:32,607 DEBUG: Done:	 Training
-2016-09-06 10:07:32,607 DEBUG: Start:	 Predicting
-2016-09-06 10:07:32,610 DEBUG: Done:	 Predicting
-2016-09-06 10:07:32,610 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:32,612 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:32,612 INFO: Classification on Fake database for View3 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6378
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,612 INFO: Done:	 Result Analysis
-2016-09-06 10:07:32,613 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,613 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:32,613 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:32,613 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,614 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,614 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:32,614 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,656 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:32,656 DEBUG: Start:	 Training
-2016-09-06 10:07:32,673 DEBUG: Info:	 Time for Training: 0.0603799819946[s]
-2016-09-06 10:07:32,673 DEBUG: Done:	 Training
-2016-09-06 10:07:32,673 DEBUG: Start:	 Predicting
-2016-09-06 10:07:32,678 DEBUG: Done:	 Predicting
-2016-09-06 10:07:32,678 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:32,679 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:32,679 INFO: Classification on Fake database for View3 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6783
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,680 INFO: Done:	 Result Analysis
-2016-09-06 10:07:32,681 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:07:32,681 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:07:32,682 INFO: Info:	 Shape of View0 :(300, 11)
-2016-09-06 10:07:32,682 INFO: Info:	 Shape of View1 :(300, 8)
-2016-09-06 10:07:32,683 INFO: Info:	 Shape of View2 :(300, 14)
-2016-09-06 10:07:32,683 INFO: Info:	 Shape of View3 :(300, 17)
-2016-09-06 10:07:32,683 INFO: Done:	 Read Database Files
-2016-09-06 10:07:32,683 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:07:32,687 INFO: Done:	 Determine validation split
-2016-09-06 10:07:32,687 INFO: Start:	 Determine 5 folds
-2016-09-06 10:07:32,693 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:07:32,693 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:07:32,693 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:07:32,693 INFO: Done:	 Determine folds
-2016-09-06 10:07:32,693 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:07:32,693 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 10:07:32,694 DEBUG: 	Start:	 Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index eb326c0f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ae733e19..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 21
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3db0decf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 67f77df1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.766666666667
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 18, max_depth : 3
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.766666666667
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 20a567ad..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 22e8c074..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 491
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8c9cc330..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2405
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 41068448..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9676
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7029410e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 13ea4b2b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 23
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f008d4bd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.628571428571
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e9ecf6a0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 15, max_depth : 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5522a9de..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index da23f07f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6060
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 112d345d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 432
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 271f35f0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6005
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dded33ad..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 13882acd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6dd96027..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 13
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 218c51f1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e4f1a860..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 96c36fc1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3802
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 37117611..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6378
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 824dde85..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6783
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index d22ae631..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1376 +0,0 @@
-2016-09-06 10:07:38,682 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:07:38,682 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00015915625 Gbytes /!\ 
-2016-09-06 10:07:43,694 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:07:43,696 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:07:43,766 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:43,766 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:43,767 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:43,767 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:43,768 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:43,768 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:43,768 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:43,768 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:43,768 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:43,768 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:43,769 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:43,769 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:43,769 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:43,769 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:43,808 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:43,808 DEBUG: Start:	 Training
-2016-09-06 10:07:43,810 DEBUG: Info:	 Time for Training: 0.0434219837189[s]
-2016-09-06 10:07:43,810 DEBUG: Done:	 Training
-2016-09-06 10:07:43,810 DEBUG: Start:	 Predicting
-2016-09-06 10:07:43,813 DEBUG: Done:	 Predicting
-2016-09-06 10:07:43,813 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:43,815 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:43,815 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:43,816 INFO: Done:	 Result Analysis
-2016-09-06 10:07:43,825 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:43,826 DEBUG: Start:	 Training
-2016-09-06 10:07:43,831 DEBUG: Info:	 Time for Training: 0.0656042098999[s]
-2016-09-06 10:07:43,831 DEBUG: Done:	 Training
-2016-09-06 10:07:43,831 DEBUG: Start:	 Predicting
-2016-09-06 10:07:43,834 DEBUG: Done:	 Predicting
-2016-09-06 10:07:43,834 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:43,836 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:43,836 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:07:43,836 INFO: Done:	 Result Analysis
-2016-09-06 10:07:43,916 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:43,916 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:43,916 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:43,916 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:43,916 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:43,916 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:43,917 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:43,917 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:43,917 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:43,917 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:43,917 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:43,917 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:43,918 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:43,918 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:43,951 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:43,951 DEBUG: Start:	 Training
-2016-09-06 10:07:43,952 DEBUG: Info:	 Time for Training: 0.0365099906921[s]
-2016-09-06 10:07:43,952 DEBUG: Done:	 Training
-2016-09-06 10:07:43,952 DEBUG: Start:	 Predicting
-2016-09-06 10:07:43,958 DEBUG: Done:	 Predicting
-2016-09-06 10:07:43,959 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:43,960 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:43,960 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 32
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:43,960 INFO: Done:	 Result Analysis
-2016-09-06 10:07:44,207 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:44,207 DEBUG: Start:	 Training
-2016-09-06 10:07:44,249 DEBUG: Info:	 Time for Training: 0.333906173706[s]
-2016-09-06 10:07:44,249 DEBUG: Done:	 Training
-2016-09-06 10:07:44,249 DEBUG: Start:	 Predicting
-2016-09-06 10:07:44,255 DEBUG: Done:	 Predicting
-2016-09-06 10:07:44,255 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:44,257 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:44,257 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 16, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,257 INFO: Done:	 Result Analysis
-2016-09-06 10:07:44,364 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,364 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,364 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:44,364 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:44,365 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:44,365 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:44,365 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:44,365 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:44,366 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:44,366 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:44,366 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:44,366 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:44,366 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,366 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,435 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:44,436 DEBUG: Start:	 Training
-2016-09-06 10:07:44,437 DEBUG: Info:	 Time for Training: 0.0736479759216[s]
-2016-09-06 10:07:44,437 DEBUG: Done:	 Training
-2016-09-06 10:07:44,437 DEBUG: Start:	 Predicting
-2016-09-06 10:07:44,441 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:44,441 DEBUG: Start:	 Training
-2016-09-06 10:07:44,450 DEBUG: Done:	 Predicting
-2016-09-06 10:07:44,450 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:44,453 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:44,453 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,454 INFO: Done:	 Result Analysis
-2016-09-06 10:07:44,468 DEBUG: Info:	 Time for Training: 0.104951143265[s]
-2016-09-06 10:07:44,468 DEBUG: Done:	 Training
-2016-09-06 10:07:44,468 DEBUG: Start:	 Predicting
-2016-09-06 10:07:44,472 DEBUG: Done:	 Predicting
-2016-09-06 10:07:44,472 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:44,473 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:44,473 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,473 INFO: Done:	 Result Analysis
-2016-09-06 10:07:44,609 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,609 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,609 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:44,609 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:44,609 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:44,609 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:44,610 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:44,610 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:44,610 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:44,610 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:44,610 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:44,610 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,610 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:44,610 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,657 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:44,657 DEBUG: Start:	 Training
-2016-09-06 10:07:44,663 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:44,663 DEBUG: Start:	 Training
-2016-09-06 10:07:44,675 DEBUG: Info:	 Time for Training: 0.066269159317[s]
-2016-09-06 10:07:44,675 DEBUG: Done:	 Training
-2016-09-06 10:07:44,675 DEBUG: Start:	 Predicting
-2016-09-06 10:07:44,680 DEBUG: Done:	 Predicting
-2016-09-06 10:07:44,681 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:44,682 DEBUG: Info:	 Time for Training: 0.0731539726257[s]
-2016-09-06 10:07:44,682 DEBUG: Done:	 Training
-2016-09-06 10:07:44,682 DEBUG: Start:	 Predicting
-2016-09-06 10:07:44,682 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:44,682 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,682 INFO: Done:	 Result Analysis
-2016-09-06 10:07:44,686 DEBUG: Done:	 Predicting
-2016-09-06 10:07:44,686 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:44,687 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:44,687 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,688 INFO: Done:	 Result Analysis
-2016-09-06 10:07:44,764 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,764 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,764 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:44,764 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:44,764 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:44,764 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:44,766 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:44,766 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:44,766 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:44,766 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:44,766 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:44,766 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:44,766 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,766 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,823 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:44,823 DEBUG: Start:	 Training
-2016-09-06 10:07:44,826 DEBUG: Info:	 Time for Training: 0.0639500617981[s]
-2016-09-06 10:07:44,827 DEBUG: Done:	 Training
-2016-09-06 10:07:44,827 DEBUG: Start:	 Predicting
-2016-09-06 10:07:44,830 DEBUG: Done:	 Predicting
-2016-09-06 10:07:44,831 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:44,832 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:44,833 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,833 INFO: Done:	 Result Analysis
-2016-09-06 10:07:44,841 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:44,841 DEBUG: Start:	 Training
-2016-09-06 10:07:44,845 DEBUG: Info:	 Time for Training: 0.0828351974487[s]
-2016-09-06 10:07:44,845 DEBUG: Done:	 Training
-2016-09-06 10:07:44,846 DEBUG: Start:	 Predicting
-2016-09-06 10:07:44,848 DEBUG: Done:	 Predicting
-2016-09-06 10:07:44,848 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:44,850 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:44,850 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,851 INFO: Done:	 Result Analysis
-2016-09-06 10:07:44,909 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,910 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:44,910 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,910 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:44,910 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:44,910 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:44,911 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:44,911 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:44,911 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:44,912 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:44,912 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:44,912 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,912 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:44,912 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,945 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:44,946 DEBUG: Start:	 Training
-2016-09-06 10:07:44,946 DEBUG: Info:	 Time for Training: 0.037682056427[s]
-2016-09-06 10:07:44,946 DEBUG: Done:	 Training
-2016-09-06 10:07:44,946 DEBUG: Start:	 Predicting
-2016-09-06 10:07:44,953 DEBUG: Done:	 Predicting
-2016-09-06 10:07:44,954 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:44,955 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:44,955 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 32
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,955 INFO: Done:	 Result Analysis
-2016-09-06 10:07:45,240 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:45,240 DEBUG: Start:	 Training
-2016-09-06 10:07:45,287 DEBUG: Info:	 Time for Training: 0.378414154053[s]
-2016-09-06 10:07:45,287 DEBUG: Done:	 Training
-2016-09-06 10:07:45,288 DEBUG: Start:	 Predicting
-2016-09-06 10:07:45,295 DEBUG: Done:	 Predicting
-2016-09-06 10:07:45,295 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:45,296 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:45,296 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 16, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,296 INFO: Done:	 Result Analysis
-2016-09-06 10:07:45,357 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,358 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:45,358 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:45,359 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,359 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:45,359 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:45,359 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:45,359 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:45,359 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:45,359 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,360 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:45,360 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:45,360 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:45,360 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,411 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:45,411 DEBUG: Start:	 Training
-2016-09-06 10:07:45,412 DEBUG: Info:	 Time for Training: 0.0539691448212[s]
-2016-09-06 10:07:45,412 DEBUG: Done:	 Training
-2016-09-06 10:07:45,412 DEBUG: Start:	 Predicting
-2016-09-06 10:07:45,417 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:45,417 DEBUG: Start:	 Training
-2016-09-06 10:07:45,423 DEBUG: Done:	 Predicting
-2016-09-06 10:07:45,423 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:45,426 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:45,426 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,426 INFO: Done:	 Result Analysis
-2016-09-06 10:07:45,441 DEBUG: Info:	 Time for Training: 0.0843439102173[s]
-2016-09-06 10:07:45,441 DEBUG: Done:	 Training
-2016-09-06 10:07:45,441 DEBUG: Start:	 Predicting
-2016-09-06 10:07:45,445 DEBUG: Done:	 Predicting
-2016-09-06 10:07:45,445 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:45,446 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:45,446 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,447 INFO: Done:	 Result Analysis
-2016-09-06 10:07:45,509 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,509 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,509 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:45,509 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:45,510 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:45,510 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:45,510 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:45,510 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:45,511 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:45,511 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:45,511 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:45,511 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:45,511 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,511 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,559 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:45,559 DEBUG: Start:	 Training
-2016-09-06 10:07:45,562 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:45,562 DEBUG: Start:	 Training
-2016-09-06 10:07:45,577 DEBUG: Info:	 Time for Training: 0.0689029693604[s]
-2016-09-06 10:07:45,577 DEBUG: Done:	 Training
-2016-09-06 10:07:45,577 DEBUG: Start:	 Predicting
-2016-09-06 10:07:45,583 DEBUG: Info:	 Time for Training: 0.0745868682861[s]
-2016-09-06 10:07:45,583 DEBUG: Done:	 Training
-2016-09-06 10:07:45,583 DEBUG: Start:	 Predicting
-2016-09-06 10:07:45,583 DEBUG: Done:	 Predicting
-2016-09-06 10:07:45,583 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:45,585 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:45,585 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,585 INFO: Done:	 Result Analysis
-2016-09-06 10:07:45,587 DEBUG: Done:	 Predicting
-2016-09-06 10:07:45,587 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:45,588 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:45,588 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,589 INFO: Done:	 Result Analysis
-2016-09-06 10:07:45,663 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,663 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,664 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:45,664 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:45,664 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:45,664 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:45,665 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:45,665 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:45,665 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:45,665 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:45,666 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:45,666 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:45,666 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,666 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,725 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:45,725 DEBUG: Start:	 Training
-2016-09-06 10:07:45,729 DEBUG: Info:	 Time for Training: 0.0669369697571[s]
-2016-09-06 10:07:45,729 DEBUG: Done:	 Training
-2016-09-06 10:07:45,729 DEBUG: Start:	 Predicting
-2016-09-06 10:07:45,733 DEBUG: Done:	 Predicting
-2016-09-06 10:07:45,733 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:45,735 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:45,735 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,735 INFO: Done:	 Result Analysis
-2016-09-06 10:07:45,743 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:45,744 DEBUG: Start:	 Training
-2016-09-06 10:07:45,748 DEBUG: Info:	 Time for Training: 0.086000919342[s]
-2016-09-06 10:07:45,748 DEBUG: Done:	 Training
-2016-09-06 10:07:45,748 DEBUG: Start:	 Predicting
-2016-09-06 10:07:45,751 DEBUG: Done:	 Predicting
-2016-09-06 10:07:45,751 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:45,753 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:45,753 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,754 INFO: Done:	 Result Analysis
-2016-09-06 10:07:45,915 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,915 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:45,915 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,916 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:45,916 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:45,916 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:45,917 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:45,917 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:45,917 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:45,917 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:45,917 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:45,918 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:45,918 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,918 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,967 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:45,967 DEBUG: Start:	 Training
-2016-09-06 10:07:45,968 DEBUG: Info:	 Time for Training: 0.0544729232788[s]
-2016-09-06 10:07:45,968 DEBUG: Done:	 Training
-2016-09-06 10:07:45,968 DEBUG: Start:	 Predicting
-2016-09-06 10:07:45,979 DEBUG: Done:	 Predicting
-2016-09-06 10:07:45,979 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:45,982 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:45,982 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 32
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,982 INFO: Done:	 Result Analysis
-2016-09-06 10:07:46,233 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:46,233 DEBUG: Start:	 Training
-2016-09-06 10:07:46,276 DEBUG: Info:	 Time for Training: 0.361407995224[s]
-2016-09-06 10:07:46,276 DEBUG: Done:	 Training
-2016-09-06 10:07:46,276 DEBUG: Start:	 Predicting
-2016-09-06 10:07:46,281 DEBUG: Done:	 Predicting
-2016-09-06 10:07:46,281 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:46,283 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:46,283 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 16, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,283 INFO: Done:	 Result Analysis
-2016-09-06 10:07:46,361 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,361 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:46,361 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:46,362 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,362 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:46,362 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:46,362 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:46,362 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:46,362 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:46,362 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,362 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:46,363 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:46,363 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:46,363 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,413 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:46,414 DEBUG: Start:	 Training
-2016-09-06 10:07:46,415 DEBUG: Info:	 Time for Training: 0.0534558296204[s]
-2016-09-06 10:07:46,415 DEBUG: Done:	 Training
-2016-09-06 10:07:46,415 DEBUG: Start:	 Predicting
-2016-09-06 10:07:46,416 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:46,416 DEBUG: Start:	 Training
-2016-09-06 10:07:46,437 DEBUG: Done:	 Predicting
-2016-09-06 10:07:46,437 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:46,437 DEBUG: Info:	 Time for Training: 0.077164888382[s]
-2016-09-06 10:07:46,437 DEBUG: Done:	 Training
-2016-09-06 10:07:46,438 DEBUG: Start:	 Predicting
-2016-09-06 10:07:46,438 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:46,439 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,439 INFO: Done:	 Result Analysis
-2016-09-06 10:07:46,442 DEBUG: Done:	 Predicting
-2016-09-06 10:07:46,442 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:46,443 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:46,443 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.433333333333
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.433333333333
-		- Score on test : 0.377777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,443 INFO: Done:	 Result Analysis
-2016-09-06 10:07:46,505 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,505 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:46,505 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:46,505 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,506 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:46,506 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:46,506 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:46,506 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:46,507 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:46,507 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:46,507 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,507 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:46,507 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:46,507 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,556 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:46,557 DEBUG: Start:	 Training
-2016-09-06 10:07:46,561 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:46,562 DEBUG: Start:	 Training
-2016-09-06 10:07:46,575 DEBUG: Info:	 Time for Training: 0.0704419612885[s]
-2016-09-06 10:07:46,575 DEBUG: Done:	 Training
-2016-09-06 10:07:46,575 DEBUG: Start:	 Predicting
-2016-09-06 10:07:46,581 DEBUG: Done:	 Predicting
-2016-09-06 10:07:46,581 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:46,583 DEBUG: Info:	 Time for Training: 0.0788052082062[s]
-2016-09-06 10:07:46,583 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:46,583 DEBUG: Done:	 Training
-2016-09-06 10:07:46,583 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,583 DEBUG: Start:	 Predicting
-2016-09-06 10:07:46,583 INFO: Done:	 Result Analysis
-2016-09-06 10:07:46,588 DEBUG: Done:	 Predicting
-2016-09-06 10:07:46,588 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:46,589 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:46,589 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,589 INFO: Done:	 Result Analysis
-2016-09-06 10:07:46,653 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,653 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,654 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:46,654 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:46,654 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:46,654 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:46,655 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:46,655 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:46,655 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:46,655 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:46,655 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:46,655 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:46,655 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,655 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,709 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:46,709 DEBUG: Start:	 Training
-2016-09-06 10:07:46,712 DEBUG: Info:	 Time for Training: 0.0588240623474[s]
-2016-09-06 10:07:46,712 DEBUG: Done:	 Training
-2016-09-06 10:07:46,712 DEBUG: Start:	 Predicting
-2016-09-06 10:07:46,715 DEBUG: Done:	 Predicting
-2016-09-06 10:07:46,716 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:46,718 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:46,718 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,718 INFO: Done:	 Result Analysis
-2016-09-06 10:07:46,727 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:46,727 DEBUG: Start:	 Training
-2016-09-06 10:07:46,731 DEBUG: Info:	 Time for Training: 0.0781581401825[s]
-2016-09-06 10:07:46,731 DEBUG: Done:	 Training
-2016-09-06 10:07:46,731 DEBUG: Start:	 Predicting
-2016-09-06 10:07:46,734 DEBUG: Done:	 Predicting
-2016-09-06 10:07:46,734 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:46,736 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:46,736 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,736 INFO: Done:	 Result Analysis
-2016-09-06 10:07:46,802 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,803 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:46,802 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,803 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:46,803 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:46,803 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:46,804 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:46,804 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:46,804 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:46,804 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:46,804 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:46,804 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:46,804 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,804 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,837 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:46,837 DEBUG: Start:	 Training
-2016-09-06 10:07:46,838 DEBUG: Info:	 Time for Training: 0.0363059043884[s]
-2016-09-06 10:07:46,838 DEBUG: Done:	 Training
-2016-09-06 10:07:46,838 DEBUG: Start:	 Predicting
-2016-09-06 10:07:46,844 DEBUG: Done:	 Predicting
-2016-09-06 10:07:46,845 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:46,846 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:46,846 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 32
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,846 INFO: Done:	 Result Analysis
-2016-09-06 10:07:47,123 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:47,123 DEBUG: Start:	 Training
-2016-09-06 10:07:47,170 DEBUG: Info:	 Time for Training: 0.368059158325[s]
-2016-09-06 10:07:47,170 DEBUG: Done:	 Training
-2016-09-06 10:07:47,170 DEBUG: Start:	 Predicting
-2016-09-06 10:07:47,175 DEBUG: Done:	 Predicting
-2016-09-06 10:07:47,176 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:47,177 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:47,177 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 16, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:47,177 INFO: Done:	 Result Analysis
-2016-09-06 10:07:47,249 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:47,249 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:47,249 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:47,249 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:47,249 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:47,249 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:07:47,250 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:47,250 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:47,250 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:47,250 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:47,250 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:47,250 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:07:47,250 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:47,251 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:07:47,297 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:47,297 DEBUG: Start:	 Training
-2016-09-06 10:07:47,298 DEBUG: Info:	 Time for Training: 0.049859046936[s]
-2016-09-06 10:07:47,298 DEBUG: Done:	 Training
-2016-09-06 10:07:47,298 DEBUG: Start:	 Predicting
-2016-09-06 10:07:47,305 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:07:47,305 DEBUG: Start:	 Training
-2016-09-06 10:07:47,324 DEBUG: Info:	 Time for Training: 0.0752458572388[s]
-2016-09-06 10:07:47,324 DEBUG: Done:	 Training
-2016-09-06 10:07:47,324 DEBUG: Start:	 Predicting
-2016-09-06 10:07:47,325 DEBUG: Done:	 Predicting
-2016-09-06 10:07:47,325 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:47,327 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:47,327 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:47,327 INFO: Done:	 Result Analysis
-2016-09-06 10:07:47,327 DEBUG: Done:	 Predicting
-2016-09-06 10:07:47,327 DEBUG: Start:	 Getting Results
-2016-09-06 10:07:47,329 DEBUG: Done:	 Getting Results
-2016-09-06 10:07:47,329 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:47,329 INFO: Done:	 Result Analysis
-2016-09-06 10:07:47,543 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:07:47,544 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:07:47,544 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:07:47,545 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 10:07:47,545 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:07:47,546 INFO: Info:	 Shape of View1 :(300, 17)
-2016-09-06 10:07:47,546 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 10:07:47,547 INFO: Info:	 Shape of View2 :(300, 20)
-2016-09-06 10:07:47,547 INFO: Info:	 Shape of View1 :(300, 17)
-2016-09-06 10:07:47,547 INFO: Info:	 Shape of View2 :(300, 20)
-2016-09-06 10:07:47,547 INFO: Info:	 Shape of View3 :(300, 10)
-2016-09-06 10:07:47,548 INFO: Done:	 Read Database Files
-2016-09-06 10:07:47,548 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:07:47,548 INFO: Info:	 Shape of View3 :(300, 10)
-2016-09-06 10:07:47,548 INFO: Done:	 Read Database Files
-2016-09-06 10:07:47,549 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:07:47,553 INFO: Done:	 Determine validation split
-2016-09-06 10:07:47,553 INFO: Start:	 Determine 5 folds
-2016-09-06 10:07:47,553 INFO: Done:	 Determine validation split
-2016-09-06 10:07:47,553 INFO: Start:	 Determine 5 folds
-2016-09-06 10:07:47,560 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 10:07:47,560 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 10:07:47,560 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:07:47,560 INFO: Done:	 Determine folds
-2016-09-06 10:07:47,560 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:07:47,561 INFO: Start:	 Classification
-2016-09-06 10:07:47,561 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 10:07:47,561 INFO: 	Start:	 Fold number 1
-2016-09-06 10:07:47,561 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 10:07:47,561 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:07:47,561 INFO: Done:	 Determine folds
-2016-09-06 10:07:47,561 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:07:47,561 INFO: Start:	 Classification
-2016-09-06 10:07:47,561 INFO: 	Start:	 Fold number 1
-2016-09-06 10:07:47,598 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:07:47,609 DEBUG: 			View 0 : 0.497175141243
-2016-09-06 10:07:47,616 DEBUG: 			View 1 : 0.480225988701
-2016-09-06 10:07:47,625 DEBUG: 			View 2 : 0.570621468927
-2016-09-06 10:07:47,632 DEBUG: 			View 3 : 0.570621468927
-2016-09-06 10:07:47,672 DEBUG: 			 Best view : 		View2
-2016-09-06 10:07:47,759 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:07:47,767 DEBUG: 			View 0 : 0.71186440678
-2016-09-06 10:07:47,778 DEBUG: 			View 1 : 0.717514124294
-2016-09-06 10:07:47,788 DEBUG: 			View 2 : 0.706214689266
-2016-09-06 10:07:47,798 DEBUG: 			View 3 : 0.745762711864
-2016-09-06 10:07:47,844 DEBUG: 			 Best view : 		View3
-2016-09-06 10:07:48,124 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:07:48,132 DEBUG: 			View 0 : 0.71186440678
-2016-09-06 10:07:48,140 DEBUG: 			View 1 : 0.717514124294
-2016-09-06 10:07:48,148 DEBUG: 			View 2 : 0.706214689266
-2016-09-06 10:07:48,155 DEBUG: 			View 3 : 0.745762711864
-2016-09-06 10:07:48,196 DEBUG: 			 Best view : 		View3
-2016-09-06 10:07:48,423 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:07:48,430 DEBUG: 			View 0 : 0.683615819209
-2016-09-06 10:07:48,438 DEBUG: 			View 1 : 0.677966101695
-2016-09-06 10:07:48,446 DEBUG: 			View 2 : 0.666666666667
-2016-09-06 10:07:48,453 DEBUG: 			View 3 : 0.706214689266
-2016-09-06 10:07:48,497 DEBUG: 			 Best view : 		View3
-2016-09-06 10:07:48,779 INFO: 	Start: 	 Classification
-2016-09-06 10:07:49,244 INFO: 	Done: 	 Fold number 1
-2016-09-06 10:07:49,244 INFO: 	Start:	 Fold number 2
-2016-09-06 10:07:49,274 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:07:49,285 DEBUG: 			View 0 : 0.508287292818
-2016-09-06 10:07:49,301 DEBUG: 			View 1 : 0.563535911602
-2016-09-06 10:07:49,309 DEBUG: 			View 2 : 0.497237569061
-2016-09-06 10:07:49,316 DEBUG: 			View 3 : 0.519337016575
-2016-09-06 10:07:49,348 DEBUG: 			 Best view : 		View0
-2016-09-06 10:07:49,429 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:07:49,436 DEBUG: 			View 0 : 0.707182320442
-2016-09-06 10:07:49,444 DEBUG: 			View 1 : 0.71270718232
-2016-09-06 10:07:49,451 DEBUG: 			View 2 : 0.723756906077
-2016-09-06 10:07:49,458 DEBUG: 			View 3 : 0.657458563536
-2016-09-06 10:07:49,497 DEBUG: 			 Best view : 		View2
-2016-09-06 10:07:49,646 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:07:49,654 DEBUG: 			View 0 : 0.707182320442
-2016-09-06 10:07:49,661 DEBUG: 			View 1 : 0.71270718232
-2016-09-06 10:07:49,669 DEBUG: 			View 2 : 0.723756906077
-2016-09-06 10:07:49,675 DEBUG: 			View 3 : 0.657458563536
-2016-09-06 10:07:49,717 DEBUG: 			 Best view : 		View2
-2016-09-06 10:07:49,935 INFO: 	Start: 	 Classification
-2016-09-06 10:07:50,288 INFO: 	Done: 	 Fold number 2
-2016-09-06 10:07:50,289 INFO: 	Start:	 Fold number 3
-2016-09-06 10:07:50,319 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:07:50,327 DEBUG: 			View 0 : 0.481081081081
-2016-09-06 10:07:50,334 DEBUG: 			View 1 : 0.491891891892
-2016-09-06 10:07:50,342 DEBUG: 			View 2 : 0.47027027027
-2016-09-06 10:07:50,349 DEBUG: 			View 3 : 0.508108108108
-2016-09-06 10:07:50,382 DEBUG: 			 Best view : 		View3
-2016-09-06 10:07:50,463 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:07:50,471 DEBUG: 			View 0 : 0.713513513514
-2016-09-06 10:07:50,478 DEBUG: 			View 1 : 0.702702702703
-2016-09-06 10:07:50,486 DEBUG: 			View 2 : 0.675675675676
-2016-09-06 10:07:50,493 DEBUG: 			View 3 : 0.654054054054
-2016-09-06 10:07:50,533 DEBUG: 			 Best view : 		View0
-2016-09-06 10:07:50,686 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:07:50,694 DEBUG: 			View 0 : 0.713513513514
-2016-09-06 10:07:50,701 DEBUG: 			View 1 : 0.702702702703
-2016-09-06 10:07:50,709 DEBUG: 			View 2 : 0.675675675676
-2016-09-06 10:07:50,716 DEBUG: 			View 3 : 0.654054054054
-2016-09-06 10:07:50,759 DEBUG: 			 Best view : 		View0
-2016-09-06 10:07:50,981 INFO: 	Start: 	 Classification
-2016-09-06 10:07:51,339 INFO: 	Done: 	 Fold number 3
-2016-09-06 10:07:51,340 INFO: 	Start:	 Fold number 4
-2016-09-06 10:07:51,369 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:07:51,376 DEBUG: 			View 0 : 0.483333333333
-2016-09-06 10:07:51,382 DEBUG: 			View 1 : 0.483333333333
-2016-09-06 10:07:51,389 DEBUG: 			View 2 : 0.483333333333
-2016-09-06 10:07:51,395 DEBUG: 			View 3 : 0.483333333333
-2016-09-06 10:07:51,396 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 10:07:51,428 DEBUG: 			 Best view : 		View0
-2016-09-06 10:07:51,507 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:07:51,515 DEBUG: 			View 0 : 0.738888888889
-2016-09-06 10:07:51,522 DEBUG: 			View 1 : 0.744444444444
-2016-09-06 10:07:51,530 DEBUG: 			View 2 : 0.761111111111
-2016-09-06 10:07:51,537 DEBUG: 			View 3 : 0.711111111111
-2016-09-06 10:07:51,575 DEBUG: 			 Best view : 		View2
-2016-09-06 10:07:51,724 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:07:51,732 DEBUG: 			View 0 : 0.738888888889
-2016-09-06 10:07:51,739 DEBUG: 			View 1 : 0.744444444444
-2016-09-06 10:07:51,746 DEBUG: 			View 2 : 0.761111111111
-2016-09-06 10:07:51,754 DEBUG: 			View 3 : 0.711111111111
-2016-09-06 10:07:51,795 DEBUG: 			 Best view : 		View2
-2016-09-06 10:07:52,011 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:07:52,018 DEBUG: 			View 0 : 0.677777777778
-2016-09-06 10:07:52,026 DEBUG: 			View 1 : 0.711111111111
-2016-09-06 10:07:52,033 DEBUG: 			View 2 : 0.694444444444
-2016-09-06 10:07:52,040 DEBUG: 			View 3 : 0.661111111111
-2016-09-06 10:07:52,084 DEBUG: 			 Best view : 		View0
-2016-09-06 10:07:52,368 INFO: 	Start: 	 Classification
-2016-09-06 10:07:52,836 INFO: 	Done: 	 Fold number 4
-2016-09-06 10:07:52,837 INFO: 	Start:	 Fold number 5
-2016-09-06 10:07:52,867 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:07:52,874 DEBUG: 			View 0 : 0.53591160221
-2016-09-06 10:07:52,881 DEBUG: 			View 1 : 0.563535911602
-2016-09-06 10:07:52,888 DEBUG: 			View 2 : 0.552486187845
-2016-09-06 10:07:52,895 DEBUG: 			View 3 : 0.513812154696
-2016-09-06 10:07:52,929 DEBUG: 			 Best view : 		View3
-2016-09-06 10:07:53,013 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:07:53,021 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 10:07:53,029 DEBUG: 			View 1 : 0.767955801105
-2016-09-06 10:07:53,036 DEBUG: 			View 2 : 0.696132596685
-2016-09-06 10:07:53,044 DEBUG: 			View 3 : 0.662983425414
-2016-09-06 10:07:53,084 DEBUG: 			 Best view : 		View1
-2016-09-06 10:07:53,237 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:07:53,244 DEBUG: 			View 0 : 0.690607734807
-2016-09-06 10:07:53,252 DEBUG: 			View 1 : 0.767955801105
-2016-09-06 10:07:53,259 DEBUG: 			View 2 : 0.696132596685
-2016-09-06 10:07:53,267 DEBUG: 			View 3 : 0.662983425414
-2016-09-06 10:07:53,311 DEBUG: 			 Best view : 		View1
-2016-09-06 10:07:53,532 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:07:53,539 DEBUG: 			View 0 : 0.624309392265
-2016-09-06 10:07:53,547 DEBUG: 			View 1 : 0.596685082873
-2016-09-06 10:07:53,554 DEBUG: 			View 2 : 0.723756906077
-2016-09-06 10:07:53,562 DEBUG: 			View 3 : 0.640883977901
-2016-09-06 10:07:53,606 DEBUG: 			 Best view : 		View2
-2016-09-06 10:07:53,901 INFO: 	Start: 	 Classification
-2016-09-06 10:07:54,384 INFO: 	Done: 	 Fold number 5
-2016-09-06 10:07:54,385 INFO: Done:	 Classification
-2016-09-06 10:07:54,385 INFO: Info:	 Time for Classification: 6[s]
-2016-09-06 10:07:54,385 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6e2ae222..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5fc348df..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 10de1910..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 32
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ecebfbe8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3476de3b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 03ccc8e3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 32
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e69ac2b1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 16, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6743e8cc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9d769aa7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6b7bdbac..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 97799939..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b51560b4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 94513afc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a44df1d6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 32
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6133fdbf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 16, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 268dc6e3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d2acb180..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 57b1b4cd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 622583f8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 922eef96..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4c6b0f49..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a9615039..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 32
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6e0fa905..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 16, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6352e1f5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f9f92f1b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.433333333333
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.433333333333
-		- Score on test : 0.377777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cd43ab94..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 253cd1cb..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 34a64102..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 16, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 54bf6a98..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fb0c6265..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8318
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index ccabbd2f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1370 +0,0 @@
-2016-09-06 10:08:34,156 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:08:34,156 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00014978125 Gbytes /!\ 
-2016-09-06 10:08:39,164 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:08:39,166 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:08:39,222 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,222 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,222 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:39,222 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:39,222 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:39,222 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:39,223 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,223 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,223 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,223 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:39,223 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,223 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,223 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:39,223 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,265 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:39,265 DEBUG: Start:	 Training
-2016-09-06 10:08:39,268 DEBUG: Info:	 Time for Training: 0.0466451644897[s]
-2016-09-06 10:08:39,268 DEBUG: Done:	 Training
-2016-09-06 10:08:39,268 DEBUG: Start:	 Predicting
-2016-09-06 10:08:39,271 DEBUG: Done:	 Predicting
-2016-09-06 10:08:39,271 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:39,272 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:39,272 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,273 INFO: Done:	 Result Analysis
-2016-09-06 10:08:39,287 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:39,287 DEBUG: Start:	 Training
-2016-09-06 10:08:39,292 DEBUG: Info:	 Time for Training: 0.0710310935974[s]
-2016-09-06 10:08:39,292 DEBUG: Done:	 Training
-2016-09-06 10:08:39,292 DEBUG: Start:	 Predicting
-2016-09-06 10:08:39,295 DEBUG: Done:	 Predicting
-2016-09-06 10:08:39,296 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:39,298 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:39,298 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,298 INFO: Done:	 Result Analysis
-2016-09-06 10:08:39,371 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,372 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:39,372 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:39,372 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,372 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:39,372 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:39,372 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,372 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,372 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,373 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:39,373 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,373 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:39,373 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,373 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,410 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:39,410 DEBUG: Start:	 Training
-2016-09-06 10:08:39,411 DEBUG: Info:	 Time for Training: 0.039687871933[s]
-2016-09-06 10:08:39,411 DEBUG: Done:	 Training
-2016-09-06 10:08:39,411 DEBUG: Start:	 Predicting
-2016-09-06 10:08:39,418 DEBUG: Done:	 Predicting
-2016-09-06 10:08:39,418 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:39,420 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:39,420 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,420 INFO: Done:	 Result Analysis
-2016-09-06 10:08:39,655 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:39,655 DEBUG: Start:	 Training
-2016-09-06 10:08:39,694 DEBUG: Info:	 Time for Training: 0.322620153427[s]
-2016-09-06 10:08:39,694 DEBUG: Done:	 Training
-2016-09-06 10:08:39,694 DEBUG: Start:	 Predicting
-2016-09-06 10:08:39,700 DEBUG: Done:	 Predicting
-2016-09-06 10:08:39,700 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:39,701 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:39,701 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 14, max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,702 INFO: Done:	 Result Analysis
-2016-09-06 10:08:39,821 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,822 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:39,822 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:39,822 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,822 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:39,822 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:39,823 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,823 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,823 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:39,823 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,823 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,823 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,823 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:39,823 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,869 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:39,869 DEBUG: Start:	 Training
-2016-09-06 10:08:39,870 DEBUG: Info:	 Time for Training: 0.0494568347931[s]
-2016-09-06 10:08:39,870 DEBUG: Done:	 Training
-2016-09-06 10:08:39,870 DEBUG: Start:	 Predicting
-2016-09-06 10:08:39,881 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:39,881 DEBUG: Start:	 Training
-2016-09-06 10:08:39,897 DEBUG: Done:	 Predicting
-2016-09-06 10:08:39,897 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:39,899 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:39,899 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,899 INFO: Done:	 Result Analysis
-2016-09-06 10:08:39,903 DEBUG: Info:	 Time for Training: 0.0822620391846[s]
-2016-09-06 10:08:39,904 DEBUG: Done:	 Training
-2016-09-06 10:08:39,904 DEBUG: Start:	 Predicting
-2016-09-06 10:08:39,907 DEBUG: Done:	 Predicting
-2016-09-06 10:08:39,907 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:39,908 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:39,909 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,909 INFO: Done:	 Result Analysis
-2016-09-06 10:08:39,972 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,972 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,972 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:08:39,972 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:08:39,973 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:39,973 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:39,973 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,973 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,974 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,974 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,974 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:39,974 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:39,974 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,974 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,022 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:40,022 DEBUG: Start:	 Training
-2016-09-06 10:08:40,026 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:40,026 DEBUG: Start:	 Training
-2016-09-06 10:08:40,039 DEBUG: Info:	 Time for Training: 0.0676848888397[s]
-2016-09-06 10:08:40,039 DEBUG: Done:	 Training
-2016-09-06 10:08:40,039 DEBUG: Start:	 Predicting
-2016-09-06 10:08:40,043 DEBUG: Info:	 Time for Training: 0.0717689990997[s]
-2016-09-06 10:08:40,043 DEBUG: Done:	 Training
-2016-09-06 10:08:40,043 DEBUG: Start:	 Predicting
-2016-09-06 10:08:40,045 DEBUG: Done:	 Predicting
-2016-09-06 10:08:40,045 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:40,047 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:40,047 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,047 INFO: Done:	 Result Analysis
-2016-09-06 10:08:40,047 DEBUG: Done:	 Predicting
-2016-09-06 10:08:40,047 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:40,049 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:40,049 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,049 INFO: Done:	 Result Analysis
-2016-09-06 10:08:40,117 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,117 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,117 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:40,117 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:40,117 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:40,117 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:40,118 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,118 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,118 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,118 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,118 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:40,118 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:40,118 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,118 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,164 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:40,164 DEBUG: Start:	 Training
-2016-09-06 10:08:40,167 DEBUG: Info:	 Time for Training: 0.0504739284515[s]
-2016-09-06 10:08:40,167 DEBUG: Done:	 Training
-2016-09-06 10:08:40,167 DEBUG: Start:	 Predicting
-2016-09-06 10:08:40,170 DEBUG: Done:	 Predicting
-2016-09-06 10:08:40,170 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:40,171 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:40,171 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,171 INFO: Done:	 Result Analysis
-2016-09-06 10:08:40,175 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:40,175 DEBUG: Start:	 Training
-2016-09-06 10:08:40,180 DEBUG: Info:	 Time for Training: 0.0639250278473[s]
-2016-09-06 10:08:40,181 DEBUG: Done:	 Training
-2016-09-06 10:08:40,181 DEBUG: Start:	 Predicting
-2016-09-06 10:08:40,184 DEBUG: Done:	 Predicting
-2016-09-06 10:08:40,184 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:40,186 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:40,186 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,186 INFO: Done:	 Result Analysis
-2016-09-06 10:08:40,268 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,268 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,268 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:40,268 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:40,268 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:40,268 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:40,269 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,269 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,269 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,269 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,269 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:40,269 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:40,270 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,270 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,303 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:40,303 DEBUG: Start:	 Training
-2016-09-06 10:08:40,303 DEBUG: Info:	 Time for Training: 0.0363190174103[s]
-2016-09-06 10:08:40,303 DEBUG: Done:	 Training
-2016-09-06 10:08:40,304 DEBUG: Start:	 Predicting
-2016-09-06 10:08:40,312 DEBUG: Done:	 Predicting
-2016-09-06 10:08:40,312 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:40,313 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:40,313 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,314 INFO: Done:	 Result Analysis
-2016-09-06 10:08:40,540 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:40,540 DEBUG: Start:	 Training
-2016-09-06 10:08:40,580 DEBUG: Info:	 Time for Training: 0.312906980515[s]
-2016-09-06 10:08:40,580 DEBUG: Done:	 Training
-2016-09-06 10:08:40,580 DEBUG: Start:	 Predicting
-2016-09-06 10:08:40,586 DEBUG: Done:	 Predicting
-2016-09-06 10:08:40,586 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:40,587 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:40,587 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 14, max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,587 INFO: Done:	 Result Analysis
-2016-09-06 10:08:40,724 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,724 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,725 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:40,725 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:40,725 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:40,725 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:40,726 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,726 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,726 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,726 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,726 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:40,726 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:40,726 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,726 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,774 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:40,774 DEBUG: Start:	 Training
-2016-09-06 10:08:40,775 DEBUG: Info:	 Time for Training: 0.0510659217834[s]
-2016-09-06 10:08:40,775 DEBUG: Done:	 Training
-2016-09-06 10:08:40,775 DEBUG: Start:	 Predicting
-2016-09-06 10:08:40,779 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:40,779 DEBUG: Start:	 Training
-2016-09-06 10:08:40,801 DEBUG: Done:	 Predicting
-2016-09-06 10:08:40,801 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:40,803 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:40,804 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,804 INFO: Done:	 Result Analysis
-2016-09-06 10:08:40,810 DEBUG: Info:	 Time for Training: 0.0859551429749[s]
-2016-09-06 10:08:40,810 DEBUG: Done:	 Training
-2016-09-06 10:08:40,810 DEBUG: Start:	 Predicting
-2016-09-06 10:08:40,816 DEBUG: Done:	 Predicting
-2016-09-06 10:08:40,816 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:40,818 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:40,818 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,818 INFO: Done:	 Result Analysis
-2016-09-06 10:08:40,981 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,981 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,981 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:08:40,981 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:08:40,981 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:40,981 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:40,982 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,982 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,982 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,982 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,982 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:40,982 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:40,982 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,982 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,039 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:41,039 DEBUG: Start:	 Training
-2016-09-06 10:08:41,041 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:41,041 DEBUG: Start:	 Training
-2016-09-06 10:08:41,060 DEBUG: Info:	 Time for Training: 0.0792870521545[s]
-2016-09-06 10:08:41,060 DEBUG: Done:	 Training
-2016-09-06 10:08:41,060 DEBUG: Start:	 Predicting
-2016-09-06 10:08:41,062 DEBUG: Info:	 Time for Training: 0.0816400051117[s]
-2016-09-06 10:08:41,062 DEBUG: Done:	 Training
-2016-09-06 10:08:41,062 DEBUG: Start:	 Predicting
-2016-09-06 10:08:41,066 DEBUG: Done:	 Predicting
-2016-09-06 10:08:41,067 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:41,067 DEBUG: Done:	 Predicting
-2016-09-06 10:08:41,067 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:41,068 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:41,068 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,068 INFO: Done:	 Result Analysis
-2016-09-06 10:08:41,069 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:41,069 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,069 INFO: Done:	 Result Analysis
-2016-09-06 10:08:41,127 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,128 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:41,128 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:41,128 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,129 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,129 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:41,129 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,129 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,129 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:41,129 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:41,130 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,130 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,130 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:41,130 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,179 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:41,179 DEBUG: Start:	 Training
-2016-09-06 10:08:41,181 DEBUG: Info:	 Time for Training: 0.0534510612488[s]
-2016-09-06 10:08:41,182 DEBUG: Done:	 Training
-2016-09-06 10:08:41,182 DEBUG: Start:	 Predicting
-2016-09-06 10:08:41,186 DEBUG: Done:	 Predicting
-2016-09-06 10:08:41,186 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:41,188 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:41,188 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,188 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:41,188 DEBUG: Start:	 Training
-2016-09-06 10:08:41,188 INFO: Done:	 Result Analysis
-2016-09-06 10:08:41,193 DEBUG: Info:	 Time for Training: 0.0664410591125[s]
-2016-09-06 10:08:41,193 DEBUG: Done:	 Training
-2016-09-06 10:08:41,194 DEBUG: Start:	 Predicting
-2016-09-06 10:08:41,197 DEBUG: Done:	 Predicting
-2016-09-06 10:08:41,197 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:41,199 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:41,199 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,199 INFO: Done:	 Result Analysis
-2016-09-06 10:08:41,275 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,275 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,276 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:41,276 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:41,276 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:41,276 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:41,276 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,276 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,276 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,276 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,277 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:41,277 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:41,277 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,277 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,312 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:41,312 DEBUG: Start:	 Training
-2016-09-06 10:08:41,313 DEBUG: Info:	 Time for Training: 0.0382509231567[s]
-2016-09-06 10:08:41,313 DEBUG: Done:	 Training
-2016-09-06 10:08:41,313 DEBUG: Start:	 Predicting
-2016-09-06 10:08:41,320 DEBUG: Done:	 Predicting
-2016-09-06 10:08:41,321 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:41,322 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:41,322 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,322 INFO: Done:	 Result Analysis
-2016-09-06 10:08:41,539 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:41,539 DEBUG: Start:	 Training
-2016-09-06 10:08:41,581 DEBUG: Info:	 Time for Training: 0.306406974792[s]
-2016-09-06 10:08:41,581 DEBUG: Done:	 Training
-2016-09-06 10:08:41,581 DEBUG: Start:	 Predicting
-2016-09-06 10:08:41,587 DEBUG: Done:	 Predicting
-2016-09-06 10:08:41,587 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:41,589 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:41,589 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 14, max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,589 INFO: Done:	 Result Analysis
-2016-09-06 10:08:41,723 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,723 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:41,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:41,723 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:41,723 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:41,724 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,724 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,724 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,724 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,725 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:41,725 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:41,725 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,725 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,775 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:41,775 DEBUG: Start:	 Training
-2016-09-06 10:08:41,776 DEBUG: Info:	 Time for Training: 0.0535860061646[s]
-2016-09-06 10:08:41,776 DEBUG: Done:	 Training
-2016-09-06 10:08:41,776 DEBUG: Start:	 Predicting
-2016-09-06 10:08:41,780 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:41,780 DEBUG: Start:	 Training
-2016-09-06 10:08:41,790 DEBUG: Done:	 Predicting
-2016-09-06 10:08:41,790 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:41,793 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:41,793 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,794 INFO: Done:	 Result Analysis
-2016-09-06 10:08:41,807 DEBUG: Info:	 Time for Training: 0.0841271877289[s]
-2016-09-06 10:08:41,807 DEBUG: Done:	 Training
-2016-09-06 10:08:41,807 DEBUG: Start:	 Predicting
-2016-09-06 10:08:41,810 DEBUG: Done:	 Predicting
-2016-09-06 10:08:41,810 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:41,812 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:41,812 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,812 INFO: Done:	 Result Analysis
-2016-09-06 10:08:41,874 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,874 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,874 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:08:41,874 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:08:41,874 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:41,874 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:41,875 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,875 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,875 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,875 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,875 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:41,875 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:41,876 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,876 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,927 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:41,927 DEBUG: Start:	 Training
-2016-09-06 10:08:41,935 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:41,935 DEBUG: Start:	 Training
-2016-09-06 10:08:41,945 DEBUG: Info:	 Time for Training: 0.0712940692902[s]
-2016-09-06 10:08:41,945 DEBUG: Done:	 Training
-2016-09-06 10:08:41,945 DEBUG: Start:	 Predicting
-2016-09-06 10:08:41,951 DEBUG: Done:	 Predicting
-2016-09-06 10:08:41,951 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:41,952 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:41,952 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,953 INFO: Done:	 Result Analysis
-2016-09-06 10:08:41,953 DEBUG: Info:	 Time for Training: 0.079491853714[s]
-2016-09-06 10:08:41,953 DEBUG: Done:	 Training
-2016-09-06 10:08:41,953 DEBUG: Start:	 Predicting
-2016-09-06 10:08:41,957 DEBUG: Done:	 Predicting
-2016-09-06 10:08:41,957 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:41,959 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:41,959 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,959 INFO: Done:	 Result Analysis
-2016-09-06 10:08:42,026 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,026 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,026 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:42,026 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:42,027 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:42,027 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:42,027 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,028 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,028 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,028 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,028 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:42,028 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,028 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:42,028 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,066 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:42,066 DEBUG: Start:	 Training
-2016-09-06 10:08:42,067 DEBUG: Info:	 Time for Training: 0.0417790412903[s]
-2016-09-06 10:08:42,068 DEBUG: Done:	 Training
-2016-09-06 10:08:42,068 DEBUG: Start:	 Predicting
-2016-09-06 10:08:42,071 DEBUG: Done:	 Predicting
-2016-09-06 10:08:42,071 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:42,072 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:42,072 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,073 INFO: Done:	 Result Analysis
-2016-09-06 10:08:42,084 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:42,084 DEBUG: Start:	 Training
-2016-09-06 10:08:42,088 DEBUG: Info:	 Time for Training: 0.0621321201324[s]
-2016-09-06 10:08:42,088 DEBUG: Done:	 Training
-2016-09-06 10:08:42,088 DEBUG: Start:	 Predicting
-2016-09-06 10:08:42,091 DEBUG: Done:	 Predicting
-2016-09-06 10:08:42,091 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:42,093 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:42,093 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,093 INFO: Done:	 Result Analysis
-2016-09-06 10:08:42,174 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,174 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,174 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:42,174 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:42,174 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:42,174 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:42,175 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,175 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,175 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,175 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,175 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:42,175 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:42,175 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,175 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,208 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:42,208 DEBUG: Start:	 Training
-2016-09-06 10:08:42,209 DEBUG: Info:	 Time for Training: 0.0357630252838[s]
-2016-09-06 10:08:42,209 DEBUG: Done:	 Training
-2016-09-06 10:08:42,209 DEBUG: Start:	 Predicting
-2016-09-06 10:08:42,215 DEBUG: Done:	 Predicting
-2016-09-06 10:08:42,215 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:42,216 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:42,216 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,217 INFO: Done:	 Result Analysis
-2016-09-06 10:08:42,450 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:42,451 DEBUG: Start:	 Training
-2016-09-06 10:08:42,492 DEBUG: Info:	 Time for Training: 0.318718194962[s]
-2016-09-06 10:08:42,492 DEBUG: Done:	 Training
-2016-09-06 10:08:42,492 DEBUG: Start:	 Predicting
-2016-09-06 10:08:42,498 DEBUG: Done:	 Predicting
-2016-09-06 10:08:42,498 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:42,500 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:42,500 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 14, max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.980952380952
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,500 INFO: Done:	 Result Analysis
-2016-09-06 10:08:42,623 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,624 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:42,624 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:42,625 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,625 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,625 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:42,625 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,625 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,626 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:42,626 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:42,627 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,627 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,627 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:42,627 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,673 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:42,674 DEBUG: Start:	 Training
-2016-09-06 10:08:42,674 DEBUG: Info:	 Time for Training: 0.0515320301056[s]
-2016-09-06 10:08:42,675 DEBUG: Done:	 Training
-2016-09-06 10:08:42,675 DEBUG: Start:	 Predicting
-2016-09-06 10:08:42,682 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:42,683 DEBUG: Start:	 Training
-2016-09-06 10:08:42,690 DEBUG: Done:	 Predicting
-2016-09-06 10:08:42,690 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:42,692 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:42,692 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,693 INFO: Done:	 Result Analysis
-2016-09-06 10:08:42,707 DEBUG: Info:	 Time for Training: 0.0825428962708[s]
-2016-09-06 10:08:42,707 DEBUG: Done:	 Training
-2016-09-06 10:08:42,707 DEBUG: Start:	 Predicting
-2016-09-06 10:08:42,710 DEBUG: Done:	 Predicting
-2016-09-06 10:08:42,710 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:42,711 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:42,712 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,712 INFO: Done:	 Result Analysis
-2016-09-06 10:08:42,917 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:08:42,918 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:08:42,919 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 10:08:42,919 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:08:42,920 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:08:42,920 INFO: Info:	 Shape of View1 :(300, 19)
-2016-09-06 10:08:42,921 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 10:08:42,921 INFO: Info:	 Shape of View2 :(300, 15)
-2016-09-06 10:08:42,921 INFO: Info:	 Shape of View1 :(300, 19)
-2016-09-06 10:08:42,922 INFO: Info:	 Shape of View3 :(300, 9)
-2016-09-06 10:08:42,922 INFO: Done:	 Read Database Files
-2016-09-06 10:08:42,922 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:08:42,922 INFO: Info:	 Shape of View2 :(300, 15)
-2016-09-06 10:08:42,923 INFO: Info:	 Shape of View3 :(300, 9)
-2016-09-06 10:08:42,923 INFO: Done:	 Read Database Files
-2016-09-06 10:08:42,924 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:08:42,931 INFO: Done:	 Determine validation split
-2016-09-06 10:08:42,931 INFO: Start:	 Determine 5 folds
-2016-09-06 10:08:42,932 INFO: Done:	 Determine validation split
-2016-09-06 10:08:42,932 INFO: Start:	 Determine 5 folds
-2016-09-06 10:08:42,944 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 10:08:42,945 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:08:42,945 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 10:08:42,945 INFO: Done:	 Determine folds
-2016-09-06 10:08:42,945 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 10:08:42,945 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:08:42,945 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:08:42,945 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 10:08:42,945 INFO: Start:	 Classification
-2016-09-06 10:08:42,945 INFO: Done:	 Determine folds
-2016-09-06 10:08:42,945 INFO: 	Start:	 Fold number 1
-2016-09-06 10:08:42,946 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:08:42,946 INFO: Start:	 Classification
-2016-09-06 10:08:42,946 INFO: 	Start:	 Fold number 1
-2016-09-06 10:08:43,005 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:08:43,014 DEBUG: 			View 0 : 0.463687150838
-2016-09-06 10:08:43,022 DEBUG: 			View 1 : 0.530726256983
-2016-09-06 10:08:43,029 DEBUG: 			View 2 : 0.541899441341
-2016-09-06 10:08:43,037 DEBUG: 			View 3 : 0.513966480447
-2016-09-06 10:08:43,071 DEBUG: 			 Best view : 		View3
-2016-09-06 10:08:43,154 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:08:43,161 DEBUG: 			View 0 : 0.653631284916
-2016-09-06 10:08:43,170 DEBUG: 			View 1 : 0.720670391061
-2016-09-06 10:08:43,178 DEBUG: 			View 2 : 0.720670391061
-2016-09-06 10:08:43,186 DEBUG: 			View 3 : 0.698324022346
-2016-09-06 10:08:43,226 DEBUG: 			 Best view : 		View1
-2016-09-06 10:08:43,378 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:08:43,394 DEBUG: 			View 0 : 0.653631284916
-2016-09-06 10:08:43,405 DEBUG: 			View 1 : 0.720670391061
-2016-09-06 10:08:43,413 DEBUG: 			View 2 : 0.720670391061
-2016-09-06 10:08:43,420 DEBUG: 			View 3 : 0.698324022346
-2016-09-06 10:08:43,462 DEBUG: 			 Best view : 		View1
-2016-09-06 10:08:43,721 INFO: 	Start: 	 Classification
-2016-09-06 10:08:44,142 INFO: 	Done: 	 Fold number 1
-2016-09-06 10:08:44,143 INFO: 	Start:	 Fold number 2
-2016-09-06 10:08:44,175 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:08:44,183 DEBUG: 			View 0 : 0.530386740331
-2016-09-06 10:08:44,191 DEBUG: 			View 1 : 0.591160220994
-2016-09-06 10:08:44,199 DEBUG: 			View 2 : 0.53591160221
-2016-09-06 10:08:44,206 DEBUG: 			View 3 : 0.491712707182
-2016-09-06 10:08:44,241 DEBUG: 			 Best view : 		View1
-2016-09-06 10:08:44,326 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:08:44,334 DEBUG: 			View 0 : 0.718232044199
-2016-09-06 10:08:44,342 DEBUG: 			View 1 : 0.696132596685
-2016-09-06 10:08:44,350 DEBUG: 			View 2 : 0.674033149171
-2016-09-06 10:08:44,358 DEBUG: 			View 3 : 0.635359116022
-2016-09-06 10:08:44,410 DEBUG: 			 Best view : 		View0
-2016-09-06 10:08:44,581 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:08:44,589 DEBUG: 			View 0 : 0.718232044199
-2016-09-06 10:08:44,596 DEBUG: 			View 1 : 0.696132596685
-2016-09-06 10:08:44,604 DEBUG: 			View 2 : 0.674033149171
-2016-09-06 10:08:44,611 DEBUG: 			View 3 : 0.635359116022
-2016-09-06 10:08:44,652 DEBUG: 			 Best view : 		View0
-2016-09-06 10:08:44,871 INFO: 	Start: 	 Classification
-2016-09-06 10:08:45,230 INFO: 	Done: 	 Fold number 2
-2016-09-06 10:08:45,230 INFO: 	Start:	 Fold number 3
-2016-09-06 10:08:45,259 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:08:45,267 DEBUG: 			View 0 : 0.505617977528
-2016-09-06 10:08:45,274 DEBUG: 			View 1 : 0.522471910112
-2016-09-06 10:08:45,281 DEBUG: 			View 2 : 0.516853932584
-2016-09-06 10:08:45,288 DEBUG: 			View 3 : 0.52808988764
-2016-09-06 10:08:45,320 DEBUG: 			 Best view : 		View1
-2016-09-06 10:08:45,399 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:08:45,406 DEBUG: 			View 0 : 0.629213483146
-2016-09-06 10:08:45,414 DEBUG: 			View 1 : 0.724719101124
-2016-09-06 10:08:45,421 DEBUG: 			View 2 : 0.691011235955
-2016-09-06 10:08:45,429 DEBUG: 			View 3 : 0.629213483146
-2016-09-06 10:08:45,467 DEBUG: 			 Best view : 		View1
-2016-09-06 10:08:45,616 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:08:45,624 DEBUG: 			View 0 : 0.629213483146
-2016-09-06 10:08:45,631 DEBUG: 			View 1 : 0.724719101124
-2016-09-06 10:08:45,639 DEBUG: 			View 2 : 0.691011235955
-2016-09-06 10:08:45,646 DEBUG: 			View 3 : 0.629213483146
-2016-09-06 10:08:45,686 DEBUG: 			 Best view : 		View1
-2016-09-06 10:08:45,910 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:08:45,918 DEBUG: 			View 0 : 0.623595505618
-2016-09-06 10:08:45,926 DEBUG: 			View 1 : 0.662921348315
-2016-09-06 10:08:45,934 DEBUG: 			View 2 : 0.651685393258
-2016-09-06 10:08:45,942 DEBUG: 			View 3 : 0.623595505618
-2016-09-06 10:08:45,986 DEBUG: 			 Best view : 		View0
-2016-09-06 10:08:46,283 INFO: 	Start: 	 Classification
-2016-09-06 10:08:46,944 INFO: 	Done: 	 Fold number 3
-2016-09-06 10:08:46,944 INFO: 	Start:	 Fold number 4
-2016-09-06 10:08:46,983 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:08:46,993 DEBUG: 			View 0 : 0.56043956044
-2016-09-06 10:08:47,001 DEBUG: 			View 1 : 0.521978021978
-2016-09-06 10:08:47,010 DEBUG: 			View 2 : 0.554945054945
-2016-09-06 10:08:47,018 DEBUG: 			View 3 : 0.521978021978
-2016-09-06 10:08:47,055 DEBUG: 			 Best view : 		View1
-2016-09-06 10:08:47,142 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:08:47,150 DEBUG: 			View 0 : 0.67032967033
-2016-09-06 10:08:47,158 DEBUG: 			View 1 : 0.736263736264
-2016-09-06 10:08:47,167 DEBUG: 			View 2 : 0.681318681319
-2016-09-06 10:08:47,175 DEBUG: 			View 3 : 0.714285714286
-2016-09-06 10:08:47,218 DEBUG: 			 Best view : 		View1
-2016-09-06 10:08:47,372 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:08:47,380 DEBUG: 			View 0 : 0.67032967033
-2016-09-06 10:08:47,388 DEBUG: 			View 1 : 0.736263736264
-2016-09-06 10:08:47,395 DEBUG: 			View 2 : 0.681318681319
-2016-09-06 10:08:47,402 DEBUG: 			View 3 : 0.714285714286
-2016-09-06 10:08:47,445 DEBUG: 			 Best view : 		View1
-2016-09-06 10:08:47,666 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:08:47,674 DEBUG: 			View 0 : 0.549450549451
-2016-09-06 10:08:47,682 DEBUG: 			View 1 : 0.763736263736
-2016-09-06 10:08:47,690 DEBUG: 			View 2 : 0.598901098901
-2016-09-06 10:08:47,697 DEBUG: 			View 3 : 0.637362637363
-2016-09-06 10:08:47,741 DEBUG: 			 Best view : 		View1
-2016-09-06 10:08:48,048 INFO: 	Start: 	 Classification
-2016-09-06 10:08:48,536 INFO: 	Done: 	 Fold number 4
-2016-09-06 10:08:48,536 INFO: 	Start:	 Fold number 5
-2016-09-06 10:08:48,569 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:08:48,577 DEBUG: 			View 0 : 0.491712707182
-2016-09-06 10:08:48,584 DEBUG: 			View 1 : 0.491712707182
-2016-09-06 10:08:48,594 DEBUG: 			View 2 : 0.491712707182
-2016-09-06 10:08:48,606 DEBUG: 			View 3 : 0.491712707182
-2016-09-06 10:08:48,606 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 10:08:48,657 DEBUG: 			 Best view : 		View0
-2016-09-06 10:08:48,759 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:08:48,767 DEBUG: 			View 0 : 0.651933701657
-2016-09-06 10:08:48,777 DEBUG: 			View 1 : 0.646408839779
-2016-09-06 10:08:48,789 DEBUG: 			View 2 : 0.662983425414
-2016-09-06 10:08:48,803 DEBUG: 			View 3 : 0.591160220994
-2016-09-06 10:08:48,867 DEBUG: 			 Best view : 		View2
-2016-09-06 10:08:49,051 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:08:49,059 DEBUG: 			View 0 : 0.651933701657
-2016-09-06 10:08:49,068 DEBUG: 			View 1 : 0.646408839779
-2016-09-06 10:08:49,077 DEBUG: 			View 2 : 0.662983425414
-2016-09-06 10:08:49,085 DEBUG: 			View 3 : 0.591160220994
-2016-09-06 10:08:49,135 DEBUG: 			 Best view : 		View2
-2016-09-06 10:08:49,386 INFO: 	Start: 	 Classification
-2016-09-06 10:08:49,796 INFO: 	Done: 	 Fold number 5
-2016-09-06 10:08:49,796 INFO: Done:	 Classification
-2016-09-06 10:08:49,796 INFO: Info:	 Time for Classification: 6[s]
-2016-09-06 10:08:49,796 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ddea2b4f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e1d14880..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 87eef941..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 57e7471b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 14, max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cbd7b4f1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b91d1651..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d413cf87..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c7edd45a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d0be9dd2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 20848fbc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 14, max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0da52710..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d1c231bd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1d1835ab..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3fd73c43..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 45bc125e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e933492b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 30c528bc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b47c31af..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 14, max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7e8ec934..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ea49c27..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ac93c75f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 47e6566c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 77221a3d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e0f6b680..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 917a2d1c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 84b2e632..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 14, max_depth : 28
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.980952380952
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0fabbd85..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ba1f9386..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7580
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 47a28cd4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1363 +0,0 @@
-2016-09-06 10:08:53,165 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:08:53,165 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.0001193125 Gbytes /!\ 
-2016-09-06 10:08:58,177 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:08:58,179 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:08:58,234 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,234 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,234 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:58,234 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:58,234 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:58,234 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:58,235 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,235 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,235 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,235 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,235 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:58,235 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:58,235 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,235 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,274 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:58,274 DEBUG: Start:	 Training
-2016-09-06 10:08:58,276 DEBUG: Info:	 Time for Training: 0.0428988933563[s]
-2016-09-06 10:08:58,276 DEBUG: Done:	 Training
-2016-09-06 10:08:58,276 DEBUG: Start:	 Predicting
-2016-09-06 10:08:58,279 DEBUG: Done:	 Predicting
-2016-09-06 10:08:58,279 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:58,280 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:58,280 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,281 INFO: Done:	 Result Analysis
-2016-09-06 10:08:58,288 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:58,288 DEBUG: Start:	 Training
-2016-09-06 10:08:58,293 DEBUG: Info:	 Time for Training: 0.0592339038849[s]
-2016-09-06 10:08:58,293 DEBUG: Done:	 Training
-2016-09-06 10:08:58,293 DEBUG: Start:	 Predicting
-2016-09-06 10:08:58,295 DEBUG: Done:	 Predicting
-2016-09-06 10:08:58,296 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:58,298 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:58,298 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,298 INFO: Done:	 Result Analysis
-2016-09-06 10:08:58,378 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,378 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,379 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:58,379 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:58,379 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:58,379 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:58,380 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,380 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,380 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,380 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,380 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:58,380 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:58,380 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,380 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,418 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:58,418 DEBUG: Start:	 Training
-2016-09-06 10:08:58,419 DEBUG: Info:	 Time for Training: 0.0408041477203[s]
-2016-09-06 10:08:58,419 DEBUG: Done:	 Training
-2016-09-06 10:08:58,419 DEBUG: Start:	 Predicting
-2016-09-06 10:08:58,426 DEBUG: Done:	 Predicting
-2016-09-06 10:08:58,426 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:58,428 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:58,428 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 35
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,428 INFO: Done:	 Result Analysis
-2016-09-06 10:08:58,469 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:58,469 DEBUG: Start:	 Training
-2016-09-06 10:08:58,478 DEBUG: Info:	 Time for Training: 0.0997009277344[s]
-2016-09-06 10:08:58,478 DEBUG: Done:	 Training
-2016-09-06 10:08:58,478 DEBUG: Start:	 Predicting
-2016-09-06 10:08:58,481 DEBUG: Done:	 Predicting
-2016-09-06 10:08:58,481 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:58,483 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:58,483 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,483 INFO: Done:	 Result Analysis
-2016-09-06 10:08:58,633 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,633 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,634 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:58,634 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:58,634 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:58,634 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:58,635 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,635 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,635 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,635 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,635 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:58,635 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:58,635 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,635 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,686 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:58,686 DEBUG: Start:	 Training
-2016-09-06 10:08:58,687 DEBUG: Info:	 Time for Training: 0.0547561645508[s]
-2016-09-06 10:08:58,687 DEBUG: Done:	 Training
-2016-09-06 10:08:58,687 DEBUG: Start:	 Predicting
-2016-09-06 10:08:58,692 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:58,692 DEBUG: Start:	 Training
-2016-09-06 10:08:58,713 DEBUG: Info:	 Time for Training: 0.0805191993713[s]
-2016-09-06 10:08:58,713 DEBUG: Done:	 Training
-2016-09-06 10:08:58,713 DEBUG: Done:	 Predicting
-2016-09-06 10:08:58,713 DEBUG: Start:	 Predicting
-2016-09-06 10:08:58,713 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:58,714 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:58,715 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.661904761905
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,715 INFO: Done:	 Result Analysis
-2016-09-06 10:08:58,717 DEBUG: Done:	 Predicting
-2016-09-06 10:08:58,717 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:58,718 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:58,719 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,719 INFO: Done:	 Result Analysis
-2016-09-06 10:08:58,777 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,777 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,777 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:08:58,777 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:58,777 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:08:58,777 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:58,778 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,778 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,778 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,778 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,778 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:58,778 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:58,778 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,778 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,829 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:58,829 DEBUG: Start:	 Training
-2016-09-06 10:08:58,838 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:58,838 DEBUG: Start:	 Training
-2016-09-06 10:08:58,847 DEBUG: Info:	 Time for Training: 0.0710601806641[s]
-2016-09-06 10:08:58,847 DEBUG: Done:	 Training
-2016-09-06 10:08:58,847 DEBUG: Start:	 Predicting
-2016-09-06 10:08:58,854 DEBUG: Done:	 Predicting
-2016-09-06 10:08:58,854 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:58,855 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:58,855 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,856 INFO: Done:	 Result Analysis
-2016-09-06 10:08:58,861 DEBUG: Info:	 Time for Training: 0.0853440761566[s]
-2016-09-06 10:08:58,861 DEBUG: Done:	 Training
-2016-09-06 10:08:58,862 DEBUG: Start:	 Predicting
-2016-09-06 10:08:58,866 DEBUG: Done:	 Predicting
-2016-09-06 10:08:58,866 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:58,867 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:58,867 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,867 INFO: Done:	 Result Analysis
-2016-09-06 10:08:58,923 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,923 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:58,923 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:58,924 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:58,923 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,924 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:58,924 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:58,924 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:58,924 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,924 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:58,925 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:58,925 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:58,925 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:58,925 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,960 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:58,961 DEBUG: Start:	 Training
-2016-09-06 10:08:58,963 DEBUG: Info:	 Time for Training: 0.0409200191498[s]
-2016-09-06 10:08:58,963 DEBUG: Done:	 Training
-2016-09-06 10:08:58,963 DEBUG: Start:	 Predicting
-2016-09-06 10:08:58,966 DEBUG: Done:	 Predicting
-2016-09-06 10:08:58,966 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:58,967 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:58,967 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,968 INFO: Done:	 Result Analysis
-2016-09-06 10:08:58,981 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:58,981 DEBUG: Start:	 Training
-2016-09-06 10:08:58,986 DEBUG: Info:	 Time for Training: 0.0627229213715[s]
-2016-09-06 10:08:58,986 DEBUG: Done:	 Training
-2016-09-06 10:08:58,986 DEBUG: Start:	 Predicting
-2016-09-06 10:08:58,989 DEBUG: Done:	 Predicting
-2016-09-06 10:08:58,989 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:58,992 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:58,992 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,992 INFO: Done:	 Result Analysis
-2016-09-06 10:08:59,074 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,074 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,075 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:59,075 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:59,075 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:59,075 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:59,075 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,075 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,076 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,076 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,076 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:59,076 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:59,076 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,076 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,108 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:59,108 DEBUG: Start:	 Training
-2016-09-06 10:08:59,109 DEBUG: Info:	 Time for Training: 0.0350739955902[s]
-2016-09-06 10:08:59,109 DEBUG: Done:	 Training
-2016-09-06 10:08:59,109 DEBUG: Start:	 Predicting
-2016-09-06 10:08:59,116 DEBUG: Done:	 Predicting
-2016-09-06 10:08:59,116 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:59,117 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:59,117 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 35
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,117 INFO: Done:	 Result Analysis
-2016-09-06 10:08:59,170 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:59,170 DEBUG: Start:	 Training
-2016-09-06 10:08:59,178 DEBUG: Info:	 Time for Training: 0.10413312912[s]
-2016-09-06 10:08:59,178 DEBUG: Done:	 Training
-2016-09-06 10:08:59,178 DEBUG: Start:	 Predicting
-2016-09-06 10:08:59,182 DEBUG: Done:	 Predicting
-2016-09-06 10:08:59,182 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:59,184 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:59,184 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.9
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,184 INFO: Done:	 Result Analysis
-2016-09-06 10:08:59,324 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,324 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,324 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:59,324 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:59,325 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:59,325 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:59,326 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,326 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,326 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,326 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,326 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:59,326 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:59,326 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,326 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,396 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:59,396 DEBUG: Start:	 Training
-2016-09-06 10:08:59,397 DEBUG: Info:	 Time for Training: 0.074168920517[s]
-2016-09-06 10:08:59,397 DEBUG: Done:	 Training
-2016-09-06 10:08:59,398 DEBUG: Start:	 Predicting
-2016-09-06 10:08:59,403 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:59,403 DEBUG: Start:	 Training
-2016-09-06 10:08:59,421 DEBUG: Done:	 Predicting
-2016-09-06 10:08:59,421 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:59,424 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:59,424 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,424 INFO: Done:	 Result Analysis
-2016-09-06 10:08:59,431 DEBUG: Info:	 Time for Training: 0.107607841492[s]
-2016-09-06 10:08:59,431 DEBUG: Done:	 Training
-2016-09-06 10:08:59,431 DEBUG: Start:	 Predicting
-2016-09-06 10:08:59,434 DEBUG: Done:	 Predicting
-2016-09-06 10:08:59,435 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:59,436 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:59,436 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,436 INFO: Done:	 Result Analysis
-2016-09-06 10:08:59,571 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,571 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,571 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:08:59,571 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:08:59,571 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:59,571 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:59,571 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,571 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,572 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,572 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,572 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:59,572 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:59,572 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,572 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,620 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:59,620 DEBUG: Start:	 Training
-2016-09-06 10:08:59,624 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:59,624 DEBUG: Start:	 Training
-2016-09-06 10:08:59,637 DEBUG: Info:	 Time for Training: 0.0667371749878[s]
-2016-09-06 10:08:59,637 DEBUG: Done:	 Training
-2016-09-06 10:08:59,637 DEBUG: Start:	 Predicting
-2016-09-06 10:08:59,643 DEBUG: Done:	 Predicting
-2016-09-06 10:08:59,643 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:59,643 DEBUG: Info:	 Time for Training: 0.0726878643036[s]
-2016-09-06 10:08:59,643 DEBUG: Done:	 Training
-2016-09-06 10:08:59,643 DEBUG: Start:	 Predicting
-2016-09-06 10:08:59,644 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:59,644 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,645 INFO: Done:	 Result Analysis
-2016-09-06 10:08:59,647 DEBUG: Done:	 Predicting
-2016-09-06 10:08:59,647 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:59,649 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:59,649 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,649 INFO: Done:	 Result Analysis
-2016-09-06 10:08:59,723 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:59,723 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:59,724 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,724 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:59,724 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:59,724 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:08:59,724 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:08:59,724 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:59,725 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:08:59,725 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,725 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:08:59,725 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:59,725 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,756 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:59,756 DEBUG: Start:	 Training
-2016-09-06 10:08:59,757 DEBUG: Info:	 Time for Training: 0.0341780185699[s]
-2016-09-06 10:08:59,757 DEBUG: Done:	 Training
-2016-09-06 10:08:59,757 DEBUG: Start:	 Predicting
-2016-09-06 10:08:59,760 DEBUG: Done:	 Predicting
-2016-09-06 10:08:59,760 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:59,761 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:59,761 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.344444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.344444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,762 INFO: Done:	 Result Analysis
-2016-09-06 10:08:59,771 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:59,771 DEBUG: Start:	 Training
-2016-09-06 10:08:59,774 DEBUG: Info:	 Time for Training: 0.0514109134674[s]
-2016-09-06 10:08:59,774 DEBUG: Done:	 Training
-2016-09-06 10:08:59,774 DEBUG: Start:	 Predicting
-2016-09-06 10:08:59,777 DEBUG: Done:	 Predicting
-2016-09-06 10:08:59,777 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:59,779 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:59,779 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.366666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.366666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,779 INFO: Done:	 Result Analysis
-2016-09-06 10:08:59,875 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,875 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,876 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:59,876 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:59,877 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:59,877 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:08:59,878 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:08:59,878 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:08:59,878 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:08:59,878 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:08:59,878 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:59,878 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:08:59,878 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,878 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,909 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:59,910 DEBUG: Start:	 Training
-2016-09-06 10:08:59,910 DEBUG: Info:	 Time for Training: 0.0356111526489[s]
-2016-09-06 10:08:59,910 DEBUG: Done:	 Training
-2016-09-06 10:08:59,910 DEBUG: Start:	 Predicting
-2016-09-06 10:08:59,916 DEBUG: Done:	 Predicting
-2016-09-06 10:08:59,916 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:59,918 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:59,918 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 35
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,918 INFO: Done:	 Result Analysis
-2016-09-06 10:08:59,962 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:08:59,962 DEBUG: Start:	 Training
-2016-09-06 10:08:59,971 DEBUG: Info:	 Time for Training: 0.0960199832916[s]
-2016-09-06 10:08:59,971 DEBUG: Done:	 Training
-2016-09-06 10:08:59,971 DEBUG: Start:	 Predicting
-2016-09-06 10:08:59,974 DEBUG: Done:	 Predicting
-2016-09-06 10:08:59,974 DEBUG: Start:	 Getting Results
-2016-09-06 10:08:59,975 DEBUG: Done:	 Getting Results
-2016-09-06 10:08:59,976 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.895238095238
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,976 INFO: Done:	 Result Analysis
-2016-09-06 10:09:00,126 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,126 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,127 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:09:00,127 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:09:00,127 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:09:00,127 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:09:00,128 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,128 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,128 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,128 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,128 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:09:00,128 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:09:00,128 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,128 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,198 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:09:00,198 DEBUG: Start:	 Training
-2016-09-06 10:09:00,199 DEBUG: Info:	 Time for Training: 0.073979139328[s]
-2016-09-06 10:09:00,199 DEBUG: Done:	 Training
-2016-09-06 10:09:00,199 DEBUG: Start:	 Predicting
-2016-09-06 10:09:00,201 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:09:00,201 DEBUG: Start:	 Training
-2016-09-06 10:09:00,217 DEBUG: Done:	 Predicting
-2016-09-06 10:09:00,217 DEBUG: Start:	 Getting Results
-2016-09-06 10:09:00,217 DEBUG: Info:	 Time for Training: 0.09197306633[s]
-2016-09-06 10:09:00,217 DEBUG: Done:	 Training
-2016-09-06 10:09:00,217 DEBUG: Start:	 Predicting
-2016-09-06 10:09:00,219 DEBUG: Done:	 Getting Results
-2016-09-06 10:09:00,219 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,219 INFO: Done:	 Result Analysis
-2016-09-06 10:09:00,220 DEBUG: Done:	 Predicting
-2016-09-06 10:09:00,220 DEBUG: Start:	 Getting Results
-2016-09-06 10:09:00,221 DEBUG: Done:	 Getting Results
-2016-09-06 10:09:00,222 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,222 INFO: Done:	 Result Analysis
-2016-09-06 10:09:00,375 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,375 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,376 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:09:00,376 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:09:00,376 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:09:00,376 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:09:00,377 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,377 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,377 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,377 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,378 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:09:00,378 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:09:00,378 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,378 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,452 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:09:00,452 DEBUG: Start:	 Training
-2016-09-06 10:09:00,454 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:09:00,454 DEBUG: Start:	 Training
-2016-09-06 10:09:00,472 DEBUG: Info:	 Time for Training: 0.0980281829834[s]
-2016-09-06 10:09:00,472 DEBUG: Done:	 Training
-2016-09-06 10:09:00,472 DEBUG: Start:	 Predicting
-2016-09-06 10:09:00,474 DEBUG: Info:	 Time for Training: 0.100123167038[s]
-2016-09-06 10:09:00,474 DEBUG: Done:	 Training
-2016-09-06 10:09:00,474 DEBUG: Start:	 Predicting
-2016-09-06 10:09:00,475 DEBUG: Done:	 Predicting
-2016-09-06 10:09:00,475 DEBUG: Start:	 Getting Results
-2016-09-06 10:09:00,477 DEBUG: Done:	 Getting Results
-2016-09-06 10:09:00,477 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,477 INFO: Done:	 Result Analysis
-2016-09-06 10:09:00,479 DEBUG: Done:	 Predicting
-2016-09-06 10:09:00,479 DEBUG: Start:	 Getting Results
-2016-09-06 10:09:00,481 DEBUG: Done:	 Getting Results
-2016-09-06 10:09:00,481 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,481 INFO: Done:	 Result Analysis
-2016-09-06 10:09:00,627 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,627 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,627 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:09:00,627 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:09:00,627 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:09:00,627 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:09:00,628 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,628 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,629 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,629 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,629 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:09:00,629 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:09:00,629 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,629 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,678 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:09:00,679 DEBUG: Start:	 Training
-2016-09-06 10:09:00,680 DEBUG: Info:	 Time for Training: 0.054713010788[s]
-2016-09-06 10:09:00,681 DEBUG: Done:	 Training
-2016-09-06 10:09:00,681 DEBUG: Start:	 Predicting
-2016-09-06 10:09:00,684 DEBUG: Done:	 Predicting
-2016-09-06 10:09:00,684 DEBUG: Start:	 Getting Results
-2016-09-06 10:09:00,687 DEBUG: Done:	 Getting Results
-2016-09-06 10:09:00,687 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,687 INFO: Done:	 Result Analysis
-2016-09-06 10:09:00,696 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:09:00,696 DEBUG: Start:	 Training
-2016-09-06 10:09:00,700 DEBUG: Info:	 Time for Training: 0.0740790367126[s]
-2016-09-06 10:09:00,700 DEBUG: Done:	 Training
-2016-09-06 10:09:00,700 DEBUG: Start:	 Predicting
-2016-09-06 10:09:00,703 DEBUG: Done:	 Predicting
-2016-09-06 10:09:00,703 DEBUG: Start:	 Getting Results
-2016-09-06 10:09:00,705 DEBUG: Done:	 Getting Results
-2016-09-06 10:09:00,705 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,705 INFO: Done:	 Result Analysis
-2016-09-06 10:09:00,775 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,775 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,775 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:09:00,775 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:09:00,775 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:09:00,775 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:09:00,776 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,776 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,776 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,776 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,776 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:09:00,776 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:09:00,777 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,777 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,824 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:09:00,824 DEBUG: Start:	 Training
-2016-09-06 10:09:00,825 DEBUG: Info:	 Time for Training: 0.0511150360107[s]
-2016-09-06 10:09:00,825 DEBUG: Done:	 Training
-2016-09-06 10:09:00,825 DEBUG: Start:	 Predicting
-2016-09-06 10:09:00,834 DEBUG: Done:	 Predicting
-2016-09-06 10:09:00,834 DEBUG: Start:	 Getting Results
-2016-09-06 10:09:00,836 DEBUG: Done:	 Getting Results
-2016-09-06 10:09:00,836 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 35
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,836 INFO: Done:	 Result Analysis
-2016-09-06 10:09:00,882 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:09:00,882 DEBUG: Start:	 Training
-2016-09-06 10:09:00,891 DEBUG: Info:	 Time for Training: 0.116482019424[s]
-2016-09-06 10:09:00,891 DEBUG: Done:	 Training
-2016-09-06 10:09:00,891 DEBUG: Start:	 Predicting
-2016-09-06 10:09:00,894 DEBUG: Done:	 Predicting
-2016-09-06 10:09:00,894 DEBUG: Start:	 Getting Results
-2016-09-06 10:09:00,895 DEBUG: Done:	 Getting Results
-2016-09-06 10:09:00,895 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.355555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.355555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,896 INFO: Done:	 Result Analysis
-2016-09-06 10:09:01,024 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:01,024 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:01,025 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:09:01,025 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:09:01,025 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:09:01,025 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:09:01,026 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:01,026 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:01,026 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:01,026 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:01,026 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:09:01,026 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:09:01,026 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:09:01,026 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:09:01,095 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:09:01,095 DEBUG: Start:	 Training
-2016-09-06 10:09:01,096 DEBUG: Info:	 Time for Training: 0.072772026062[s]
-2016-09-06 10:09:01,096 DEBUG: Done:	 Training
-2016-09-06 10:09:01,096 DEBUG: Start:	 Predicting
-2016-09-06 10:09:01,098 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:09:01,098 DEBUG: Start:	 Training
-2016-09-06 10:09:01,109 DEBUG: Done:	 Predicting
-2016-09-06 10:09:01,110 DEBUG: Start:	 Getting Results
-2016-09-06 10:09:01,112 DEBUG: Done:	 Getting Results
-2016-09-06 10:09:01,112 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:09:01,112 INFO: Done:	 Result Analysis
-2016-09-06 10:09:01,118 DEBUG: Info:	 Time for Training: 0.0949590206146[s]
-2016-09-06 10:09:01,119 DEBUG: Done:	 Training
-2016-09-06 10:09:01,119 DEBUG: Start:	 Predicting
-2016-09-06 10:09:01,123 DEBUG: Done:	 Predicting
-2016-09-06 10:09:01,123 DEBUG: Start:	 Getting Results
-2016-09-06 10:09:01,124 DEBUG: Done:	 Getting Results
-2016-09-06 10:09:01,125 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:09:01,125 INFO: Done:	 Result Analysis
-2016-09-06 10:09:01,423 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:09:01,424 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:09:01,425 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 10:09:01,425 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:09:01,425 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:09:01,426 INFO: Info:	 Shape of View1 :(300, 14)
-2016-09-06 10:09:01,426 INFO: Info:	 Shape of View0 :(300, 19)
-2016-09-06 10:09:01,426 INFO: Info:	 Shape of View2 :(300, 5)
-2016-09-06 10:09:01,427 INFO: Info:	 Shape of View1 :(300, 14)
-2016-09-06 10:09:01,427 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 10:09:01,427 INFO: Done:	 Read Database Files
-2016-09-06 10:09:01,428 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:09:01,428 INFO: Info:	 Shape of View2 :(300, 5)
-2016-09-06 10:09:01,429 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 10:09:01,429 INFO: Done:	 Read Database Files
-2016-09-06 10:09:01,429 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:09:01,436 INFO: Done:	 Determine validation split
-2016-09-06 10:09:01,436 INFO: Start:	 Determine 5 folds
-2016-09-06 10:09:01,436 INFO: Done:	 Determine validation split
-2016-09-06 10:09:01,437 INFO: Start:	 Determine 5 folds
-2016-09-06 10:09:01,450 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:09:01,450 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:09:01,450 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:09:01,450 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:09:01,450 INFO: Done:	 Determine folds
-2016-09-06 10:09:01,450 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:09:01,450 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:09:01,450 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:09:01,450 INFO: Start:	 Classification
-2016-09-06 10:09:01,450 INFO: Done:	 Determine folds
-2016-09-06 10:09:01,451 INFO: 	Start:	 Fold number 1
-2016-09-06 10:09:01,451 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:09:01,451 INFO: Start:	 Classification
-2016-09-06 10:09:01,451 INFO: 	Start:	 Fold number 1
-2016-09-06 10:09:01,514 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:09:01,528 DEBUG: 			View 0 : 0.433333333333
-2016-09-06 10:09:01,541 DEBUG: 			View 1 : 0.577777777778
-2016-09-06 10:09:01,554 DEBUG: 			View 2 : 0.444444444444
-2016-09-06 10:09:01,566 DEBUG: 			View 3 : 0.433333333333
-2016-09-06 10:09:01,621 DEBUG: 			 Best view : 		View0
-2016-09-06 10:09:01,751 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:09:01,765 DEBUG: 			View 0 : 0.75
-2016-09-06 10:09:01,779 DEBUG: 			View 1 : 0.622222222222
-2016-09-06 10:09:01,792 DEBUG: 			View 2 : 0.711111111111
-2016-09-06 10:09:01,805 DEBUG: 			View 3 : 0.683333333333
-2016-09-06 10:09:01,851 DEBUG: 			 Best view : 		View0
-2016-09-06 10:09:02,004 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:09:02,013 DEBUG: 			View 0 : 0.75
-2016-09-06 10:09:02,022 DEBUG: 			View 1 : 0.622222222222
-2016-09-06 10:09:02,030 DEBUG: 			View 2 : 0.711111111111
-2016-09-06 10:09:02,037 DEBUG: 			View 3 : 0.683333333333
-2016-09-06 10:09:02,077 DEBUG: 			 Best view : 		View0
-2016-09-06 10:09:02,295 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:09:02,303 DEBUG: 			View 0 : 0.738888888889
-2016-09-06 10:09:02,310 DEBUG: 			View 1 : 0.666666666667
-2016-09-06 10:09:02,317 DEBUG: 			View 2 : 0.677777777778
-2016-09-06 10:09:02,324 DEBUG: 			View 3 : 0.666666666667
-2016-09-06 10:09:02,367 DEBUG: 			 Best view : 		View0
-2016-09-06 10:09:02,653 INFO: 	Start: 	 Classification
-2016-09-06 10:09:03,124 INFO: 	Done: 	 Fold number 1
-2016-09-06 10:09:03,124 INFO: 	Start:	 Fold number 2
-2016-09-06 10:09:03,155 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:09:03,162 DEBUG: 			View 0 : 0.472826086957
-2016-09-06 10:09:03,169 DEBUG: 			View 1 : 0.559782608696
-2016-09-06 10:09:03,176 DEBUG: 			View 2 : 0.527173913043
-2016-09-06 10:09:03,183 DEBUG: 			View 3 : 0.570652173913
-2016-09-06 10:09:03,216 DEBUG: 			 Best view : 		View2
-2016-09-06 10:09:03,297 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:09:03,305 DEBUG: 			View 0 : 0.695652173913
-2016-09-06 10:09:03,313 DEBUG: 			View 1 : 0.625
-2016-09-06 10:09:03,320 DEBUG: 			View 2 : 0.657608695652
-2016-09-06 10:09:03,327 DEBUG: 			View 3 : 0.657608695652
-2016-09-06 10:09:03,366 DEBUG: 			 Best view : 		View0
-2016-09-06 10:09:03,518 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:09:03,526 DEBUG: 			View 0 : 0.695652173913
-2016-09-06 10:09:03,533 DEBUG: 			View 1 : 0.625
-2016-09-06 10:09:03,540 DEBUG: 			View 2 : 0.657608695652
-2016-09-06 10:09:03,548 DEBUG: 			View 3 : 0.657608695652
-2016-09-06 10:09:03,589 DEBUG: 			 Best view : 		View0
-2016-09-06 10:09:03,812 INFO: 	Start: 	 Classification
-2016-09-06 10:09:04,173 INFO: 	Done: 	 Fold number 2
-2016-09-06 10:09:04,173 INFO: 	Start:	 Fold number 3
-2016-09-06 10:09:04,204 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:09:04,211 DEBUG: 			View 0 : 0.513812154696
-2016-09-06 10:09:04,218 DEBUG: 			View 1 : 0.480662983425
-2016-09-06 10:09:04,225 DEBUG: 			View 2 : 0.441988950276
-2016-09-06 10:09:04,232 DEBUG: 			View 3 : 0.453038674033
-2016-09-06 10:09:04,264 DEBUG: 			 Best view : 		View0
-2016-09-06 10:09:04,345 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:09:04,353 DEBUG: 			View 0 : 0.685082872928
-2016-09-06 10:09:04,360 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 10:09:04,367 DEBUG: 			View 2 : 0.685082872928
-2016-09-06 10:09:04,374 DEBUG: 			View 3 : 0.707182320442
-2016-09-06 10:09:04,413 DEBUG: 			 Best view : 		View3
-2016-09-06 10:09:04,562 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:09:04,569 DEBUG: 			View 0 : 0.685082872928
-2016-09-06 10:09:04,577 DEBUG: 			View 1 : 0.662983425414
-2016-09-06 10:09:04,584 DEBUG: 			View 2 : 0.685082872928
-2016-09-06 10:09:04,591 DEBUG: 			View 3 : 0.707182320442
-2016-09-06 10:09:04,631 DEBUG: 			 Best view : 		View3
-2016-09-06 10:09:04,849 INFO: 	Start: 	 Classification
-2016-09-06 10:09:05,203 INFO: 	Done: 	 Fold number 3
-2016-09-06 10:09:05,203 INFO: 	Start:	 Fold number 4
-2016-09-06 10:09:05,235 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:09:05,242 DEBUG: 			View 0 : 0.491891891892
-2016-09-06 10:09:05,249 DEBUG: 			View 1 : 0.47027027027
-2016-09-06 10:09:05,256 DEBUG: 			View 2 : 0.491891891892
-2016-09-06 10:09:05,263 DEBUG: 			View 3 : 0.513513513514
-2016-09-06 10:09:05,296 DEBUG: 			 Best view : 		View1
-2016-09-06 10:09:05,378 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:09:05,385 DEBUG: 			View 0 : 0.675675675676
-2016-09-06 10:09:05,393 DEBUG: 			View 1 : 0.686486486486
-2016-09-06 10:09:05,400 DEBUG: 			View 2 : 0.681081081081
-2016-09-06 10:09:05,407 DEBUG: 			View 3 : 0.686486486486
-2016-09-06 10:09:05,447 DEBUG: 			 Best view : 		View1
-2016-09-06 10:09:05,600 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:09:05,608 DEBUG: 			View 0 : 0.675675675676
-2016-09-06 10:09:05,615 DEBUG: 			View 1 : 0.686486486486
-2016-09-06 10:09:05,622 DEBUG: 			View 2 : 0.681081081081
-2016-09-06 10:09:05,629 DEBUG: 			View 3 : 0.686486486486
-2016-09-06 10:09:05,671 DEBUG: 			 Best view : 		View1
-2016-09-06 10:09:05,894 INFO: 	Start: 	 Classification
-2016-09-06 10:09:06,257 INFO: 	Done: 	 Fold number 4
-2016-09-06 10:09:06,257 INFO: 	Start:	 Fold number 5
-2016-09-06 10:09:06,287 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:09:06,295 DEBUG: 			View 0 : 0.538888888889
-2016-09-06 10:09:06,302 DEBUG: 			View 1 : 0.461111111111
-2016-09-06 10:09:06,309 DEBUG: 			View 2 : 0.511111111111
-2016-09-06 10:09:06,316 DEBUG: 			View 3 : 0.561111111111
-2016-09-06 10:09:06,348 DEBUG: 			 Best view : 		View0
-2016-09-06 10:09:06,428 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:09:06,435 DEBUG: 			View 0 : 0.722222222222
-2016-09-06 10:09:06,443 DEBUG: 			View 1 : 0.638888888889
-2016-09-06 10:09:06,450 DEBUG: 			View 2 : 0.644444444444
-2016-09-06 10:09:06,457 DEBUG: 			View 3 : 0.611111111111
-2016-09-06 10:09:06,496 DEBUG: 			 Best view : 		View0
-2016-09-06 10:09:06,646 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:09:06,654 DEBUG: 			View 0 : 0.722222222222
-2016-09-06 10:09:06,662 DEBUG: 			View 1 : 0.638888888889
-2016-09-06 10:09:06,669 DEBUG: 			View 2 : 0.644444444444
-2016-09-06 10:09:06,676 DEBUG: 			View 3 : 0.572222222222
-2016-09-06 10:09:06,717 DEBUG: 			 Best view : 		View0
-2016-09-06 10:09:06,935 INFO: 	Start: 	 Classification
-2016-09-06 10:09:07,291 INFO: 	Done: 	 Fold number 5
-2016-09-06 10:09:07,291 INFO: Done:	 Classification
-2016-09-06 10:09:07,291 INFO: Info:	 Time for Classification: 5[s]
-2016-09-06 10:09:07,291 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5340a4ef..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 401659ee..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2b148b8a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 35
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4370daf7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0f779050..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.661904761905
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bbd4f050..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ea7ee5a0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 238e7f55..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a919948f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.366666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.366666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 53b3252b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.344444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.344444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 71631804..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 35
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ffc954f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.895238095238
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4a7d3a5e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 848051bc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f77cf58d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2282cfc9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7e19a21f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0cc72c43..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ad6d2c4f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 35
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 02ce1d84..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.355555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.355555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 013f2790..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2c1b8cde..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 52bd8c30..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 291885b0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 722f6d1c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 36d5aa61..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 830
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 17ec1f47..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1375 +0,0 @@
-2016-09-06 10:13:30,568 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:13:30,568 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000133375 Gbytes /!\ 
-2016-09-06 10:13:35,578 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:13:35,581 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:13:35,639 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:35,640 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:35,640 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:13:35,640 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:13:35,640 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:35,640 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:35,641 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:35,641 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:35,641 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:35,641 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:35,641 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:35,641 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:35,641 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:35,641 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:35,678 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:35,678 DEBUG: Start:	 Training
-2016-09-06 10:13:35,680 DEBUG: Info:	 Time for Training: 0.0411851406097[s]
-2016-09-06 10:13:35,680 DEBUG: Done:	 Training
-2016-09-06 10:13:35,680 DEBUG: Start:	 Predicting
-2016-09-06 10:13:35,683 DEBUG: Done:	 Predicting
-2016-09-06 10:13:35,683 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:35,684 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:35,684 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:13:35,685 INFO: Done:	 Result Analysis
-2016-09-06 10:13:35,692 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:35,692 DEBUG: Start:	 Training
-2016-09-06 10:13:35,696 DEBUG: Info:	 Time for Training: 0.0574531555176[s]
-2016-09-06 10:13:35,696 DEBUG: Done:	 Training
-2016-09-06 10:13:35,696 DEBUG: Start:	 Predicting
-2016-09-06 10:13:35,699 DEBUG: Done:	 Predicting
-2016-09-06 10:13:35,700 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:35,701 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:35,701 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:13:35,702 INFO: Done:	 Result Analysis
-2016-09-06 10:13:35,786 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:35,786 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:35,786 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:13:35,786 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:35,786 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:13:35,786 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:35,787 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:35,787 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:35,787 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:35,787 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:35,787 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:35,788 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:35,788 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:35,788 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:35,820 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:35,820 DEBUG: Start:	 Training
-2016-09-06 10:13:35,821 DEBUG: Info:	 Time for Training: 0.0358920097351[s]
-2016-09-06 10:13:35,821 DEBUG: Done:	 Training
-2016-09-06 10:13:35,821 DEBUG: Start:	 Predicting
-2016-09-06 10:13:35,828 DEBUG: Done:	 Predicting
-2016-09-06 10:13:35,829 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:35,831 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:35,831 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:13:35,831 INFO: Done:	 Result Analysis
-2016-09-06 10:13:35,871 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:35,871 DEBUG: Start:	 Training
-2016-09-06 10:13:35,880 DEBUG: Info:	 Time for Training: 0.094918012619[s]
-2016-09-06 10:13:35,880 DEBUG: Done:	 Training
-2016-09-06 10:13:35,880 DEBUG: Start:	 Predicting
-2016-09-06 10:13:35,883 DEBUG: Done:	 Predicting
-2016-09-06 10:13:35,883 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:35,885 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:35,885 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.9
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:13:35,885 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,032 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,032 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,032 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:13:36,032 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:13:36,033 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,033 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,033 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:36,033 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:36,033 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:36,033 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:36,033 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,033 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,034 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,034 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,079 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,079 DEBUG: Start:	 Training
-2016-09-06 10:13:36,080 DEBUG: Info:	 Time for Training: 0.0481998920441[s]
-2016-09-06 10:13:36,080 DEBUG: Done:	 Training
-2016-09-06 10:13:36,080 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,084 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,084 DEBUG: Start:	 Training
-2016-09-06 10:13:36,102 DEBUG: Info:	 Time for Training: 0.070839881897[s]
-2016-09-06 10:13:36,103 DEBUG: Done:	 Training
-2016-09-06 10:13:36,103 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,106 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,106 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,108 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,108 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,108 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,110 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,111 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,114 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,114 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,115 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,177 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,177 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,178 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:13:36,178 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:13:36,178 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,178 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,178 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:36,178 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:36,179 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:36,179 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:36,179 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,179 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,179 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,179 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,233 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,233 DEBUG: Start:	 Training
-2016-09-06 10:13:36,235 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,236 DEBUG: Start:	 Training
-2016-09-06 10:13:36,250 DEBUG: Info:	 Time for Training: 0.0732500553131[s]
-2016-09-06 10:13:36,250 DEBUG: Done:	 Training
-2016-09-06 10:13:36,250 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,254 DEBUG: Info:	 Time for Training: 0.0775668621063[s]
-2016-09-06 10:13:36,254 DEBUG: Done:	 Training
-2016-09-06 10:13:36,255 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,256 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,257 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,258 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,258 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,259 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,259 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,259 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,260 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,260 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,261 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,328 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,328 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:13:36,329 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,330 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,330 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,330 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,330 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,330 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,331 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:13:36,331 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,331 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,332 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,332 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,332 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,372 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,372 DEBUG: Start:	 Training
-2016-09-06 10:13:36,374 DEBUG: Info:	 Time for Training: 0.0452740192413[s]
-2016-09-06 10:13:36,375 DEBUG: Done:	 Training
-2016-09-06 10:13:36,375 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,378 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,378 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,380 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,380 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,381 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,383 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,383 DEBUG: Start:	 Training
-2016-09-06 10:13:36,387 DEBUG: Info:	 Time for Training: 0.0596179962158[s]
-2016-09-06 10:13:36,387 DEBUG: Done:	 Training
-2016-09-06 10:13:36,387 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,390 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,390 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,392 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,392 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,392 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,471 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,471 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,471 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:13:36,471 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:13:36,471 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,471 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,472 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,472 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,472 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,472 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,472 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,472 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,472 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,472 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,505 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,505 DEBUG: Start:	 Training
-2016-09-06 10:13:36,506 DEBUG: Info:	 Time for Training: 0.0356941223145[s]
-2016-09-06 10:13:36,506 DEBUG: Done:	 Training
-2016-09-06 10:13:36,506 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,513 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,513 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,515 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,515 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,515 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,557 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,557 DEBUG: Start:	 Training
-2016-09-06 10:13:36,565 DEBUG: Info:	 Time for Training: 0.0949969291687[s]
-2016-09-06 10:13:36,565 DEBUG: Done:	 Training
-2016-09-06 10:13:36,566 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,569 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,569 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,570 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,571 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.904761904762
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.904761904762
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,571 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,718 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,718 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,718 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:13:36,718 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:13:36,718 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,718 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,719 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,719 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,719 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,719 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,719 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,719 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,719 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,719 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,771 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,771 DEBUG: Start:	 Training
-2016-09-06 10:13:36,779 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,779 DEBUG: Start:	 Training
-2016-09-06 10:13:36,780 DEBUG: Info:	 Time for Training: 0.063157081604[s]
-2016-09-06 10:13:36,780 DEBUG: Done:	 Training
-2016-09-06 10:13:36,781 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,792 DEBUG: Info:	 Time for Training: 0.0748438835144[s]
-2016-09-06 10:13:36,792 DEBUG: Done:	 Training
-2016-09-06 10:13:36,792 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,796 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,796 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,797 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,797 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,797 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,801 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,801 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,804 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,804 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,804 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,869 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,870 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:13:36,870 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,870 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,870 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:13:36,870 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:36,871 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,871 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,871 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,871 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,872 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,872 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,872 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:36,872 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,918 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,919 DEBUG: Start:	 Training
-2016-09-06 10:13:36,930 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:36,930 DEBUG: Start:	 Training
-2016-09-06 10:13:36,935 DEBUG: Info:	 Time for Training: 0.0670058727264[s]
-2016-09-06 10:13:36,936 DEBUG: Done:	 Training
-2016-09-06 10:13:36,936 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,941 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,941 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,943 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,943 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,943 INFO: Done:	 Result Analysis
-2016-09-06 10:13:36,947 DEBUG: Info:	 Time for Training: 0.0786368846893[s]
-2016-09-06 10:13:36,947 DEBUG: Done:	 Training
-2016-09-06 10:13:36,947 DEBUG: Start:	 Predicting
-2016-09-06 10:13:36,952 DEBUG: Done:	 Predicting
-2016-09-06 10:13:36,952 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:36,954 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:36,954 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,954 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,012 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,012 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,013 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:13:37,013 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:13:37,013 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,013 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,013 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,013 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,014 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,014 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,014 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,014 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,014 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,014 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,046 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,046 DEBUG: Start:	 Training
-2016-09-06 10:13:37,048 DEBUG: Info:	 Time for Training: 0.035749912262[s]
-2016-09-06 10:13:37,048 DEBUG: Done:	 Training
-2016-09-06 10:13:37,048 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,051 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,051 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,052 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,052 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,052 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,061 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,061 DEBUG: Start:	 Training
-2016-09-06 10:13:37,064 DEBUG: Info:	 Time for Training: 0.0523209571838[s]
-2016-09-06 10:13:37,064 DEBUG: Done:	 Training
-2016-09-06 10:13:37,065 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,067 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,068 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,069 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,070 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,078 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,160 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,160 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,161 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:13:37,161 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:13:37,162 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,162 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,163 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,163 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,163 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,163 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,163 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,163 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,163 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,163 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,195 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,195 DEBUG: Start:	 Training
-2016-09-06 10:13:37,196 DEBUG: Info:	 Time for Training: 0.0357730388641[s]
-2016-09-06 10:13:37,196 DEBUG: Done:	 Training
-2016-09-06 10:13:37,196 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,202 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,202 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,204 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,204 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,204 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,251 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,252 DEBUG: Start:	 Training
-2016-09-06 10:13:37,260 DEBUG: Info:	 Time for Training: 0.0996959209442[s]
-2016-09-06 10:13:37,260 DEBUG: Done:	 Training
-2016-09-06 10:13:37,260 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,263 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,263 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,265 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,265 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.895238095238
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,265 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,416 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,416 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,417 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:13:37,417 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:13:37,417 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,417 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,417 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,417 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,418 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,418 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,418 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,418 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,418 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,418 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,462 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,463 DEBUG: Start:	 Training
-2016-09-06 10:13:37,463 DEBUG: Info:	 Time for Training: 0.0475831031799[s]
-2016-09-06 10:13:37,463 DEBUG: Done:	 Training
-2016-09-06 10:13:37,463 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,467 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,468 DEBUG: Start:	 Training
-2016-09-06 10:13:37,476 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,477 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,478 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,478 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,479 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,492 DEBUG: Info:	 Time for Training: 0.0758819580078[s]
-2016-09-06 10:13:37,492 DEBUG: Done:	 Training
-2016-09-06 10:13:37,492 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,495 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,495 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,496 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,496 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,497 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,558 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,558 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,559 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:13:37,559 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:13:37,559 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,559 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,559 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,559 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,559 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,560 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,560 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,560 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,560 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,560 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,610 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,610 DEBUG: Start:	 Training
-2016-09-06 10:13:37,614 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,614 DEBUG: Start:	 Training
-2016-09-06 10:13:37,627 DEBUG: Info:	 Time for Training: 0.0695948600769[s]
-2016-09-06 10:13:37,627 DEBUG: Done:	 Training
-2016-09-06 10:13:37,628 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,632 DEBUG: Info:	 Time for Training: 0.0741398334503[s]
-2016-09-06 10:13:37,632 DEBUG: Done:	 Training
-2016-09-06 10:13:37,632 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,633 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,633 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,634 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,634 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,634 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,635 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,635 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,637 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,637 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.628571428571
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,637 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,708 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,708 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:13:37,708 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,708 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,709 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:13:37,709 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:37,709 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,709 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:37,709 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,709 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,710 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:37,710 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:37,710 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,710 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,749 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,749 DEBUG: Start:	 Training
-2016-09-06 10:13:37,751 DEBUG: Info:	 Time for Training: 0.0440349578857[s]
-2016-09-06 10:13:37,751 DEBUG: Done:	 Training
-2016-09-06 10:13:37,752 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,754 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,754 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,755 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,756 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,756 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,768 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,768 DEBUG: Start:	 Training
-2016-09-06 10:13:37,773 DEBUG: Info:	 Time for Training: 0.0648808479309[s]
-2016-09-06 10:13:37,773 DEBUG: Done:	 Training
-2016-09-06 10:13:37,773 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,776 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,776 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,778 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,778 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,778 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,859 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,859 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,860 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:13:37,860 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:13:37,860 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,860 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:37,861 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:37,861 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:37,862 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:37,862 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:37,862 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,862 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:37,862 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,862 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,914 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,915 DEBUG: Start:	 Training
-2016-09-06 10:13:37,915 DEBUG: Info:	 Time for Training: 0.0569260120392[s]
-2016-09-06 10:13:37,915 DEBUG: Done:	 Training
-2016-09-06 10:13:37,916 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,927 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,927 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,929 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,929 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,930 INFO: Done:	 Result Analysis
-2016-09-06 10:13:37,973 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:37,974 DEBUG: Start:	 Training
-2016-09-06 10:13:37,982 DEBUG: Info:	 Time for Training: 0.123476028442[s]
-2016-09-06 10:13:37,982 DEBUG: Done:	 Training
-2016-09-06 10:13:37,982 DEBUG: Start:	 Predicting
-2016-09-06 10:13:37,985 DEBUG: Done:	 Predicting
-2016-09-06 10:13:37,985 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:37,987 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:37,987 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,987 INFO: Done:	 Result Analysis
-2016-09-06 10:13:38,103 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:38,103 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:38,104 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:13:38,104 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:13:38,104 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:38,104 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:13:38,105 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:38,105 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:38,105 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:38,105 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:38,105 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:38,105 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:13:38,105 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:38,105 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:13:38,152 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:38,152 DEBUG: Start:	 Training
-2016-09-06 10:13:38,153 DEBUG: Info:	 Time for Training: 0.0500018596649[s]
-2016-09-06 10:13:38,153 DEBUG: Done:	 Training
-2016-09-06 10:13:38,153 DEBUG: Start:	 Predicting
-2016-09-06 10:13:38,160 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:13:38,160 DEBUG: Start:	 Training
-2016-09-06 10:13:38,170 DEBUG: Done:	 Predicting
-2016-09-06 10:13:38,170 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:38,172 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:38,172 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:38,172 INFO: Done:	 Result Analysis
-2016-09-06 10:13:38,182 DEBUG: Info:	 Time for Training: 0.0787019729614[s]
-2016-09-06 10:13:38,182 DEBUG: Done:	 Training
-2016-09-06 10:13:38,182 DEBUG: Start:	 Predicting
-2016-09-06 10:13:38,185 DEBUG: Done:	 Predicting
-2016-09-06 10:13:38,186 DEBUG: Start:	 Getting Results
-2016-09-06 10:13:38,187 DEBUG: Done:	 Getting Results
-2016-09-06 10:13:38,187 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:38,187 INFO: Done:	 Result Analysis
-2016-09-06 10:13:38,404 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:13:38,404 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:13:38,405 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:13:38,405 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:13:38,405 INFO: Info:	 Shape of View0 :(300, 10)
-2016-09-06 10:13:38,406 INFO: Info:	 Shape of View0 :(300, 10)
-2016-09-06 10:13:38,406 INFO: Info:	 Shape of View1 :(300, 13)
-2016-09-06 10:13:38,407 INFO: Info:	 Shape of View1 :(300, 13)
-2016-09-06 10:13:38,407 INFO: Info:	 Shape of View2 :(300, 7)
-2016-09-06 10:13:38,407 INFO: Info:	 Shape of View2 :(300, 7)
-2016-09-06 10:13:38,407 INFO: Info:	 Shape of View3 :(300, 19)
-2016-09-06 10:13:38,407 INFO: Done:	 Read Database Files
-2016-09-06 10:13:38,408 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:13:38,408 INFO: Info:	 Shape of View3 :(300, 19)
-2016-09-06 10:13:38,408 INFO: Done:	 Read Database Files
-2016-09-06 10:13:38,408 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:13:38,412 INFO: Done:	 Determine validation split
-2016-09-06 10:13:38,412 INFO: Start:	 Determine 5 folds
-2016-09-06 10:13:38,412 INFO: Done:	 Determine validation split
-2016-09-06 10:13:38,413 INFO: Start:	 Determine 5 folds
-2016-09-06 10:13:38,422 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:13:38,422 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:13:38,422 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:13:38,422 INFO: Done:	 Determine folds
-2016-09-06 10:13:38,423 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:13:38,423 INFO: Start:	 Classification
-2016-09-06 10:13:38,423 INFO: 	Start:	 Fold number 1
-2016-09-06 10:13:38,425 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:13:38,425 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:13:38,425 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:13:38,425 INFO: Done:	 Determine folds
-2016-09-06 10:13:38,425 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:13:38,425 INFO: Start:	 Classification
-2016-09-06 10:13:38,426 INFO: 	Start:	 Fold number 1
-2016-09-06 10:13:38,463 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:13:38,473 DEBUG: 			View 0 : 0.535135135135
-2016-09-06 10:13:38,481 DEBUG: 			View 1 : 0.524324324324
-2016-09-06 10:13:38,491 DEBUG: 			View 2 : 0.437837837838
-2016-09-06 10:13:38,500 DEBUG: 			View 3 : 0.535135135135
-2016-09-06 10:13:38,541 DEBUG: 			 Best view : 		View1
-2016-09-06 10:13:38,635 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:13:38,645 DEBUG: 			View 0 : 0.756756756757
-2016-09-06 10:13:38,652 DEBUG: 			View 1 : 0.735135135135
-2016-09-06 10:13:38,662 DEBUG: 			View 2 : 0.697297297297
-2016-09-06 10:13:38,669 DEBUG: 			View 3 : 0.697297297297
-2016-09-06 10:13:38,719 DEBUG: 			 Best view : 		View0
-2016-09-06 10:13:38,877 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:13:38,885 DEBUG: 			View 0 : 0.756756756757
-2016-09-06 10:13:38,892 DEBUG: 			View 1 : 0.735135135135
-2016-09-06 10:13:38,899 DEBUG: 			View 2 : 0.697297297297
-2016-09-06 10:13:38,907 DEBUG: 			View 3 : 0.697297297297
-2016-09-06 10:13:38,949 DEBUG: 			 Best view : 		View0
-2016-09-06 10:13:39,172 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:13:39,179 DEBUG: 			View 0 : 0.702702702703
-2016-09-06 10:13:39,187 DEBUG: 			View 1 : 0.67027027027
-2016-09-06 10:13:39,194 DEBUG: 			View 2 : 0.708108108108
-2016-09-06 10:13:39,202 DEBUG: 			View 3 : 0.67027027027
-2016-09-06 10:13:39,246 DEBUG: 			 Best view : 		View0
-2016-09-06 10:13:39,538 INFO: 	Start: 	 Classification
-2016-09-06 10:13:40,019 INFO: 	Done: 	 Fold number 1
-2016-09-06 10:13:40,019 INFO: 	Start:	 Fold number 2
-2016-09-06 10:13:40,049 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:13:40,056 DEBUG: 			View 0 : 0.522222222222
-2016-09-06 10:13:40,063 DEBUG: 			View 1 : 0.516666666667
-2016-09-06 10:13:40,069 DEBUG: 			View 2 : 0.477777777778
-2016-09-06 10:13:40,077 DEBUG: 			View 3 : 0.516666666667
-2016-09-06 10:13:40,109 DEBUG: 			 Best view : 		View0
-2016-09-06 10:13:40,187 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:13:40,195 DEBUG: 			View 0 : 0.622222222222
-2016-09-06 10:13:40,202 DEBUG: 			View 1 : 0.672222222222
-2016-09-06 10:13:40,209 DEBUG: 			View 2 : 0.677777777778
-2016-09-06 10:13:40,217 DEBUG: 			View 3 : 0.672222222222
-2016-09-06 10:13:40,256 DEBUG: 			 Best view : 		View2
-2016-09-06 10:13:40,404 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:13:40,411 DEBUG: 			View 0 : 0.622222222222
-2016-09-06 10:13:40,419 DEBUG: 			View 1 : 0.672222222222
-2016-09-06 10:13:40,426 DEBUG: 			View 2 : 0.677777777778
-2016-09-06 10:13:40,433 DEBUG: 			View 3 : 0.672222222222
-2016-09-06 10:13:40,475 DEBUG: 			 Best view : 		View2
-2016-09-06 10:13:40,691 INFO: 	Start: 	 Classification
-2016-09-06 10:13:41,043 INFO: 	Done: 	 Fold number 2
-2016-09-06 10:13:41,043 INFO: 	Start:	 Fold number 3
-2016-09-06 10:13:41,073 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:13:41,079 DEBUG: 			View 0 : 0.544444444444
-2016-09-06 10:13:41,087 DEBUG: 			View 1 : 0.522222222222
-2016-09-06 10:13:41,093 DEBUG: 			View 2 : 0.466666666667
-2016-09-06 10:13:41,101 DEBUG: 			View 3 : 0.511111111111
-2016-09-06 10:13:41,133 DEBUG: 			 Best view : 		View1
-2016-09-06 10:13:41,212 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:13:41,220 DEBUG: 			View 0 : 0.688888888889
-2016-09-06 10:13:41,227 DEBUG: 			View 1 : 0.627777777778
-2016-09-06 10:13:41,234 DEBUG: 			View 2 : 0.716666666667
-2016-09-06 10:13:41,242 DEBUG: 			View 3 : 0.733333333333
-2016-09-06 10:13:41,280 DEBUG: 			 Best view : 		View3
-2016-09-06 10:13:41,428 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:13:41,435 DEBUG: 			View 0 : 0.688888888889
-2016-09-06 10:13:41,443 DEBUG: 			View 1 : 0.627777777778
-2016-09-06 10:13:41,450 DEBUG: 			View 2 : 0.716666666667
-2016-09-06 10:13:41,457 DEBUG: 			View 3 : 0.733333333333
-2016-09-06 10:13:41,498 DEBUG: 			 Best view : 		View3
-2016-09-06 10:13:41,716 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:13:41,723 DEBUG: 			View 0 : 0.666666666667
-2016-09-06 10:13:41,730 DEBUG: 			View 1 : 0.611111111111
-2016-09-06 10:13:41,737 DEBUG: 			View 2 : 0.705555555556
-2016-09-06 10:13:41,745 DEBUG: 			View 3 : 0.688888888889
-2016-09-06 10:13:41,789 DEBUG: 			 Best view : 		View2
-2016-09-06 10:13:42,070 INFO: 	Start: 	 Classification
-2016-09-06 10:13:42,538 INFO: 	Done: 	 Fold number 3
-2016-09-06 10:13:42,538 INFO: 	Start:	 Fold number 4
-2016-09-06 10:13:42,568 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:13:42,575 DEBUG: 			View 0 : 0.527472527473
-2016-09-06 10:13:42,582 DEBUG: 			View 1 : 0.582417582418
-2016-09-06 10:13:42,588 DEBUG: 			View 2 : 0.516483516484
-2016-09-06 10:13:42,596 DEBUG: 			View 3 : 0.516483516484
-2016-09-06 10:13:42,628 DEBUG: 			 Best view : 		View0
-2016-09-06 10:13:42,708 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:13:42,716 DEBUG: 			View 0 : 0.697802197802
-2016-09-06 10:13:42,723 DEBUG: 			View 1 : 0.675824175824
-2016-09-06 10:13:42,730 DEBUG: 			View 2 : 0.675824175824
-2016-09-06 10:13:42,738 DEBUG: 			View 3 : 0.686813186813
-2016-09-06 10:13:42,776 DEBUG: 			 Best view : 		View0
-2016-09-06 10:13:42,926 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:13:42,933 DEBUG: 			View 0 : 0.697802197802
-2016-09-06 10:13:42,940 DEBUG: 			View 1 : 0.675824175824
-2016-09-06 10:13:42,947 DEBUG: 			View 2 : 0.675824175824
-2016-09-06 10:13:42,955 DEBUG: 			View 3 : 0.686813186813
-2016-09-06 10:13:42,996 DEBUG: 			 Best view : 		View0
-2016-09-06 10:13:43,215 INFO: 	Start: 	 Classification
-2016-09-06 10:13:43,569 INFO: 	Done: 	 Fold number 4
-2016-09-06 10:13:43,570 INFO: 	Start:	 Fold number 5
-2016-09-06 10:13:43,600 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:13:43,607 DEBUG: 			View 0 : 0.497297297297
-2016-09-06 10:13:43,615 DEBUG: 			View 1 : 0.486486486486
-2016-09-06 10:13:43,621 DEBUG: 			View 2 : 0.491891891892
-2016-09-06 10:13:43,629 DEBUG: 			View 3 : 0.502702702703
-2016-09-06 10:13:43,662 DEBUG: 			 Best view : 		View3
-2016-09-06 10:13:43,743 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:13:43,751 DEBUG: 			View 0 : 0.702702702703
-2016-09-06 10:13:43,758 DEBUG: 			View 1 : 0.702702702703
-2016-09-06 10:13:43,765 DEBUG: 			View 2 : 0.605405405405
-2016-09-06 10:13:43,773 DEBUG: 			View 3 : 0.718918918919
-2016-09-06 10:13:43,812 DEBUG: 			 Best view : 		View3
-2016-09-06 10:13:43,965 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:13:43,972 DEBUG: 			View 0 : 0.702702702703
-2016-09-06 10:13:43,980 DEBUG: 			View 1 : 0.702702702703
-2016-09-06 10:13:43,987 DEBUG: 			View 2 : 0.605405405405
-2016-09-06 10:13:43,995 DEBUG: 			View 3 : 0.718918918919
-2016-09-06 10:13:44,037 DEBUG: 			 Best view : 		View3
-2016-09-06 10:13:44,260 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:13:44,268 DEBUG: 			View 0 : 0.724324324324
-2016-09-06 10:13:44,275 DEBUG: 			View 1 : 0.627027027027
-2016-09-06 10:13:44,283 DEBUG: 			View 2 : 0.681081081081
-2016-09-06 10:13:44,290 DEBUG: 			View 3 : 0.648648648649
-2016-09-06 10:13:44,335 DEBUG: 			 Best view : 		View0
-2016-09-06 10:13:44,628 INFO: 	Start: 	 Classification
-2016-09-06 10:13:45,105 INFO: 	Done: 	 Fold number 5
-2016-09-06 10:13:45,106 INFO: Done:	 Classification
-2016-09-06 10:13:45,106 INFO: Info:	 Time for Classification: 6[s]
-2016-09-06 10:13:45,106 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d5944341..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 41319f9a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ffd9cc63..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 61f7d864..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.9
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5e68fb88..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 73356fd5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1a9bd291..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7ece7302..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.904761904762
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.904761904762
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 23e4c20c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a6ee9073..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 736ada89..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 749dc0af..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 05b31d53..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0e7c951d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 58caffdd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 48
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fffde9a8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 3, max_depth : 16
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 35e2c637..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fc1d9bb0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3b5c3c54..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.628571428571
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 785b3511..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5be94406..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7668376b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 9584
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 5867a8bf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1381 +0,0 @@
-2016-09-06 10:23:43,264 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:23:43,264 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 9.821875e-05 Gbytes /!\ 
-2016-09-06 10:23:48,275 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:23:48,277 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:23:48,332 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:48,332 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:48,332 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:23:48,332 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:23:48,332 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:48,332 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:48,333 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:48,333 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:48,333 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:48,333 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:48,333 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:48,333 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:48,333 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:48,333 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:48,366 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:48,367 DEBUG: Start:	 Training
-2016-09-06 10:23:48,368 DEBUG: Info:	 Time for Training: 0.0366399288177[s]
-2016-09-06 10:23:48,368 DEBUG: Done:	 Training
-2016-09-06 10:23:48,368 DEBUG: Start:	 Predicting
-2016-09-06 10:23:48,371 DEBUG: Done:	 Predicting
-2016-09-06 10:23:48,371 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:48,372 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:48,372 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.919047619048
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:23:48,372 INFO: Done:	 Result Analysis
-2016-09-06 10:23:48,382 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:48,382 DEBUG: Start:	 Training
-2016-09-06 10:23:48,385 DEBUG: Info:	 Time for Training: 0.0539109706879[s]
-2016-09-06 10:23:48,385 DEBUG: Done:	 Training
-2016-09-06 10:23:48,385 DEBUG: Start:	 Predicting
-2016-09-06 10:23:48,388 DEBUG: Done:	 Predicting
-2016-09-06 10:23:48,388 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:48,390 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:48,390 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:48,390 INFO: Done:	 Result Analysis
-2016-09-06 10:23:48,477 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:48,477 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:48,477 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:23:48,477 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:23:48,477 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:48,477 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:48,478 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:48,478 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:48,478 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:48,478 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:48,478 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:48,478 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:48,478 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:48,478 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:48,509 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:48,509 DEBUG: Start:	 Training
-2016-09-06 10:23:48,510 DEBUG: Info:	 Time for Training: 0.0335669517517[s]
-2016-09-06 10:23:48,510 DEBUG: Done:	 Training
-2016-09-06 10:23:48,510 DEBUG: Start:	 Predicting
-2016-09-06 10:23:48,514 DEBUG: Done:	 Predicting
-2016-09-06 10:23:48,514 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:48,516 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:48,516 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.67619047619
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:23:48,516 INFO: Done:	 Result Analysis
-2016-09-06 10:23:48,820 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:48,820 DEBUG: Start:	 Training
-2016-09-06 10:23:48,870 DEBUG: Info:	 Time for Training: 0.393655061722[s]
-2016-09-06 10:23:48,870 DEBUG: Done:	 Training
-2016-09-06 10:23:48,870 DEBUG: Start:	 Predicting
-2016-09-06 10:23:48,876 DEBUG: Done:	 Predicting
-2016-09-06 10:23:48,876 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:48,878 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:48,878 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 20, max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:48,878 INFO: Done:	 Result Analysis
-2016-09-06 10:23:49,036 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,036 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:23:49,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:23:49,037 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:49,037 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:49,038 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:49,038 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:49,038 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:49,038 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:49,038 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:49,038 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:49,038 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,038 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,100 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:49,100 DEBUG: Start:	 Training
-2016-09-06 10:23:49,122 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:49,122 DEBUG: Start:	 Training
-2016-09-06 10:23:49,123 DEBUG: Info:	 Time for Training: 0.0881431102753[s]
-2016-09-06 10:23:49,124 DEBUG: Done:	 Training
-2016-09-06 10:23:49,124 DEBUG: Start:	 Predicting
-2016-09-06 10:23:49,125 DEBUG: Info:	 Time for Training: 0.0898480415344[s]
-2016-09-06 10:23:49,125 DEBUG: Done:	 Training
-2016-09-06 10:23:49,125 DEBUG: Start:	 Predicting
-2016-09-06 10:23:49,129 DEBUG: Done:	 Predicting
-2016-09-06 10:23:49,129 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:49,130 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:49,130 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.480952380952
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.480952380952
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,130 INFO: Done:	 Result Analysis
-2016-09-06 10:23:49,138 DEBUG: Done:	 Predicting
-2016-09-06 10:23:49,138 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:49,140 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:49,140 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,140 INFO: Done:	 Result Analysis
-2016-09-06 10:23:49,278 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,279 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:23:49,279 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:49,280 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:49,280 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,280 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:49,280 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:23:49,280 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:49,280 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,280 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:49,281 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:49,281 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:49,282 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:49,282 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,329 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:49,329 DEBUG: Start:	 Training
-2016-09-06 10:23:49,340 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:49,340 DEBUG: Start:	 Training
-2016-09-06 10:23:49,345 DEBUG: Info:	 Time for Training: 0.0676798820496[s]
-2016-09-06 10:23:49,346 DEBUG: Done:	 Training
-2016-09-06 10:23:49,346 DEBUG: Start:	 Predicting
-2016-09-06 10:23:49,351 DEBUG: Done:	 Predicting
-2016-09-06 10:23:49,351 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:49,352 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:49,353 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,353 INFO: Done:	 Result Analysis
-2016-09-06 10:23:49,358 DEBUG: Info:	 Time for Training: 0.0790169239044[s]
-2016-09-06 10:23:49,358 DEBUG: Done:	 Training
-2016-09-06 10:23:49,358 DEBUG: Start:	 Predicting
-2016-09-06 10:23:49,361 DEBUG: Done:	 Predicting
-2016-09-06 10:23:49,361 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:49,363 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:49,364 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.752380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.752380952381
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,364 INFO: Done:	 Result Analysis
-2016-09-06 10:23:49,427 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,427 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,427 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:23:49,427 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:23:49,427 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:49,427 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:49,428 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:49,428 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:49,428 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:49,428 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:49,428 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:49,428 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:49,428 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,428 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,470 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:49,470 DEBUG: Start:	 Training
-2016-09-06 10:23:49,472 DEBUG: Info:	 Time for Training: 0.0461599826813[s]
-2016-09-06 10:23:49,472 DEBUG: Done:	 Training
-2016-09-06 10:23:49,473 DEBUG: Start:	 Predicting
-2016-09-06 10:23:49,475 DEBUG: Done:	 Predicting
-2016-09-06 10:23:49,475 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:49,476 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:49,477 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.780952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.780952380952
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,477 INFO: Done:	 Result Analysis
-2016-09-06 10:23:49,479 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:49,479 DEBUG: Start:	 Training
-2016-09-06 10:23:49,483 DEBUG: Info:	 Time for Training: 0.057247877121[s]
-2016-09-06 10:23:49,484 DEBUG: Done:	 Training
-2016-09-06 10:23:49,484 DEBUG: Start:	 Predicting
-2016-09-06 10:23:49,487 DEBUG: Done:	 Predicting
-2016-09-06 10:23:49,487 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:49,489 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:49,489 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,489 INFO: Done:	 Result Analysis
-2016-09-06 10:23:49,575 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,575 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,575 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:23:49,575 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:23:49,575 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:49,575 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:49,576 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:49,576 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:49,576 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:49,576 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:49,576 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:49,576 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:49,576 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,576 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,606 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:49,607 DEBUG: Start:	 Training
-2016-09-06 10:23:49,607 DEBUG: Info:	 Time for Training: 0.0329051017761[s]
-2016-09-06 10:23:49,607 DEBUG: Done:	 Training
-2016-09-06 10:23:49,607 DEBUG: Start:	 Predicting
-2016-09-06 10:23:49,612 DEBUG: Done:	 Predicting
-2016-09-06 10:23:49,613 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:49,614 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:49,614 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.657142857143
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.657142857143
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,614 INFO: Done:	 Result Analysis
-2016-09-06 10:23:49,922 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:49,922 DEBUG: Start:	 Training
-2016-09-06 10:23:49,974 DEBUG: Info:	 Time for Training: 0.399342060089[s]
-2016-09-06 10:23:49,974 DEBUG: Done:	 Training
-2016-09-06 10:23:49,974 DEBUG: Start:	 Predicting
-2016-09-06 10:23:49,981 DEBUG: Done:	 Predicting
-2016-09-06 10:23:49,981 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:49,982 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:49,982 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 20, max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,982 INFO: Done:	 Result Analysis
-2016-09-06 10:23:50,127 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,127 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,128 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:23:50,128 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:23:50,128 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:50,128 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:50,129 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:50,129 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:50,129 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:50,129 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:50,129 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:50,129 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:50,129 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,129 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,173 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:50,173 DEBUG: Start:	 Training
-2016-09-06 10:23:50,174 DEBUG: Info:	 Time for Training: 0.0477719306946[s]
-2016-09-06 10:23:50,174 DEBUG: Done:	 Training
-2016-09-06 10:23:50,174 DEBUG: Start:	 Predicting
-2016-09-06 10:23:50,179 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:50,179 DEBUG: Start:	 Training
-2016-09-06 10:23:50,187 DEBUG: Done:	 Predicting
-2016-09-06 10:23:50,188 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:50,190 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:50,190 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,190 INFO: Done:	 Result Analysis
-2016-09-06 10:23:50,203 DEBUG: Info:	 Time for Training: 0.0768611431122[s]
-2016-09-06 10:23:50,204 DEBUG: Done:	 Training
-2016-09-06 10:23:50,204 DEBUG: Start:	 Predicting
-2016-09-06 10:23:50,207 DEBUG: Done:	 Predicting
-2016-09-06 10:23:50,207 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:50,208 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:50,208 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,209 INFO: Done:	 Result Analysis
-2016-09-06 10:23:50,271 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,271 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:23:50,271 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,271 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:50,272 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:23:50,272 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:50,272 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:50,273 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:50,273 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:50,273 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,273 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:50,273 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:50,274 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:50,274 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,323 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:50,323 DEBUG: Start:	 Training
-2016-09-06 10:23:50,325 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:50,325 DEBUG: Start:	 Training
-2016-09-06 10:23:50,341 DEBUG: Info:	 Time for Training: 0.0708639621735[s]
-2016-09-06 10:23:50,341 DEBUG: Done:	 Training
-2016-09-06 10:23:50,341 DEBUG: Start:	 Predicting
-2016-09-06 10:23:50,344 DEBUG: Info:	 Time for Training: 0.0738418102264[s]
-2016-09-06 10:23:50,344 DEBUG: Done:	 Training
-2016-09-06 10:23:50,344 DEBUG: Start:	 Predicting
-2016-09-06 10:23:50,347 DEBUG: Done:	 Predicting
-2016-09-06 10:23:50,347 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:50,347 DEBUG: Done:	 Predicting
-2016-09-06 10:23:50,348 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:50,348 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:50,348 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,348 INFO: Done:	 Result Analysis
-2016-09-06 10:23:50,349 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:50,349 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,349 INFO: Done:	 Result Analysis
-2016-09-06 10:23:50,420 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,420 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,421 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:23:50,421 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:23:50,421 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:50,421 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:50,422 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:50,422 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:50,422 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:50,422 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:50,423 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:50,423 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:50,423 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,423 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,474 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:50,474 DEBUG: Start:	 Training
-2016-09-06 10:23:50,476 DEBUG: Info:	 Time for Training: 0.0563578605652[s]
-2016-09-06 10:23:50,476 DEBUG: Done:	 Training
-2016-09-06 10:23:50,476 DEBUG: Start:	 Predicting
-2016-09-06 10:23:50,480 DEBUG: Done:	 Predicting
-2016-09-06 10:23:50,480 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:50,482 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:50,482 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.904761904762
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.904761904762
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,483 INFO: Done:	 Result Analysis
-2016-09-06 10:23:50,493 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:50,493 DEBUG: Start:	 Training
-2016-09-06 10:23:50,496 DEBUG: Info:	 Time for Training: 0.0766098499298[s]
-2016-09-06 10:23:50,496 DEBUG: Done:	 Training
-2016-09-06 10:23:50,496 DEBUG: Start:	 Predicting
-2016-09-06 10:23:50,499 DEBUG: Done:	 Predicting
-2016-09-06 10:23:50,499 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:50,501 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:50,501 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,501 INFO: Done:	 Result Analysis
-2016-09-06 10:23:50,564 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,564 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:23:50,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:23:50,564 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:50,565 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:50,565 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:50,565 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:50,565 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:50,565 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:50,565 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:50,565 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,565 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:50,566 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,596 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:50,596 DEBUG: Start:	 Training
-2016-09-06 10:23:50,597 DEBUG: Info:	 Time for Training: 0.0331511497498[s]
-2016-09-06 10:23:50,597 DEBUG: Done:	 Training
-2016-09-06 10:23:50,597 DEBUG: Start:	 Predicting
-2016-09-06 10:23:50,602 DEBUG: Done:	 Predicting
-2016-09-06 10:23:50,602 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:50,603 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:50,603 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,603 INFO: Done:	 Result Analysis
-2016-09-06 10:23:50,908 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:50,908 DEBUG: Start:	 Training
-2016-09-06 10:23:50,959 DEBUG: Info:	 Time for Training: 0.395528078079[s]
-2016-09-06 10:23:50,959 DEBUG: Done:	 Training
-2016-09-06 10:23:50,959 DEBUG: Start:	 Predicting
-2016-09-06 10:23:50,966 DEBUG: Done:	 Predicting
-2016-09-06 10:23:50,966 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:50,967 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:50,968 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 20, max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,968 INFO: Done:	 Result Analysis
-2016-09-06 10:23:51,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,113 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:23:51,113 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:23:51,113 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:51,113 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:51,113 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:51,113 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:51,113 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:51,113 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:51,113 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:51,113 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:51,114 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,114 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,158 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:51,159 DEBUG: Start:	 Training
-2016-09-06 10:23:51,159 DEBUG: Info:	 Time for Training: 0.0475599765778[s]
-2016-09-06 10:23:51,160 DEBUG: Done:	 Training
-2016-09-06 10:23:51,160 DEBUG: Start:	 Predicting
-2016-09-06 10:23:51,163 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:51,163 DEBUG: Start:	 Training
-2016-09-06 10:23:51,180 DEBUG: Info:	 Time for Training: 0.0679569244385[s]
-2016-09-06 10:23:51,180 DEBUG: Done:	 Training
-2016-09-06 10:23:51,180 DEBUG: Start:	 Predicting
-2016-09-06 10:23:51,184 DEBUG: Done:	 Predicting
-2016-09-06 10:23:51,184 DEBUG: Done:	 Predicting
-2016-09-06 10:23:51,184 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:51,184 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:51,186 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:51,187 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,187 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:51,187 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,187 INFO: Done:	 Result Analysis
-2016-09-06 10:23:51,187 INFO: Done:	 Result Analysis
-2016-09-06 10:23:51,265 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,265 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,265 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:23:51,265 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:23:51,265 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:51,265 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:51,266 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:51,266 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:51,266 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:51,266 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:51,266 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:51,266 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:51,266 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,266 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,315 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:51,315 DEBUG: Start:	 Training
-2016-09-06 10:23:51,321 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:51,321 DEBUG: Start:	 Training
-2016-09-06 10:23:51,333 DEBUG: Info:	 Time for Training: 0.0680320262909[s]
-2016-09-06 10:23:51,333 DEBUG: Done:	 Training
-2016-09-06 10:23:51,333 DEBUG: Start:	 Predicting
-2016-09-06 10:23:51,338 DEBUG: Done:	 Predicting
-2016-09-06 10:23:51,338 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:51,339 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:51,340 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,340 INFO: Done:	 Result Analysis
-2016-09-06 10:23:51,340 DEBUG: Info:	 Time for Training: 0.075443983078[s]
-2016-09-06 10:23:51,340 DEBUG: Done:	 Training
-2016-09-06 10:23:51,340 DEBUG: Start:	 Predicting
-2016-09-06 10:23:51,343 DEBUG: Done:	 Predicting
-2016-09-06 10:23:51,343 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:51,345 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:51,345 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,345 INFO: Done:	 Result Analysis
-2016-09-06 10:23:51,420 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,420 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,420 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:23:51,420 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:23:51,420 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:51,420 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:51,421 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:51,421 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:51,421 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:51,421 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:51,421 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:51,422 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,422 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:51,422 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,462 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:51,462 DEBUG: Start:	 Training
-2016-09-06 10:23:51,463 DEBUG: Info:	 Time for Training: 0.0440928936005[s]
-2016-09-06 10:23:51,463 DEBUG: Done:	 Training
-2016-09-06 10:23:51,463 DEBUG: Start:	 Predicting
-2016-09-06 10:23:51,466 DEBUG: Done:	 Predicting
-2016-09-06 10:23:51,466 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:51,467 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:51,467 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.880952380952
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.880952380952
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,467 INFO: Done:	 Result Analysis
-2016-09-06 10:23:51,470 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:51,470 DEBUG: Start:	 Training
-2016-09-06 10:23:51,473 DEBUG: Info:	 Time for Training: 0.0543620586395[s]
-2016-09-06 10:23:51,473 DEBUG: Done:	 Training
-2016-09-06 10:23:51,473 DEBUG: Start:	 Predicting
-2016-09-06 10:23:51,476 DEBUG: Done:	 Predicting
-2016-09-06 10:23:51,476 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:51,478 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:51,479 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,479 INFO: Done:	 Result Analysis
-2016-09-06 10:23:51,574 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,574 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,574 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:23:51,574 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:23:51,574 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:51,574 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:51,575 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:51,575 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:51,575 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:51,575 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:51,576 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:51,576 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:51,576 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,576 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,623 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:51,623 DEBUG: Start:	 Training
-2016-09-06 10:23:51,624 DEBUG: Info:	 Time for Training: 0.0511050224304[s]
-2016-09-06 10:23:51,624 DEBUG: Done:	 Training
-2016-09-06 10:23:51,624 DEBUG: Start:	 Predicting
-2016-09-06 10:23:51,631 DEBUG: Done:	 Predicting
-2016-09-06 10:23:51,631 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:51,633 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:51,633 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.695238095238
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.695238095238
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,634 INFO: Done:	 Result Analysis
-2016-09-06 10:23:51,937 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:51,937 DEBUG: Start:	 Training
-2016-09-06 10:23:51,987 DEBUG: Info:	 Time for Training: 0.414555072784[s]
-2016-09-06 10:23:51,988 DEBUG: Done:	 Training
-2016-09-06 10:23:51,988 DEBUG: Start:	 Predicting
-2016-09-06 10:23:51,994 DEBUG: Done:	 Predicting
-2016-09-06 10:23:51,994 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:51,995 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:51,996 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 20, max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.952380952381
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,996 INFO: Done:	 Result Analysis
-2016-09-06 10:23:52,119 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:52,119 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:52,120 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:23:52,120 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:23:52,120 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:52,120 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:23:52,120 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:52,120 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:52,121 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:52,121 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:52,121 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:52,121 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:23:52,121 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:52,121 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:23:52,167 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:52,167 DEBUG: Start:	 Training
-2016-09-06 10:23:52,167 DEBUG: Info:	 Time for Training: 0.0486381053925[s]
-2016-09-06 10:23:52,168 DEBUG: Done:	 Training
-2016-09-06 10:23:52,168 DEBUG: Start:	 Predicting
-2016-09-06 10:23:52,170 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:23:52,170 DEBUG: Start:	 Training
-2016-09-06 10:23:52,186 DEBUG: Done:	 Predicting
-2016-09-06 10:23:52,186 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:52,187 DEBUG: Info:	 Time for Training: 0.0685579776764[s]
-2016-09-06 10:23:52,188 DEBUG: Done:	 Training
-2016-09-06 10:23:52,188 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:52,188 DEBUG: Start:	 Predicting
-2016-09-06 10:23:52,188 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:23:52,188 INFO: Done:	 Result Analysis
-2016-09-06 10:23:52,191 DEBUG: Done:	 Predicting
-2016-09-06 10:23:52,191 DEBUG: Start:	 Getting Results
-2016-09-06 10:23:52,192 DEBUG: Done:	 Getting Results
-2016-09-06 10:23:52,192 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:52,193 INFO: Done:	 Result Analysis
-2016-09-06 10:23:52,415 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:23:52,415 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:23:52,416 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 10:23:52,417 INFO: Info:	 Shape of View1 :(300, 13)
-2016-09-06 10:23:52,417 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:23:52,417 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:23:52,417 INFO: Info:	 Shape of View2 :(300, 7)
-2016-09-06 10:23:52,418 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 10:23:52,418 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 10:23:52,419 INFO: Done:	 Read Database Files
-2016-09-06 10:23:52,419 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:23:52,419 INFO: Info:	 Shape of View1 :(300, 13)
-2016-09-06 10:23:52,420 INFO: Info:	 Shape of View2 :(300, 7)
-2016-09-06 10:23:52,421 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 10:23:52,421 INFO: Done:	 Read Database Files
-2016-09-06 10:23:52,421 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:23:52,426 INFO: Done:	 Determine validation split
-2016-09-06 10:23:52,426 INFO: Start:	 Determine 5 folds
-2016-09-06 10:23:52,427 INFO: Done:	 Determine validation split
-2016-09-06 10:23:52,427 INFO: Start:	 Determine 5 folds
-2016-09-06 10:23:52,435 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:23:52,435 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:23:52,435 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:23:52,435 INFO: Done:	 Determine folds
-2016-09-06 10:23:52,435 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:23:52,435 INFO: Start:	 Classification
-2016-09-06 10:23:52,435 INFO: 	Start:	 Fold number 1
-2016-09-06 10:23:52,436 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:23:52,436 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:23:52,436 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:23:52,436 INFO: Done:	 Determine folds
-2016-09-06 10:23:52,436 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:23:52,436 INFO: Start:	 Classification
-2016-09-06 10:23:52,437 INFO: 	Start:	 Fold number 1
-2016-09-06 10:23:52,457 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:23:52,463 DEBUG: 			View 0 : 0.516666666667
-2016-09-06 10:23:52,469 DEBUG: 			View 1 : 0.475
-2016-09-06 10:23:52,475 DEBUG: 			View 2 : 0.525
-2016-09-06 10:23:52,480 DEBUG: 			View 3 : 0.525
-2016-09-06 10:23:52,503 DEBUG: 			 Best view : 		View2
-2016-09-06 10:23:52,559 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:23:52,565 DEBUG: 			View 0 : 0.733333333333
-2016-09-06 10:23:52,572 DEBUG: 			View 1 : 0.741666666667
-2016-09-06 10:23:52,577 DEBUG: 			View 2 : 0.741666666667
-2016-09-06 10:23:52,583 DEBUG: 			View 3 : 0.658333333333
-2016-09-06 10:23:52,610 DEBUG: 			 Best view : 		View1
-2016-09-06 10:23:52,727 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:23:52,737 DEBUG: 			View 0 : 0.733333333333
-2016-09-06 10:23:52,748 DEBUG: 			View 1 : 0.741666666667
-2016-09-06 10:23:52,758 DEBUG: 			View 2 : 0.741666666667
-2016-09-06 10:23:52,768 DEBUG: 			View 3 : 0.658333333333
-2016-09-06 10:23:52,803 DEBUG: 			 Best view : 		View1
-2016-09-06 10:23:52,947 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:23:52,953 DEBUG: 			View 0 : 0.675
-2016-09-06 10:23:52,958 DEBUG: 			View 1 : 0.725
-2016-09-06 10:23:52,964 DEBUG: 			View 2 : 0.691666666667
-2016-09-06 10:23:52,969 DEBUG: 			View 3 : 0.641666666667
-2016-09-06 10:23:52,998 DEBUG: 			 Best view : 		View1
-2016-09-06 10:23:53,188 INFO: 	Start: 	 Classification
-2016-09-06 10:23:53,569 INFO: 	Done: 	 Fold number 1
-2016-09-06 10:23:53,569 INFO: 	Start:	 Fold number 2
-2016-09-06 10:23:53,589 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:23:53,594 DEBUG: 			View 0 : 0.603448275862
-2016-09-06 10:23:53,600 DEBUG: 			View 1 : 0.568965517241
-2016-09-06 10:23:53,605 DEBUG: 			View 2 : 0.413793103448
-2016-09-06 10:23:53,609 DEBUG: 			View 3 : 0.48275862069
-2016-09-06 10:23:53,631 DEBUG: 			 Best view : 		View1
-2016-09-06 10:23:53,682 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:23:53,687 DEBUG: 			View 0 : 0.741379310345
-2016-09-06 10:23:53,693 DEBUG: 			View 1 : 0.724137931034
-2016-09-06 10:23:53,698 DEBUG: 			View 2 : 0.741379310345
-2016-09-06 10:23:53,703 DEBUG: 			View 3 : 0.741379310345
-2016-09-06 10:23:53,728 DEBUG: 			 Best view : 		View0
-2016-09-06 10:23:53,823 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:23:53,828 DEBUG: 			View 0 : 0.741379310345
-2016-09-06 10:23:53,833 DEBUG: 			View 1 : 0.724137931034
-2016-09-06 10:23:53,839 DEBUG: 			View 2 : 0.741379310345
-2016-09-06 10:23:53,844 DEBUG: 			View 3 : 0.741379310345
-2016-09-06 10:23:53,870 DEBUG: 			 Best view : 		View0
-2016-09-06 10:23:54,011 INFO: 	Start: 	 Classification
-2016-09-06 10:23:54,292 INFO: 	Done: 	 Fold number 2
-2016-09-06 10:23:54,292 INFO: 	Start:	 Fold number 3
-2016-09-06 10:23:54,313 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:23:54,319 DEBUG: 			View 0 : 0.471074380165
-2016-09-06 10:23:54,324 DEBUG: 			View 1 : 0.504132231405
-2016-09-06 10:23:54,329 DEBUG: 			View 2 : 0.504132231405
-2016-09-06 10:23:54,334 DEBUG: 			View 3 : 0.528925619835
-2016-09-06 10:23:54,356 DEBUG: 			 Best view : 		View0
-2016-09-06 10:23:54,410 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:23:54,415 DEBUG: 			View 0 : 0.685950413223
-2016-09-06 10:23:54,421 DEBUG: 			View 1 : 0.619834710744
-2016-09-06 10:23:54,426 DEBUG: 			View 2 : 0.685950413223
-2016-09-06 10:23:54,431 DEBUG: 			View 3 : 0.760330578512
-2016-09-06 10:23:54,457 DEBUG: 			 Best view : 		View3
-2016-09-06 10:23:54,556 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:23:54,562 DEBUG: 			View 0 : 0.685950413223
-2016-09-06 10:23:54,567 DEBUG: 			View 1 : 0.619834710744
-2016-09-06 10:23:54,573 DEBUG: 			View 2 : 0.702479338843
-2016-09-06 10:23:54,578 DEBUG: 			View 3 : 0.760330578512
-2016-09-06 10:23:54,605 DEBUG: 			 Best view : 		View3
-2016-09-06 10:23:54,750 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:23:54,756 DEBUG: 			View 0 : 0.702479338843
-2016-09-06 10:23:54,761 DEBUG: 			View 1 : 0.669421487603
-2016-09-06 10:23:54,766 DEBUG: 			View 2 : 0.735537190083
-2016-09-06 10:23:54,772 DEBUG: 			View 3 : 0.669421487603
-2016-09-06 10:23:54,801 DEBUG: 			 Best view : 		View2
-2016-09-06 10:23:54,990 INFO: 	Start: 	 Classification
-2016-09-06 10:23:55,369 INFO: 	Done: 	 Fold number 3
-2016-09-06 10:23:55,369 INFO: 	Start:	 Fold number 4
-2016-09-06 10:23:55,389 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:23:55,394 DEBUG: 			View 0 : 0.44347826087
-2016-09-06 10:23:55,399 DEBUG: 			View 1 : 0.547826086957
-2016-09-06 10:23:55,404 DEBUG: 			View 2 : 0.565217391304
-2016-09-06 10:23:55,409 DEBUG: 			View 3 : 0.582608695652
-2016-09-06 10:23:55,430 DEBUG: 			 Best view : 		View0
-2016-09-06 10:23:55,481 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:23:55,487 DEBUG: 			View 0 : 0.713043478261
-2016-09-06 10:23:55,492 DEBUG: 			View 1 : 0.75652173913
-2016-09-06 10:23:55,497 DEBUG: 			View 2 : 0.747826086957
-2016-09-06 10:23:55,502 DEBUG: 			View 3 : 0.704347826087
-2016-09-06 10:23:55,527 DEBUG: 			 Best view : 		View1
-2016-09-06 10:23:55,624 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:23:55,629 DEBUG: 			View 0 : 0.713043478261
-2016-09-06 10:23:55,634 DEBUG: 			View 1 : 0.75652173913
-2016-09-06 10:23:55,639 DEBUG: 			View 2 : 0.747826086957
-2016-09-06 10:23:55,644 DEBUG: 			View 3 : 0.704347826087
-2016-09-06 10:23:55,671 DEBUG: 			 Best view : 		View1
-2016-09-06 10:23:55,811 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:23:55,817 DEBUG: 			View 0 : 0.730434782609
-2016-09-06 10:23:55,822 DEBUG: 			View 1 : 0.704347826087
-2016-09-06 10:23:55,827 DEBUG: 			View 2 : 0.747826086957
-2016-09-06 10:23:55,832 DEBUG: 			View 3 : 0.704347826087
-2016-09-06 10:23:55,860 DEBUG: 			 Best view : 		View0
-2016-09-06 10:23:56,042 INFO: 	Start: 	 Classification
-2016-09-06 10:23:56,413 INFO: 	Done: 	 Fold number 4
-2016-09-06 10:23:56,413 INFO: 	Start:	 Fold number 5
-2016-09-06 10:23:56,433 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:23:56,438 DEBUG: 			View 0 : 0.529914529915
-2016-09-06 10:23:56,444 DEBUG: 			View 1 : 0.581196581197
-2016-09-06 10:23:56,449 DEBUG: 			View 2 : 0.512820512821
-2016-09-06 10:23:56,454 DEBUG: 			View 3 : 0.470085470085
-2016-09-06 10:23:56,475 DEBUG: 			 Best view : 		View1
-2016-09-06 10:23:56,527 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:23:56,533 DEBUG: 			View 0 : 0.717948717949
-2016-09-06 10:23:56,538 DEBUG: 			View 1 : 0.752136752137
-2016-09-06 10:23:56,543 DEBUG: 			View 2 : 0.735042735043
-2016-09-06 10:23:56,548 DEBUG: 			View 3 : 0.769230769231
-2016-09-06 10:23:56,573 DEBUG: 			 Best view : 		View3
-2016-09-06 10:23:56,669 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:23:56,675 DEBUG: 			View 0 : 0.717948717949
-2016-09-06 10:23:56,680 DEBUG: 			View 1 : 0.752136752137
-2016-09-06 10:23:56,686 DEBUG: 			View 2 : 0.735042735043
-2016-09-06 10:23:56,691 DEBUG: 			View 3 : 0.769230769231
-2016-09-06 10:23:56,718 DEBUG: 			 Best view : 		View3
-2016-09-06 10:23:56,859 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:23:56,864 DEBUG: 			View 0 : 0.683760683761
-2016-09-06 10:23:56,869 DEBUG: 			View 1 : 0.709401709402
-2016-09-06 10:23:56,875 DEBUG: 			View 2 : 0.683760683761
-2016-09-06 10:23:56,879 DEBUG: 			View 3 : 0.735042735043
-2016-09-06 10:23:56,908 DEBUG: 			 Best view : 		View0
-2016-09-06 10:23:57,092 INFO: 	Start: 	 Classification
-2016-09-06 10:23:57,464 INFO: 	Done: 	 Fold number 5
-2016-09-06 10:23:57,464 INFO: Done:	 Classification
-2016-09-06 10:23:57,464 INFO: Info:	 Time for Classification: 5[s]
-2016-09-06 10:23:57,464 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2b8d6b9f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7eddcdd0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.919047619048
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a458111b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.67619047619
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 64b9eb5d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 20, max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3cfc7072..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1253f669..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.780952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.780952380952
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8df47bba..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.657142857143
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.657142857143
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 87a46146..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 20, max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 87eb9ae8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 464f736b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.480952380952
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.480952380952
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 353cb868..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.752380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.752380952381
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e442b3e4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f728e303..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 50877a2d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.904761904762
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.904761904762
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b10d7427..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 49d16072..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 20, max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 19aa2ae1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ff73da41..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c05ffbde..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a611a167..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f3a2e850..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d2adb74a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.880952380952
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.880952380952
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f4bdfa1d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.695238095238
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.695238095238
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 463c74e7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 20, max_depth : 7
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.952380952381
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d2530167..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6564b15f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ffe6f80e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dbdf7554..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 624e0007..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 56a9a0dc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3508
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index aa39260a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1369 +0,0 @@
-2016-09-06 10:24:40,231 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:24:40,231 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00012634375 Gbytes /!\ 
-2016-09-06 10:24:45,246 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:24:45,248 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:24:45,303 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:45,303 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:45,303 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:24:45,303 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:24:45,303 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:45,303 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:45,304 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:45,304 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:45,305 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:45,305 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:45,305 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:45,305 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:45,305 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:45,305 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:45,348 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:45,348 DEBUG: Start:	 Training
-2016-09-06 10:24:45,350 DEBUG: Info:	 Time for Training: 0.0480980873108[s]
-2016-09-06 10:24:45,350 DEBUG: Done:	 Training
-2016-09-06 10:24:45,350 DEBUG: Start:	 Predicting
-2016-09-06 10:24:45,352 DEBUG: Done:	 Predicting
-2016-09-06 10:24:45,353 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:45,354 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:45,354 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:24:45,354 INFO: Done:	 Result Analysis
-2016-09-06 10:24:45,362 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:45,362 DEBUG: Start:	 Training
-2016-09-06 10:24:45,366 DEBUG: Info:	 Time for Training: 0.0647637844086[s]
-2016-09-06 10:24:45,366 DEBUG: Done:	 Training
-2016-09-06 10:24:45,366 DEBUG: Start:	 Predicting
-2016-09-06 10:24:45,369 DEBUG: Done:	 Predicting
-2016-09-06 10:24:45,369 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:45,371 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:45,371 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:24:45,372 INFO: Done:	 Result Analysis
-2016-09-06 10:24:45,442 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:45,442 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:24:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:24:45,443 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:45,443 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:45,444 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:45,444 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:45,444 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:45,444 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:45,444 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:45,444 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:45,444 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:45,444 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:45,476 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:45,476 DEBUG: Start:	 Training
-2016-09-06 10:24:45,476 DEBUG: Info:	 Time for Training: 0.0347051620483[s]
-2016-09-06 10:24:45,476 DEBUG: Done:	 Training
-2016-09-06 10:24:45,476 DEBUG: Start:	 Predicting
-2016-09-06 10:24:45,482 DEBUG: Done:	 Predicting
-2016-09-06 10:24:45,482 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:45,484 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:45,484 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.642857142857
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:24:45,484 INFO: Done:	 Result Analysis
-2016-09-06 10:24:45,890 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:45,890 DEBUG: Start:	 Training
-2016-09-06 10:24:45,958 DEBUG: Info:	 Time for Training: 0.516037940979[s]
-2016-09-06 10:24:45,958 DEBUG: Done:	 Training
-2016-09-06 10:24:45,958 DEBUG: Start:	 Predicting
-2016-09-06 10:24:45,965 DEBUG: Done:	 Predicting
-2016-09-06 10:24:45,966 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:45,967 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:45,967 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:24:45,967 INFO: Done:	 Result Analysis
-2016-09-06 10:24:46,099 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,099 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,099 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:24:46,099 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:24:46,099 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:46,099 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:46,100 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:46,100 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:46,100 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:46,100 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:46,100 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:46,100 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:46,100 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,100 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,144 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:46,144 DEBUG: Start:	 Training
-2016-09-06 10:24:46,144 DEBUG: Info:	 Time for Training: 0.0459389686584[s]
-2016-09-06 10:24:46,144 DEBUG: Done:	 Training
-2016-09-06 10:24:46,145 DEBUG: Start:	 Predicting
-2016-09-06 10:24:46,149 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:46,149 DEBUG: Start:	 Training
-2016-09-06 10:24:46,162 DEBUG: Done:	 Predicting
-2016-09-06 10:24:46,162 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:46,164 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:46,164 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,164 INFO: Done:	 Result Analysis
-2016-09-06 10:24:46,168 DEBUG: Info:	 Time for Training: 0.0690729618073[s]
-2016-09-06 10:24:46,168 DEBUG: Done:	 Training
-2016-09-06 10:24:46,168 DEBUG: Start:	 Predicting
-2016-09-06 10:24:46,171 DEBUG: Done:	 Predicting
-2016-09-06 10:24:46,171 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:46,172 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:46,172 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,173 INFO: Done:	 Result Analysis
-2016-09-06 10:24:46,250 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,250 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,251 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:24:46,251 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:24:46,251 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:46,251 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:46,252 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:46,252 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:46,252 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:46,252 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:46,253 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:46,253 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:46,253 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,253 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,325 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:46,325 DEBUG: Start:	 Training
-2016-09-06 10:24:46,329 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:46,330 DEBUG: Start:	 Training
-2016-09-06 10:24:46,348 DEBUG: Info:	 Time for Training: 0.0991640090942[s]
-2016-09-06 10:24:46,349 DEBUG: Done:	 Training
-2016-09-06 10:24:46,349 DEBUG: Start:	 Predicting
-2016-09-06 10:24:46,354 DEBUG: Info:	 Time for Training: 0.104753017426[s]
-2016-09-06 10:24:46,354 DEBUG: Done:	 Training
-2016-09-06 10:24:46,354 DEBUG: Start:	 Predicting
-2016-09-06 10:24:46,356 DEBUG: Done:	 Predicting
-2016-09-06 10:24:46,357 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:46,359 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:46,359 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,359 DEBUG: Done:	 Predicting
-2016-09-06 10:24:46,359 INFO: Done:	 Result Analysis
-2016-09-06 10:24:46,359 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:46,361 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:46,361 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,361 INFO: Done:	 Result Analysis
-2016-09-06 10:24:46,499 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,499 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,500 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:24:46,500 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:24:46,500 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:46,500 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:46,500 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:46,500 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:46,501 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:46,501 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:46,501 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:46,501 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:46,501 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,501 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,540 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:46,541 DEBUG: Start:	 Training
-2016-09-06 10:24:46,543 DEBUG: Info:	 Time for Training: 0.0447499752045[s]
-2016-09-06 10:24:46,543 DEBUG: Done:	 Training
-2016-09-06 10:24:46,544 DEBUG: Start:	 Predicting
-2016-09-06 10:24:46,546 DEBUG: Done:	 Predicting
-2016-09-06 10:24:46,546 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:46,548 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:46,548 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,548 INFO: Done:	 Result Analysis
-2016-09-06 10:24:46,555 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:46,555 DEBUG: Start:	 Training
-2016-09-06 10:24:46,560 DEBUG: Info:	 Time for Training: 0.0609669685364[s]
-2016-09-06 10:24:46,560 DEBUG: Done:	 Training
-2016-09-06 10:24:46,560 DEBUG: Start:	 Predicting
-2016-09-06 10:24:46,563 DEBUG: Done:	 Predicting
-2016-09-06 10:24:46,563 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:46,565 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:46,565 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,565 INFO: Done:	 Result Analysis
-2016-09-06 10:24:46,647 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,647 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,647 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:24:46,647 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:24:46,647 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:46,647 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:46,648 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:46,648 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:46,648 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:46,648 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:46,649 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:46,649 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:46,649 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,649 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,681 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:46,681 DEBUG: Start:	 Training
-2016-09-06 10:24:46,681 DEBUG: Info:	 Time for Training: 0.0352909564972[s]
-2016-09-06 10:24:46,681 DEBUG: Done:	 Training
-2016-09-06 10:24:46,682 DEBUG: Start:	 Predicting
-2016-09-06 10:24:46,688 DEBUG: Done:	 Predicting
-2016-09-06 10:24:46,688 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:46,689 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:46,689 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,690 INFO: Done:	 Result Analysis
-2016-09-06 10:24:47,105 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:47,106 DEBUG: Start:	 Training
-2016-09-06 10:24:47,176 DEBUG: Info:	 Time for Training: 0.529899120331[s]
-2016-09-06 10:24:47,176 DEBUG: Done:	 Training
-2016-09-06 10:24:47,176 DEBUG: Start:	 Predicting
-2016-09-06 10:24:47,184 DEBUG: Done:	 Predicting
-2016-09-06 10:24:47,184 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:47,185 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:47,185 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,186 INFO: Done:	 Result Analysis
-2016-09-06 10:24:47,297 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,297 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,297 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:24:47,297 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:24:47,297 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:47,297 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:47,298 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:47,298 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:47,298 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:47,298 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:47,299 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:47,299 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:47,299 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,299 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,369 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:47,369 DEBUG: Start:	 Training
-2016-09-06 10:24:47,370 DEBUG: Info:	 Time for Training: 0.0735681056976[s]
-2016-09-06 10:24:47,370 DEBUG: Done:	 Training
-2016-09-06 10:24:47,370 DEBUG: Start:	 Predicting
-2016-09-06 10:24:47,376 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:47,376 DEBUG: Start:	 Training
-2016-09-06 10:24:47,387 DEBUG: Done:	 Predicting
-2016-09-06 10:24:47,388 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:47,390 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:47,390 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.377777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,390 INFO: Done:	 Result Analysis
-2016-09-06 10:24:47,402 DEBUG: Info:	 Time for Training: 0.106291055679[s]
-2016-09-06 10:24:47,403 DEBUG: Done:	 Training
-2016-09-06 10:24:47,403 DEBUG: Start:	 Predicting
-2016-09-06 10:24:47,406 DEBUG: Done:	 Predicting
-2016-09-06 10:24:47,406 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:47,408 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:47,408 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,408 INFO: Done:	 Result Analysis
-2016-09-06 10:24:47,547 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,547 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,547 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:24:47,547 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:24:47,548 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:47,548 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:47,549 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:47,549 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:47,549 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:47,549 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:47,549 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:47,549 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:47,549 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,549 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,623 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:47,623 DEBUG: Start:	 Training
-2016-09-06 10:24:47,631 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:47,631 DEBUG: Start:	 Training
-2016-09-06 10:24:47,648 DEBUG: Info:	 Time for Training: 0.102329015732[s]
-2016-09-06 10:24:47,648 DEBUG: Done:	 Training
-2016-09-06 10:24:47,649 DEBUG: Start:	 Predicting
-2016-09-06 10:24:47,657 DEBUG: Done:	 Predicting
-2016-09-06 10:24:47,657 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:47,658 DEBUG: Info:	 Time for Training: 0.112071990967[s]
-2016-09-06 10:24:47,658 DEBUG: Done:	 Training
-2016-09-06 10:24:47,658 DEBUG: Start:	 Predicting
-2016-09-06 10:24:47,659 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:47,659 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,660 INFO: Done:	 Result Analysis
-2016-09-06 10:24:47,663 DEBUG: Done:	 Predicting
-2016-09-06 10:24:47,663 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:47,664 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:47,665 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,665 INFO: Done:	 Result Analysis
-2016-09-06 10:24:47,798 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,798 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,798 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:24:47,798 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:24:47,799 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:47,799 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:47,799 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:47,799 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:47,800 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:47,800 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:47,800 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:47,800 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:47,800 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,800 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,837 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:47,837 DEBUG: Start:	 Training
-2016-09-06 10:24:47,839 DEBUG: Info:	 Time for Training: 0.0416760444641[s]
-2016-09-06 10:24:47,839 DEBUG: Done:	 Training
-2016-09-06 10:24:47,839 DEBUG: Start:	 Predicting
-2016-09-06 10:24:47,842 DEBUG: Done:	 Predicting
-2016-09-06 10:24:47,842 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:47,843 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:47,843 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,844 INFO: Done:	 Result Analysis
-2016-09-06 10:24:47,852 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:47,852 DEBUG: Start:	 Training
-2016-09-06 10:24:47,856 DEBUG: Info:	 Time for Training: 0.0591020584106[s]
-2016-09-06 10:24:47,856 DEBUG: Done:	 Training
-2016-09-06 10:24:47,857 DEBUG: Start:	 Predicting
-2016-09-06 10:24:47,860 DEBUG: Done:	 Predicting
-2016-09-06 10:24:47,860 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:47,862 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:47,862 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,862 INFO: Done:	 Result Analysis
-2016-09-06 10:24:47,951 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,951 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,952 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:24:47,952 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:24:47,952 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:47,952 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:47,953 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:47,953 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:47,953 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:47,953 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:47,953 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:47,953 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:47,953 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,953 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,985 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:47,985 DEBUG: Start:	 Training
-2016-09-06 10:24:47,986 DEBUG: Info:	 Time for Training: 0.0345377922058[s]
-2016-09-06 10:24:47,986 DEBUG: Done:	 Training
-2016-09-06 10:24:47,986 DEBUG: Start:	 Predicting
-2016-09-06 10:24:47,992 DEBUG: Done:	 Predicting
-2016-09-06 10:24:47,992 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:47,994 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:47,994 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,994 INFO: Done:	 Result Analysis
-2016-09-06 10:24:48,411 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:48,411 DEBUG: Start:	 Training
-2016-09-06 10:24:48,481 DEBUG: Info:	 Time for Training: 0.529783964157[s]
-2016-09-06 10:24:48,481 DEBUG: Done:	 Training
-2016-09-06 10:24:48,481 DEBUG: Start:	 Predicting
-2016-09-06 10:24:48,489 DEBUG: Done:	 Predicting
-2016-09-06 10:24:48,489 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:48,490 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:48,490 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:24:48,491 INFO: Done:	 Result Analysis
-2016-09-06 10:24:48,607 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:48,607 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:48,608 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:24:48,608 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:24:48,608 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:48,608 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:48,609 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:48,609 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:48,609 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:48,609 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:48,609 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:48,609 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:48,609 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:48,609 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:48,677 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:48,677 DEBUG: Start:	 Training
-2016-09-06 10:24:48,678 DEBUG: Info:	 Time for Training: 0.0721070766449[s]
-2016-09-06 10:24:48,679 DEBUG: Done:	 Training
-2016-09-06 10:24:48,679 DEBUG: Start:	 Predicting
-2016-09-06 10:24:48,684 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:48,684 DEBUG: Start:	 Training
-2016-09-06 10:24:48,693 DEBUG: Done:	 Predicting
-2016-09-06 10:24:48,693 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:48,694 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:48,694 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.628571428571
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:24:48,695 INFO: Done:	 Result Analysis
-2016-09-06 10:24:48,703 DEBUG: Info:	 Time for Training: 0.0963509082794[s]
-2016-09-06 10:24:48,703 DEBUG: Done:	 Training
-2016-09-06 10:24:48,703 DEBUG: Start:	 Predicting
-2016-09-06 10:24:48,706 DEBUG: Done:	 Predicting
-2016-09-06 10:24:48,707 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:48,708 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:48,708 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.42380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.42380952381
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:48,708 INFO: Done:	 Result Analysis
-2016-09-06 10:24:48,851 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:48,851 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:48,851 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:24:48,851 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:24:48,852 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:48,852 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:48,852 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:48,852 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:48,852 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:48,852 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:48,853 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:48,853 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:48,853 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:48,853 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:48,902 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:48,902 DEBUG: Start:	 Training
-2016-09-06 10:24:48,903 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:48,903 DEBUG: Start:	 Training
-2016-09-06 10:24:48,918 DEBUG: Info:	 Time for Training: 0.066841840744[s]
-2016-09-06 10:24:48,918 DEBUG: Done:	 Training
-2016-09-06 10:24:48,918 DEBUG: Start:	 Predicting
-2016-09-06 10:24:48,921 DEBUG: Done:	 Predicting
-2016-09-06 10:24:48,922 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:48,922 DEBUG: Info:	 Time for Training: 0.0710518360138[s]
-2016-09-06 10:24:48,922 DEBUG: Done:	 Training
-2016-09-06 10:24:48,922 DEBUG: Start:	 Predicting
-2016-09-06 10:24:48,923 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:48,923 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:24:48,923 INFO: Done:	 Result Analysis
-2016-09-06 10:24:48,928 DEBUG: Done:	 Predicting
-2016-09-06 10:24:48,928 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:48,929 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:48,929 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:48,929 INFO: Done:	 Result Analysis
-2016-09-06 10:24:48,999 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,000 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:24:49,000 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:49,000 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,000 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:24:49,001 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:49,001 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,001 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,001 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,001 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:49,002 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,002 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,002 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:49,002 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,040 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:49,040 DEBUG: Start:	 Training
-2016-09-06 10:24:49,042 DEBUG: Info:	 Time for Training: 0.0424060821533[s]
-2016-09-06 10:24:49,042 DEBUG: Done:	 Training
-2016-09-06 10:24:49,042 DEBUG: Start:	 Predicting
-2016-09-06 10:24:49,045 DEBUG: Done:	 Predicting
-2016-09-06 10:24:49,046 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:49,047 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:49,047 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,048 INFO: Done:	 Result Analysis
-2016-09-06 10:24:49,051 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:49,051 DEBUG: Start:	 Training
-2016-09-06 10:24:49,054 DEBUG: Info:	 Time for Training: 0.0557050704956[s]
-2016-09-06 10:24:49,055 DEBUG: Done:	 Training
-2016-09-06 10:24:49,055 DEBUG: Start:	 Predicting
-2016-09-06 10:24:49,057 DEBUG: Done:	 Predicting
-2016-09-06 10:24:49,058 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:49,060 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:49,060 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,060 INFO: Done:	 Result Analysis
-2016-09-06 10:24:49,150 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,151 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:24:49,151 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:49,151 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,152 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,152 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:49,152 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,152 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,152 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:24:49,152 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:49,153 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,153 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,154 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:49,154 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,185 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:49,185 DEBUG: Start:	 Training
-2016-09-06 10:24:49,185 DEBUG: Info:	 Time for Training: 0.0358011722565[s]
-2016-09-06 10:24:49,185 DEBUG: Done:	 Training
-2016-09-06 10:24:49,186 DEBUG: Start:	 Predicting
-2016-09-06 10:24:49,192 DEBUG: Done:	 Predicting
-2016-09-06 10:24:49,192 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:49,193 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:49,193 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,194 INFO: Done:	 Result Analysis
-2016-09-06 10:24:49,647 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:49,647 DEBUG: Start:	 Training
-2016-09-06 10:24:49,717 DEBUG: Info:	 Time for Training: 0.565485954285[s]
-2016-09-06 10:24:49,717 DEBUG: Done:	 Training
-2016-09-06 10:24:49,717 DEBUG: Start:	 Predicting
-2016-09-06 10:24:49,725 DEBUG: Done:	 Predicting
-2016-09-06 10:24:49,725 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:49,726 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:49,726 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,727 INFO: Done:	 Result Analysis
-2016-09-06 10:24:49,800 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:24:49,801 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,801 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:49,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:24:49,801 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:24:49,802 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,802 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,802 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,802 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,802 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:49,802 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,802 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:24:49,802 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,847 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:49,847 DEBUG: Start:	 Training
-2016-09-06 10:24:49,848 DEBUG: Info:	 Time for Training: 0.0476620197296[s]
-2016-09-06 10:24:49,848 DEBUG: Done:	 Training
-2016-09-06 10:24:49,848 DEBUG: Start:	 Predicting
-2016-09-06 10:24:49,854 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:24:49,854 DEBUG: Start:	 Training
-2016-09-06 10:24:49,869 DEBUG: Done:	 Predicting
-2016-09-06 10:24:49,870 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:49,872 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:49,872 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,872 INFO: Done:	 Result Analysis
-2016-09-06 10:24:49,879 DEBUG: Info:	 Time for Training: 0.0796000957489[s]
-2016-09-06 10:24:49,879 DEBUG: Done:	 Training
-2016-09-06 10:24:49,879 DEBUG: Start:	 Predicting
-2016-09-06 10:24:49,883 DEBUG: Done:	 Predicting
-2016-09-06 10:24:49,883 DEBUG: Start:	 Getting Results
-2016-09-06 10:24:49,884 DEBUG: Done:	 Getting Results
-2016-09-06 10:24:49,884 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,884 INFO: Done:	 Result Analysis
-2016-09-06 10:24:50,099 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:24:50,100 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:24:50,100 INFO: Info:	 Shape of View0 :(300, 9)
-2016-09-06 10:24:50,101 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 10:24:50,101 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:24:50,101 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 10:24:50,101 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:24:50,102 INFO: Info:	 Shape of View3 :(300, 10)
-2016-09-06 10:24:50,102 INFO: Info:	 Shape of View0 :(300, 9)
-2016-09-06 10:24:50,102 INFO: Done:	 Read Database Files
-2016-09-06 10:24:50,102 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:24:50,102 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 10:24:50,103 INFO: Info:	 Shape of View2 :(300, 11)
-2016-09-06 10:24:50,104 INFO: Info:	 Shape of View3 :(300, 10)
-2016-09-06 10:24:50,104 INFO: Done:	 Read Database Files
-2016-09-06 10:24:50,104 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:24:50,106 INFO: Done:	 Determine validation split
-2016-09-06 10:24:50,106 INFO: Start:	 Determine 5 folds
-2016-09-06 10:24:50,108 INFO: Done:	 Determine validation split
-2016-09-06 10:24:50,108 INFO: Start:	 Determine 5 folds
-2016-09-06 10:24:50,116 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:24:50,116 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:24:50,116 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:24:50,117 INFO: Done:	 Determine folds
-2016-09-06 10:24:50,117 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:24:50,117 INFO: Start:	 Classification
-2016-09-06 10:24:50,117 INFO: 	Start:	 Fold number 1
-2016-09-06 10:24:50,118 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:24:50,118 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:24:50,118 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:24:50,118 INFO: Done:	 Determine folds
-2016-09-06 10:24:50,119 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:24:50,119 INFO: Start:	 Classification
-2016-09-06 10:24:50,119 INFO: 	Start:	 Fold number 1
-2016-09-06 10:24:50,149 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:24:50,158 DEBUG: 			View 0 : 0.479289940828
-2016-09-06 10:24:50,167 DEBUG: 			View 1 : 0.526627218935
-2016-09-06 10:24:50,175 DEBUG: 			View 2 : 0.538461538462
-2016-09-06 10:24:50,184 DEBUG: 			View 3 : 0.532544378698
-2016-09-06 10:24:50,217 DEBUG: 			 Best view : 		View2
-2016-09-06 10:24:50,296 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:24:50,304 DEBUG: 			View 0 : 0.704142011834
-2016-09-06 10:24:50,312 DEBUG: 			View 1 : 0.644970414201
-2016-09-06 10:24:50,320 DEBUG: 			View 2 : 0.668639053254
-2016-09-06 10:24:50,328 DEBUG: 			View 3 : 0.739644970414
-2016-09-06 10:24:50,366 DEBUG: 			 Best view : 		View3
-2016-09-06 10:24:50,510 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:24:50,521 DEBUG: 			View 0 : 0.704142011834
-2016-09-06 10:24:50,529 DEBUG: 			View 1 : 0.644970414201
-2016-09-06 10:24:50,537 DEBUG: 			View 2 : 0.668639053254
-2016-09-06 10:24:50,544 DEBUG: 			View 3 : 0.739644970414
-2016-09-06 10:24:50,582 DEBUG: 			 Best view : 		View3
-2016-09-06 10:24:50,783 INFO: 	Start: 	 Classification
-2016-09-06 10:24:51,128 INFO: 	Done: 	 Fold number 1
-2016-09-06 10:24:51,128 INFO: 	Start:	 Fold number 2
-2016-09-06 10:24:51,157 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:24:51,164 DEBUG: 			View 0 : 0.502958579882
-2016-09-06 10:24:51,172 DEBUG: 			View 1 : 0.526627218935
-2016-09-06 10:24:51,179 DEBUG: 			View 2 : 0.479289940828
-2016-09-06 10:24:51,186 DEBUG: 			View 3 : 0.562130177515
-2016-09-06 10:24:51,216 DEBUG: 			 Best view : 		View0
-2016-09-06 10:24:51,291 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:24:51,298 DEBUG: 			View 0 : 0.656804733728
-2016-09-06 10:24:51,306 DEBUG: 			View 1 : 0.710059171598
-2016-09-06 10:24:51,313 DEBUG: 			View 2 : 0.745562130178
-2016-09-06 10:24:51,321 DEBUG: 			View 3 : 0.757396449704
-2016-09-06 10:24:51,357 DEBUG: 			 Best view : 		View3
-2016-09-06 10:24:51,497 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:24:51,505 DEBUG: 			View 0 : 0.656804733728
-2016-09-06 10:24:51,512 DEBUG: 			View 1 : 0.710059171598
-2016-09-06 10:24:51,520 DEBUG: 			View 2 : 0.745562130178
-2016-09-06 10:24:51,527 DEBUG: 			View 3 : 0.757396449704
-2016-09-06 10:24:51,566 DEBUG: 			 Best view : 		View3
-2016-09-06 10:24:51,768 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:24:51,776 DEBUG: 			View 0 : 0.639053254438
-2016-09-06 10:24:51,783 DEBUG: 			View 1 : 0.627218934911
-2016-09-06 10:24:51,791 DEBUG: 			View 2 : 0.721893491124
-2016-09-06 10:24:51,798 DEBUG: 			View 3 : 0.692307692308
-2016-09-06 10:24:51,839 DEBUG: 			 Best view : 		View2
-2016-09-06 10:24:52,104 INFO: 	Start: 	 Classification
-2016-09-06 10:24:52,553 INFO: 	Done: 	 Fold number 2
-2016-09-06 10:24:52,553 INFO: 	Start:	 Fold number 3
-2016-09-06 10:24:52,582 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:24:52,589 DEBUG: 			View 0 : 0.467455621302
-2016-09-06 10:24:52,596 DEBUG: 			View 1 : 0.550295857988
-2016-09-06 10:24:52,603 DEBUG: 			View 2 : 0.520710059172
-2016-09-06 10:24:52,609 DEBUG: 			View 3 : 0.479289940828
-2016-09-06 10:24:52,640 DEBUG: 			 Best view : 		View0
-2016-09-06 10:24:52,714 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:24:52,722 DEBUG: 			View 0 : 0.721893491124
-2016-09-06 10:24:52,729 DEBUG: 			View 1 : 0.674556213018
-2016-09-06 10:24:52,737 DEBUG: 			View 2 : 0.656804733728
-2016-09-06 10:24:52,744 DEBUG: 			View 3 : 0.674556213018
-2016-09-06 10:24:52,780 DEBUG: 			 Best view : 		View0
-2016-09-06 10:24:52,919 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:24:52,926 DEBUG: 			View 0 : 0.721893491124
-2016-09-06 10:24:52,933 DEBUG: 			View 1 : 0.674556213018
-2016-09-06 10:24:52,941 DEBUG: 			View 2 : 0.656804733728
-2016-09-06 10:24:52,948 DEBUG: 			View 3 : 0.674556213018
-2016-09-06 10:24:52,987 DEBUG: 			 Best view : 		View0
-2016-09-06 10:24:53,188 INFO: 	Start: 	 Classification
-2016-09-06 10:24:53,527 INFO: 	Done: 	 Fold number 3
-2016-09-06 10:24:53,527 INFO: 	Start:	 Fold number 4
-2016-09-06 10:24:53,556 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:24:53,563 DEBUG: 			View 0 : 0.526627218935
-2016-09-06 10:24:53,570 DEBUG: 			View 1 : 0.485207100592
-2016-09-06 10:24:53,577 DEBUG: 			View 2 : 0.514792899408
-2016-09-06 10:24:53,584 DEBUG: 			View 3 : 0.497041420118
-2016-09-06 10:24:53,614 DEBUG: 			 Best view : 		View1
-2016-09-06 10:24:53,689 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:24:53,696 DEBUG: 			View 0 : 0.692307692308
-2016-09-06 10:24:53,704 DEBUG: 			View 1 : 0.680473372781
-2016-09-06 10:24:53,711 DEBUG: 			View 2 : 0.739644970414
-2016-09-06 10:24:53,719 DEBUG: 			View 3 : 0.615384615385
-2016-09-06 10:24:53,755 DEBUG: 			 Best view : 		View2
-2016-09-06 10:24:53,893 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:24:53,900 DEBUG: 			View 0 : 0.692307692308
-2016-09-06 10:24:53,908 DEBUG: 			View 1 : 0.615384615385
-2016-09-06 10:24:53,915 DEBUG: 			View 2 : 0.739644970414
-2016-09-06 10:24:53,923 DEBUG: 			View 3 : 0.615384615385
-2016-09-06 10:24:53,960 DEBUG: 			 Best view : 		View2
-2016-09-06 10:24:54,163 INFO: 	Start: 	 Classification
-2016-09-06 10:24:54,501 INFO: 	Done: 	 Fold number 4
-2016-09-06 10:24:54,501 INFO: 	Start:	 Fold number 5
-2016-09-06 10:24:54,530 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:24:54,538 DEBUG: 			View 0 : 0.538461538462
-2016-09-06 10:24:54,545 DEBUG: 			View 1 : 0.556213017751
-2016-09-06 10:24:54,552 DEBUG: 			View 2 : 0.479289940828
-2016-09-06 10:24:54,560 DEBUG: 			View 3 : 0.550295857988
-2016-09-06 10:24:54,590 DEBUG: 			 Best view : 		View2
-2016-09-06 10:24:54,665 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:24:54,672 DEBUG: 			View 0 : 0.686390532544
-2016-09-06 10:24:54,680 DEBUG: 			View 1 : 0.710059171598
-2016-09-06 10:24:54,687 DEBUG: 			View 2 : 0.745562130178
-2016-09-06 10:24:54,695 DEBUG: 			View 3 : 0.763313609467
-2016-09-06 10:24:54,731 DEBUG: 			 Best view : 		View3
-2016-09-06 10:24:54,871 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:24:54,878 DEBUG: 			View 0 : 0.710059171598
-2016-09-06 10:24:54,886 DEBUG: 			View 1 : 0.710059171598
-2016-09-06 10:24:54,893 DEBUG: 			View 2 : 0.745562130178
-2016-09-06 10:24:54,900 DEBUG: 			View 3 : 0.763313609467
-2016-09-06 10:24:54,939 DEBUG: 			 Best view : 		View3
-2016-09-06 10:24:55,142 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:24:55,149 DEBUG: 			View 0 : 0.680473372781
-2016-09-06 10:24:55,157 DEBUG: 			View 1 : 0.668639053254
-2016-09-06 10:24:55,164 DEBUG: 			View 2 : 0.775147928994
-2016-09-06 10:24:55,172 DEBUG: 			View 3 : 0.698224852071
-2016-09-06 10:24:55,213 DEBUG: 			 Best view : 		View2
-2016-09-06 10:24:55,477 INFO: 	Start: 	 Classification
-2016-09-06 10:24:55,929 INFO: 	Done: 	 Fold number 5
-2016-09-06 10:24:55,930 INFO: Done:	 Classification
-2016-09-06 10:24:55,930 INFO: Info:	 Time for Classification: 5[s]
-2016-09-06 10:24:55,930 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0a29c62f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 50d1ffc4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ec4b6b24..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.642857142857
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f6c91352..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 99221590..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ff3894ab..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d1109d9f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 68f5fddd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2ddf8a81..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8f033747..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 417cdc11..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 803d6342..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1bfba82f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7fbf2011..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4792d66c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 886be201..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.377777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8612a084..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2421faf6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bfb5e334..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 16)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 90bc0163..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5cf265c5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.628571428571
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d333a548..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.42380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.42380952381
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9bb3ab95..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3985361b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 81641b03..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b6a08cb0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 774d9b32..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 26
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ca851391..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 26, max_depth : 18
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4672960d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7f7f53ac..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5274
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 5e79d406..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1623 +0,0 @@
-2016-09-06 10:25:43,492 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:25:43,492 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00020134375 Gbytes /!\ 
-2016-09-06 10:25:48,507 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:25:48,510 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:25:48,559 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:48,559 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:48,560 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:25:48,560 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:25:48,560 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:48,560 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:48,560 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:48,560 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:48,561 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:48,561 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:48,561 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:48,561 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:48,561 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:48,561 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:48,604 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:48,604 DEBUG: Start:	 Training
-2016-09-06 10:25:48,607 DEBUG: Info:	 Time for Training: 0.0481219291687[s]
-2016-09-06 10:25:48,607 DEBUG: Done:	 Training
-2016-09-06 10:25:48,607 DEBUG: Start:	 Predicting
-2016-09-06 10:25:48,609 DEBUG: Done:	 Predicting
-2016-09-06 10:25:48,610 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:48,611 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:48,611 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:25:48,611 INFO: Done:	 Result Analysis
-2016-09-06 10:25:48,619 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:48,619 DEBUG: Start:	 Training
-2016-09-06 10:25:48,624 DEBUG: Info:	 Time for Training: 0.0653121471405[s]
-2016-09-06 10:25:48,624 DEBUG: Done:	 Training
-2016-09-06 10:25:48,624 DEBUG: Start:	 Predicting
-2016-09-06 10:25:48,627 DEBUG: Done:	 Predicting
-2016-09-06 10:25:48,627 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:48,629 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:48,629 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:25:48,629 INFO: Done:	 Result Analysis
-2016-09-06 10:25:48,709 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:48,709 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:48,710 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:25:48,710 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:25:48,710 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:48,710 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:48,710 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:48,710 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:48,711 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:48,711 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:48,711 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:48,711 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:48,711 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:48,711 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:48,767 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:48,767 DEBUG: Start:	 Training
-2016-09-06 10:25:48,768 DEBUG: Info:	 Time for Training: 0.0595369338989[s]
-2016-09-06 10:25:48,768 DEBUG: Done:	 Training
-2016-09-06 10:25:48,768 DEBUG: Start:	 Predicting
-2016-09-06 10:25:48,780 DEBUG: Done:	 Predicting
-2016-09-06 10:25:48,780 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:48,782 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:48,782 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:25:48,783 INFO: Done:	 Result Analysis
-2016-09-06 10:25:49,068 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:49,068 DEBUG: Start:	 Training
-2016-09-06 10:25:49,115 DEBUG: Info:	 Time for Training: 0.406928062439[s]
-2016-09-06 10:25:49,116 DEBUG: Done:	 Training
-2016-09-06 10:25:49,116 DEBUG: Start:	 Predicting
-2016-09-06 10:25:49,122 DEBUG: Done:	 Predicting
-2016-09-06 10:25:49,122 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:49,123 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:49,123 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 18, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,123 INFO: Done:	 Result Analysis
-2016-09-06 10:25:49,263 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,263 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,264 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:25:49,264 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:25:49,264 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:49,264 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:49,265 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,265 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,265 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,265 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,265 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:49,265 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:49,265 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,265 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,313 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:49,313 DEBUG: Start:	 Training
-2016-09-06 10:25:49,314 DEBUG: Info:	 Time for Training: 0.0512311458588[s]
-2016-09-06 10:25:49,314 DEBUG: Done:	 Training
-2016-09-06 10:25:49,314 DEBUG: Start:	 Predicting
-2016-09-06 10:25:49,320 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:49,320 DEBUG: Start:	 Training
-2016-09-06 10:25:49,330 DEBUG: Done:	 Predicting
-2016-09-06 10:25:49,330 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:49,332 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:49,332 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,332 INFO: Done:	 Result Analysis
-2016-09-06 10:25:49,344 DEBUG: Info:	 Time for Training: 0.0809261798859[s]
-2016-09-06 10:25:49,344 DEBUG: Done:	 Training
-2016-09-06 10:25:49,344 DEBUG: Start:	 Predicting
-2016-09-06 10:25:49,348 DEBUG: Done:	 Predicting
-2016-09-06 10:25:49,348 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:49,349 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:49,349 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,349 INFO: Done:	 Result Analysis
-2016-09-06 10:25:49,411 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,411 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,412 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:25:49,412 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:25:49,412 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:49,412 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:49,413 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,413 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,413 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,413 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:49,413 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,413 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,413 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:49,413 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,462 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:49,462 DEBUG: Start:	 Training
-2016-09-06 10:25:49,472 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:49,472 DEBUG: Start:	 Training
-2016-09-06 10:25:49,480 DEBUG: Info:	 Time for Training: 0.0694200992584[s]
-2016-09-06 10:25:49,480 DEBUG: Done:	 Training
-2016-09-06 10:25:49,480 DEBUG: Start:	 Predicting
-2016-09-06 10:25:49,486 DEBUG: Done:	 Predicting
-2016-09-06 10:25:49,486 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:49,487 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:49,488 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,488 INFO: Done:	 Result Analysis
-2016-09-06 10:25:49,494 DEBUG: Info:	 Time for Training: 0.0838282108307[s]
-2016-09-06 10:25:49,495 DEBUG: Done:	 Training
-2016-09-06 10:25:49,495 DEBUG: Start:	 Predicting
-2016-09-06 10:25:49,499 DEBUG: Done:	 Predicting
-2016-09-06 10:25:49,499 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:49,500 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:49,501 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,501 INFO: Done:	 Result Analysis
-2016-09-06 10:25:49,565 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,565 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,566 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:25:49,566 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:25:49,566 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:49,566 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:49,567 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,567 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,567 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,567 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,567 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:49,567 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:49,568 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,568 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,617 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:49,617 DEBUG: Start:	 Training
-2016-09-06 10:25:49,620 DEBUG: Info:	 Time for Training: 0.0563011169434[s]
-2016-09-06 10:25:49,620 DEBUG: Done:	 Training
-2016-09-06 10:25:49,620 DEBUG: Start:	 Predicting
-2016-09-06 10:25:49,623 DEBUG: Done:	 Predicting
-2016-09-06 10:25:49,623 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:49,624 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:49,624 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,625 INFO: Done:	 Result Analysis
-2016-09-06 10:25:49,632 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:49,632 DEBUG: Start:	 Training
-2016-09-06 10:25:49,636 DEBUG: Info:	 Time for Training: 0.072704076767[s]
-2016-09-06 10:25:49,636 DEBUG: Done:	 Training
-2016-09-06 10:25:49,637 DEBUG: Start:	 Predicting
-2016-09-06 10:25:49,639 DEBUG: Done:	 Predicting
-2016-09-06 10:25:49,639 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:49,641 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:49,641 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,642 INFO: Done:	 Result Analysis
-2016-09-06 10:25:49,706 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,706 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,706 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:25:49,706 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:25:49,706 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:49,706 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:49,707 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,707 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,707 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,707 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,707 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:49,707 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:49,707 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,707 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,745 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:49,745 DEBUG: Start:	 Training
-2016-09-06 10:25:49,745 DEBUG: Info:	 Time for Training: 0.0398440361023[s]
-2016-09-06 10:25:49,745 DEBUG: Done:	 Training
-2016-09-06 10:25:49,746 DEBUG: Start:	 Predicting
-2016-09-06 10:25:49,755 DEBUG: Done:	 Predicting
-2016-09-06 10:25:49,755 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:49,756 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:49,756 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,756 INFO: Done:	 Result Analysis
-2016-09-06 10:25:50,033 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:50,033 DEBUG: Start:	 Training
-2016-09-06 10:25:50,081 DEBUG: Info:	 Time for Training: 0.37574505806[s]
-2016-09-06 10:25:50,081 DEBUG: Done:	 Training
-2016-09-06 10:25:50,082 DEBUG: Start:	 Predicting
-2016-09-06 10:25:50,087 DEBUG: Done:	 Predicting
-2016-09-06 10:25:50,088 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:50,089 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:50,089 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 18, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,089 INFO: Done:	 Result Analysis
-2016-09-06 10:25:50,155 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,156 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:25:50,156 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:50,156 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,156 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:25:50,156 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:50,156 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,157 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,157 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,157 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,157 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:50,157 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:50,157 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,157 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,204 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:50,204 DEBUG: Start:	 Training
-2016-09-06 10:25:50,205 DEBUG: Info:	 Time for Training: 0.0503079891205[s]
-2016-09-06 10:25:50,205 DEBUG: Done:	 Training
-2016-09-06 10:25:50,205 DEBUG: Start:	 Predicting
-2016-09-06 10:25:50,216 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:50,216 DEBUG: Start:	 Training
-2016-09-06 10:25:50,232 DEBUG: Done:	 Predicting
-2016-09-06 10:25:50,232 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:50,236 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:50,236 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,236 INFO: Done:	 Result Analysis
-2016-09-06 10:25:50,245 DEBUG: Info:	 Time for Training: 0.0895159244537[s]
-2016-09-06 10:25:50,245 DEBUG: Done:	 Training
-2016-09-06 10:25:50,245 DEBUG: Start:	 Predicting
-2016-09-06 10:25:50,249 DEBUG: Done:	 Predicting
-2016-09-06 10:25:50,249 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:50,250 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:50,251 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.480952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.480952380952
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,251 INFO: Done:	 Result Analysis
-2016-09-06 10:25:50,403 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,403 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,403 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:25:50,403 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:50,403 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:25:50,403 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:50,404 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,404 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,404 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:50,404 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,404 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,404 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,404 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:50,405 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,454 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:50,454 DEBUG: Start:	 Training
-2016-09-06 10:25:50,466 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:50,466 DEBUG: Start:	 Training
-2016-09-06 10:25:50,472 DEBUG: Info:	 Time for Training: 0.0699520111084[s]
-2016-09-06 10:25:50,472 DEBUG: Done:	 Training
-2016-09-06 10:25:50,472 DEBUG: Start:	 Predicting
-2016-09-06 10:25:50,479 DEBUG: Done:	 Predicting
-2016-09-06 10:25:50,479 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:50,480 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:50,480 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,480 INFO: Done:	 Result Analysis
-2016-09-06 10:25:50,487 DEBUG: Info:	 Time for Training: 0.084676027298[s]
-2016-09-06 10:25:50,487 DEBUG: Done:	 Training
-2016-09-06 10:25:50,487 DEBUG: Start:	 Predicting
-2016-09-06 10:25:50,493 DEBUG: Done:	 Predicting
-2016-09-06 10:25:50,493 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:50,494 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:50,495 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,495 INFO: Done:	 Result Analysis
-2016-09-06 10:25:50,549 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,549 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:25:50,549 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:50,549 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,549 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:25:50,549 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:50,550 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,550 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,550 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,550 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:50,550 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,550 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:50,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,594 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:50,594 DEBUG: Start:	 Training
-2016-09-06 10:25:50,598 DEBUG: Info:	 Time for Training: 0.0494570732117[s]
-2016-09-06 10:25:50,598 DEBUG: Done:	 Training
-2016-09-06 10:25:50,598 DEBUG: Start:	 Predicting
-2016-09-06 10:25:50,601 DEBUG: Done:	 Predicting
-2016-09-06 10:25:50,601 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:50,602 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:50,603 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,603 INFO: Done:	 Result Analysis
-2016-09-06 10:25:50,604 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:50,605 DEBUG: Start:	 Training
-2016-09-06 10:25:50,610 DEBUG: Info:	 Time for Training: 0.0612750053406[s]
-2016-09-06 10:25:50,610 DEBUG: Done:	 Training
-2016-09-06 10:25:50,610 DEBUG: Start:	 Predicting
-2016-09-06 10:25:50,613 DEBUG: Done:	 Predicting
-2016-09-06 10:25:50,613 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:50,615 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:50,615 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,615 INFO: Done:	 Result Analysis
-2016-09-06 10:25:50,695 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,695 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,695 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:25:50,695 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:25:50,696 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:50,696 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:50,696 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,696 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,696 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,696 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:50,696 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,697 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,697 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:50,697 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,731 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:50,732 DEBUG: Start:	 Training
-2016-09-06 10:25:50,732 DEBUG: Info:	 Time for Training: 0.0376601219177[s]
-2016-09-06 10:25:50,732 DEBUG: Done:	 Training
-2016-09-06 10:25:50,732 DEBUG: Start:	 Predicting
-2016-09-06 10:25:50,740 DEBUG: Done:	 Predicting
-2016-09-06 10:25:50,741 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:50,742 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:50,742 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,742 INFO: Done:	 Result Analysis
-2016-09-06 10:25:51,022 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:51,022 DEBUG: Start:	 Training
-2016-09-06 10:25:51,071 DEBUG: Info:	 Time for Training: 0.37623500824[s]
-2016-09-06 10:25:51,071 DEBUG: Done:	 Training
-2016-09-06 10:25:51,071 DEBUG: Start:	 Predicting
-2016-09-06 10:25:51,077 DEBUG: Done:	 Predicting
-2016-09-06 10:25:51,077 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:51,079 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:51,079 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 18, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,079 INFO: Done:	 Result Analysis
-2016-09-06 10:25:51,152 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,152 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,153 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:25:51,153 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:25:51,153 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:51,153 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:51,154 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:51,154 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:51,154 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:51,154 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:51,154 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:51,154 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:51,154 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,154 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,224 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:51,225 DEBUG: Start:	 Training
-2016-09-06 10:25:51,226 DEBUG: Info:	 Time for Training: 0.0744400024414[s]
-2016-09-06 10:25:51,226 DEBUG: Done:	 Training
-2016-09-06 10:25:51,226 DEBUG: Start:	 Predicting
-2016-09-06 10:25:51,232 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:51,232 DEBUG: Start:	 Training
-2016-09-06 10:25:51,243 DEBUG: Done:	 Predicting
-2016-09-06 10:25:51,244 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:51,246 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:51,247 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,247 INFO: Done:	 Result Analysis
-2016-09-06 10:25:51,262 DEBUG: Info:	 Time for Training: 0.110822916031[s]
-2016-09-06 10:25:51,263 DEBUG: Done:	 Training
-2016-09-06 10:25:51,263 DEBUG: Start:	 Predicting
-2016-09-06 10:25:51,266 DEBUG: Done:	 Predicting
-2016-09-06 10:25:51,267 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:51,268 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:51,268 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,268 INFO: Done:	 Result Analysis
-2016-09-06 10:25:51,400 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,400 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,401 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:25:51,401 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:25:51,401 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:51,401 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:51,402 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:51,402 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:51,402 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:51,403 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:51,403 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:51,403 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:51,403 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,403 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,474 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:51,474 DEBUG: Start:	 Training
-2016-09-06 10:25:51,487 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:51,487 DEBUG: Start:	 Training
-2016-09-06 10:25:51,500 DEBUG: Info:	 Time for Training: 0.101441144943[s]
-2016-09-06 10:25:51,501 DEBUG: Done:	 Training
-2016-09-06 10:25:51,501 DEBUG: Start:	 Predicting
-2016-09-06 10:25:51,509 DEBUG: Done:	 Predicting
-2016-09-06 10:25:51,510 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:51,511 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:51,512 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,512 INFO: Done:	 Result Analysis
-2016-09-06 10:25:51,513 DEBUG: Info:	 Time for Training: 0.114230155945[s]
-2016-09-06 10:25:51,513 DEBUG: Done:	 Training
-2016-09-06 10:25:51,513 DEBUG: Start:	 Predicting
-2016-09-06 10:25:51,518 DEBUG: Done:	 Predicting
-2016-09-06 10:25:51,518 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:51,519 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:51,519 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,519 INFO: Done:	 Result Analysis
-2016-09-06 10:25:51,649 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,649 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,649 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:25:51,649 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:25:51,650 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:51,650 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:51,650 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:51,650 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:51,650 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:51,650 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:51,650 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:51,651 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,651 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:51,651 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,688 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:51,688 DEBUG: Start:	 Training
-2016-09-06 10:25:51,690 DEBUG: Info:	 Time for Training: 0.0416340827942[s]
-2016-09-06 10:25:51,690 DEBUG: Done:	 Training
-2016-09-06 10:25:51,690 DEBUG: Start:	 Predicting
-2016-09-06 10:25:51,693 DEBUG: Done:	 Predicting
-2016-09-06 10:25:51,693 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:51,694 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:51,694 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.914285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.914285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,695 INFO: Done:	 Result Analysis
-2016-09-06 10:25:51,703 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:51,703 DEBUG: Start:	 Training
-2016-09-06 10:25:51,708 DEBUG: Info:	 Time for Training: 0.0598511695862[s]
-2016-09-06 10:25:51,708 DEBUG: Done:	 Training
-2016-09-06 10:25:51,709 DEBUG: Start:	 Predicting
-2016-09-06 10:25:51,712 DEBUG: Done:	 Predicting
-2016-09-06 10:25:51,712 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:51,713 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:51,714 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,714 INFO: Done:	 Result Analysis
-2016-09-06 10:25:51,800 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,800 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:25:51,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:25:51,801 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:51,801 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:51,802 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:51,802 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:51,802 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:51,802 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:51,802 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:51,802 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:51,802 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,802 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,853 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:51,853 DEBUG: Start:	 Training
-2016-09-06 10:25:51,854 DEBUG: Info:	 Time for Training: 0.0547788143158[s]
-2016-09-06 10:25:51,855 DEBUG: Done:	 Training
-2016-09-06 10:25:51,855 DEBUG: Start:	 Predicting
-2016-09-06 10:25:51,866 DEBUG: Done:	 Predicting
-2016-09-06 10:25:51,866 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:51,868 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:51,868 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,868 INFO: Done:	 Result Analysis
-2016-09-06 10:25:52,153 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:52,153 DEBUG: Start:	 Training
-2016-09-06 10:25:52,201 DEBUG: Info:	 Time for Training: 0.401795864105[s]
-2016-09-06 10:25:52,202 DEBUG: Done:	 Training
-2016-09-06 10:25:52,202 DEBUG: Start:	 Predicting
-2016-09-06 10:25:52,208 DEBUG: Done:	 Predicting
-2016-09-06 10:25:52,208 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:52,209 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:52,209 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 18, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:25:52,209 INFO: Done:	 Result Analysis
-2016-09-06 10:25:52,349 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:52,350 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:52,350 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:25:52,350 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:25:52,350 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:52,350 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:25:52,351 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:52,351 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:52,351 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:52,351 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:52,351 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:52,351 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:25:52,351 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:52,351 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:25:52,399 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:52,399 DEBUG: Start:	 Training
-2016-09-06 10:25:52,400 DEBUG: Info:	 Time for Training: 0.0507619380951[s]
-2016-09-06 10:25:52,400 DEBUG: Done:	 Training
-2016-09-06 10:25:52,400 DEBUG: Start:	 Predicting
-2016-09-06 10:25:52,407 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:25:52,407 DEBUG: Start:	 Training
-2016-09-06 10:25:52,425 DEBUG: Done:	 Predicting
-2016-09-06 10:25:52,426 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:52,427 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:52,427 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.644444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.644444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:25:52,428 INFO: Done:	 Result Analysis
-2016-09-06 10:25:52,431 DEBUG: Info:	 Time for Training: 0.0818819999695[s]
-2016-09-06 10:25:52,431 DEBUG: Done:	 Training
-2016-09-06 10:25:52,431 DEBUG: Start:	 Predicting
-2016-09-06 10:25:52,435 DEBUG: Done:	 Predicting
-2016-09-06 10:25:52,435 DEBUG: Start:	 Getting Results
-2016-09-06 10:25:52,436 DEBUG: Done:	 Getting Results
-2016-09-06 10:25:52,436 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.452380952381
-accuracy_score on test : 0.388888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.452380952381
-		- Score on test : 0.388888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:25:52,436 INFO: Done:	 Result Analysis
-2016-09-06 10:25:52,647 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:25:52,647 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:25:52,648 INFO: Info:	 Shape of View0 :(300, 20)
-2016-09-06 10:25:52,649 INFO: Info:	 Shape of View1 :(300, 20)
-2016-09-06 10:25:52,649 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:25:52,649 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:25:52,649 INFO: Info:	 Shape of View2 :(300, 20)
-2016-09-06 10:25:52,650 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 10:25:52,650 INFO: Info:	 Shape of View0 :(300, 20)
-2016-09-06 10:25:52,650 INFO: Done:	 Read Database Files
-2016-09-06 10:25:52,651 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:25:52,652 INFO: Info:	 Shape of View1 :(300, 20)
-2016-09-06 10:25:52,653 INFO: Info:	 Shape of View2 :(300, 20)
-2016-09-06 10:25:52,654 INFO: Info:	 Shape of View3 :(300, 18)
-2016-09-06 10:25:52,654 INFO: Done:	 Read Database Files
-2016-09-06 10:25:52,654 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:25:52,656 INFO: Done:	 Determine validation split
-2016-09-06 10:25:52,656 INFO: Start:	 Determine 5 folds
-2016-09-06 10:25:52,658 INFO: Done:	 Determine validation split
-2016-09-06 10:25:52,658 INFO: Start:	 Determine 5 folds
-2016-09-06 10:25:52,665 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 10:25:52,665 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:25:52,666 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 10:25:52,666 INFO: Done:	 Determine folds
-2016-09-06 10:25:52,666 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:25:52,666 INFO: Start:	 Classification
-2016-09-06 10:25:52,666 INFO: 	Start:	 Fold number 1
-2016-09-06 10:25:52,669 INFO: Info:	 Length of Learning Sets: 168
-2016-09-06 10:25:52,669 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:25:52,670 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 10:25:52,670 INFO: Done:	 Determine folds
-2016-09-06 10:25:52,670 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:25:52,670 INFO: Start:	 Classification
-2016-09-06 10:25:52,670 INFO: 	Start:	 Fold number 1
-2016-09-06 10:25:52,699 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:25:52,708 DEBUG: 			View 0 : 0.434523809524
-2016-09-06 10:25:52,716 DEBUG: 			View 1 : 0.470238095238
-2016-09-06 10:25:52,724 DEBUG: 			View 2 : 0.505952380952
-2016-09-06 10:25:52,731 DEBUG: 			View 3 : 0.553571428571
-2016-09-06 10:25:52,763 DEBUG: 			 Best view : 		View0
-2016-09-06 10:25:52,841 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:25:52,849 DEBUG: 			View 0 : 0.720238095238
-2016-09-06 10:25:52,857 DEBUG: 			View 1 : 0.666666666667
-2016-09-06 10:25:52,865 DEBUG: 			View 2 : 0.660714285714
-2016-09-06 10:25:52,872 DEBUG: 			View 3 : 0.744047619048
-2016-09-06 10:25:52,909 DEBUG: 			 Best view : 		View3
-2016-09-06 10:25:53,053 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:25:53,071 DEBUG: 			View 0 : 0.720238095238
-2016-09-06 10:25:53,080 DEBUG: 			View 1 : 0.666666666667
-2016-09-06 10:25:53,088 DEBUG: 			View 2 : 0.660714285714
-2016-09-06 10:25:53,095 DEBUG: 			View 3 : 0.744047619048
-2016-09-06 10:25:53,134 DEBUG: 			 Best view : 		View3
-2016-09-06 10:25:53,338 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:25:53,345 DEBUG: 			View 0 : 0.77380952381
-2016-09-06 10:25:53,353 DEBUG: 			View 1 : 0.732142857143
-2016-09-06 10:25:53,360 DEBUG: 			View 2 : 0.678571428571
-2016-09-06 10:25:53,369 DEBUG: 			View 3 : 0.690476190476
-2016-09-06 10:25:53,419 DEBUG: 			 Best view : 		View0
-2016-09-06 10:25:53,697 INFO: 	Start: 	 Classification
-2016-09-06 10:25:54,148 INFO: 	Done: 	 Fold number 1
-2016-09-06 10:25:54,148 INFO: 	Start:	 Fold number 2
-2016-09-06 10:25:54,177 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:25:54,184 DEBUG: 			View 0 : 0.5
-2016-09-06 10:25:54,190 DEBUG: 			View 1 : 0.5
-2016-09-06 10:25:54,197 DEBUG: 			View 2 : 0.5
-2016-09-06 10:25:54,204 DEBUG: 			View 3 : 0.5
-2016-09-06 10:25:54,234 DEBUG: 			 Best view : 		View0
-2016-09-06 10:25:54,309 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:25:54,317 DEBUG: 			View 0 : 0.72619047619
-2016-09-06 10:25:54,325 DEBUG: 			View 1 : 0.761904761905
-2016-09-06 10:25:54,333 DEBUG: 			View 2 : 0.666666666667
-2016-09-06 10:25:54,341 DEBUG: 			View 3 : 0.732142857143
-2016-09-06 10:25:54,377 DEBUG: 			 Best view : 		View1
-2016-09-06 10:25:54,515 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:25:54,523 DEBUG: 			View 0 : 0.72619047619
-2016-09-06 10:25:54,531 DEBUG: 			View 1 : 0.761904761905
-2016-09-06 10:25:54,539 DEBUG: 			View 2 : 0.666666666667
-2016-09-06 10:25:54,546 DEBUG: 			View 3 : 0.732142857143
-2016-09-06 10:25:54,584 DEBUG: 			 Best view : 		View1
-2016-09-06 10:25:54,786 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:25:54,794 DEBUG: 			View 0 : 0.678571428571
-2016-09-06 10:25:54,802 DEBUG: 			View 1 : 0.744047619048
-2016-09-06 10:25:54,809 DEBUG: 			View 2 : 0.738095238095
-2016-09-06 10:25:54,817 DEBUG: 			View 3 : 0.708333333333
-2016-09-06 10:25:54,857 DEBUG: 			 Best view : 		View1
-2016-09-06 10:25:55,121 INFO: 	Start: 	 Classification
-2016-09-06 10:25:55,575 INFO: 	Done: 	 Fold number 2
-2016-09-06 10:25:55,575 INFO: 	Start:	 Fold number 3
-2016-09-06 10:25:55,604 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:25:55,611 DEBUG: 			View 0 : 0.577380952381
-2016-09-06 10:25:55,619 DEBUG: 			View 1 : 0.52380952381
-2016-09-06 10:25:55,626 DEBUG: 			View 2 : 0.47619047619
-2016-09-06 10:25:55,633 DEBUG: 			View 3 : 0.541666666667
-2016-09-06 10:25:55,664 DEBUG: 			 Best view : 		View0
-2016-09-06 10:25:55,738 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:25:55,746 DEBUG: 			View 0 : 0.72619047619
-2016-09-06 10:25:55,754 DEBUG: 			View 1 : 0.77380952381
-2016-09-06 10:25:55,762 DEBUG: 			View 2 : 0.714285714286
-2016-09-06 10:25:55,769 DEBUG: 			View 3 : 0.767857142857
-2016-09-06 10:25:55,805 DEBUG: 			 Best view : 		View1
-2016-09-06 10:25:55,944 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:25:55,952 DEBUG: 			View 0 : 0.72619047619
-2016-09-06 10:25:55,960 DEBUG: 			View 1 : 0.77380952381
-2016-09-06 10:25:55,967 DEBUG: 			View 2 : 0.714285714286
-2016-09-06 10:25:55,975 DEBUG: 			View 3 : 0.767857142857
-2016-09-06 10:25:56,013 DEBUG: 			 Best view : 		View1
-2016-09-06 10:25:56,215 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:25:56,223 DEBUG: 			View 0 : 0.72619047619
-2016-09-06 10:25:56,231 DEBUG: 			View 1 : 0.672619047619
-2016-09-06 10:25:56,239 DEBUG: 			View 2 : 0.738095238095
-2016-09-06 10:25:56,246 DEBUG: 			View 3 : 0.702380952381
-2016-09-06 10:25:56,287 DEBUG: 			 Best view : 		View2
-2016-09-06 10:25:56,554 INFO: 	Start: 	 Classification
-2016-09-06 10:25:57,010 INFO: 	Done: 	 Fold number 3
-2016-09-06 10:25:57,010 INFO: 	Start:	 Fold number 4
-2016-09-06 10:25:57,038 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:25:57,045 DEBUG: 			View 0 : 0.535714285714
-2016-09-06 10:25:57,053 DEBUG: 			View 1 : 0.5
-2016-09-06 10:25:57,060 DEBUG: 			View 2 : 0.440476190476
-2016-09-06 10:25:57,067 DEBUG: 			View 3 : 0.47619047619
-2016-09-06 10:25:57,097 DEBUG: 			 Best view : 		View3
-2016-09-06 10:25:57,171 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:25:57,178 DEBUG: 			View 0 : 0.779761904762
-2016-09-06 10:25:57,186 DEBUG: 			View 1 : 0.744047619048
-2016-09-06 10:25:57,193 DEBUG: 			View 2 : 0.702380952381
-2016-09-06 10:25:57,201 DEBUG: 			View 3 : 0.714285714286
-2016-09-06 10:25:57,237 DEBUG: 			 Best view : 		View0
-2016-09-06 10:25:57,376 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:25:57,383 DEBUG: 			View 0 : 0.779761904762
-2016-09-06 10:25:57,391 DEBUG: 			View 1 : 0.744047619048
-2016-09-06 10:25:57,399 DEBUG: 			View 2 : 0.702380952381
-2016-09-06 10:25:57,406 DEBUG: 			View 3 : 0.714285714286
-2016-09-06 10:25:57,445 DEBUG: 			 Best view : 		View0
-2016-09-06 10:25:57,648 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:25:57,656 DEBUG: 			View 0 : 0.72619047619
-2016-09-06 10:25:57,664 DEBUG: 			View 1 : 0.672619047619
-2016-09-06 10:25:57,671 DEBUG: 			View 2 : 0.690476190476
-2016-09-06 10:25:57,678 DEBUG: 			View 3 : 0.732142857143
-2016-09-06 10:25:57,719 DEBUG: 			 Best view : 		View3
-2016-09-06 10:25:57,985 INFO: 	Start: 	 Classification
-2016-09-06 10:25:58,436 INFO: 	Done: 	 Fold number 4
-2016-09-06 10:25:58,436 INFO: 	Start:	 Fold number 5
-2016-09-06 10:25:58,464 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:25:58,471 DEBUG: 			View 0 : 0.5
-2016-09-06 10:25:58,477 DEBUG: 			View 1 : 0.5
-2016-09-06 10:25:58,484 DEBUG: 			View 2 : 0.5
-2016-09-06 10:25:58,491 DEBUG: 			View 3 : 0.5
-2016-09-06 10:25:58,520 DEBUG: 			 Best view : 		View0
-2016-09-06 10:25:58,595 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:25:58,602 DEBUG: 			View 0 : 0.75
-2016-09-06 10:25:58,610 DEBUG: 			View 1 : 0.75
-2016-09-06 10:25:58,618 DEBUG: 			View 2 : 0.714285714286
-2016-09-06 10:25:58,625 DEBUG: 			View 3 : 0.738095238095
-2016-09-06 10:25:58,661 DEBUG: 			 Best view : 		View0
-2016-09-06 10:25:58,798 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:25:58,806 DEBUG: 			View 0 : 0.75
-2016-09-06 10:25:58,813 DEBUG: 			View 1 : 0.75
-2016-09-06 10:25:58,821 DEBUG: 			View 2 : 0.714285714286
-2016-09-06 10:25:58,829 DEBUG: 			View 3 : 0.738095238095
-2016-09-06 10:25:58,867 DEBUG: 			 Best view : 		View0
-2016-09-06 10:25:59,068 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:25:59,075 DEBUG: 			View 0 : 0.720238095238
-2016-09-06 10:25:59,083 DEBUG: 			View 1 : 0.642857142857
-2016-09-06 10:25:59,091 DEBUG: 			View 2 : 0.660714285714
-2016-09-06 10:25:59,098 DEBUG: 			View 3 : 0.678571428571
-2016-09-06 10:25:59,138 DEBUG: 			 Best view : 		View0
-2016-09-06 10:25:59,405 INFO: 	Start: 	 Classification
-2016-09-06 10:25:59,857 INFO: 	Done: 	 Fold number 5
-2016-09-06 10:25:59,857 INFO: Done:	 Classification
-2016-09-06 10:25:59,857 INFO: Info:	 Time for Classification: 7[s]
-2016-09-06 10:25:59,857 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 10:26:02,299 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 76.1904761905
-	-On Test : 50.9523809524
-	-On Validation : 51.3333333333Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 20), View1 of shape (300, 20), View2 of shape (300, 20), View3 of shape (300, 18)
-	-5 folds
-	- Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:06        0:00:00
-	          Total        0:00:19        0:00:02
-	So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.264880952381
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.253571428571
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.250595238095
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.273214285714
-			- Percentage of time chosen : 0.2
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.263095238095
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.276785714286
-			- Percentage of time chosen : 0.3
-		- On View2 : 
-			- Mean average Accuracy : 0.257142857143
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.267261904762
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.275595238095
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.274404761905
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.264285714286
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.277976190476
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.282142857143
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.266071428571
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.253571428571
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.26369047619
-			- Percentage of time chosen : 0.2
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.272023809524
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.264285714286
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.258928571429
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.265476190476
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 74.4047619048
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 76.1904761905
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 77.380952381
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 77.9761904762
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 75.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View0
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 74.4047619048
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 76.1904761905
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 77.380952381
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 77.9761904762
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 75.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View0
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 74.4047619048
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 76.1904761905
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 77.380952381
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 77.9761904762
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 75.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View0
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-2016-09-06 10:26:02,608 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d211664d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d51b4eaf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e3ecbf95..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4b027c77..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a7e7788a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 30353e79..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b1fdee3f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 18, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d845e8ff..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7979763e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0d73c12c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e7530b20..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 81b99c2e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e1cc8a5d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 28d885b8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e4c62d65..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 18, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 524dffb8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d606bc69..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.480952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.480952380952
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 453b1235..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7c633b5c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 672961f8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0eb148d0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.914285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.914285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bb5e97e2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 41
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c393d706..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 18, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 871e4290..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a6d7a519..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f7ae9cc6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1a3bc86e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e5648785..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 18, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a809878b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.644444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.644444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 805ad6de..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.452380952381
-accuracy_score on test : 0.388888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5608
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.452380952381
-		- Score on test : 0.388888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 0873f3f6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,235 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 76.1904761905
-	-On Test : 50.9523809524
-	-On Validation : 51.3333333333Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 20), View1 of shape (300, 20), View2 of shape (300, 20), View3 of shape (300, 18)
-	-5 folds
-	- Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:06        0:00:00
-	          Total        0:00:19        0:00:02
-	So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.264880952381
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.253571428571
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.250595238095
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.273214285714
-			- Percentage of time chosen : 0.2
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.263095238095
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.276785714286
-			- Percentage of time chosen : 0.3
-		- On View2 : 
-			- Mean average Accuracy : 0.257142857143
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.267261904762
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.275595238095
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.274404761905
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.264285714286
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.277976190476
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.282142857143
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.266071428571
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.253571428571
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.26369047619
-			- Percentage of time chosen : 0.2
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.272023809524
-			- Percentage of time chosen : 1.0
-		- On View1 : 
-			- Mean average Accuracy : 0.264285714286
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.258928571429
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.265476190476
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 74.4047619048
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 76.1904761905
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 77.380952381
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 77.9761904762
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 75.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View0
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 74.4047619048
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 76.1904761905
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 77.380952381
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View1
-		 Fold 4
-			Accuracy on train : 77.9761904762
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 75.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View0
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 74.4047619048
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 76.1904761905
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 77.380952381
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.1111111111
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 77.9761904762
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 75.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.2222222222
-			Selected View : View0
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 50.0
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.0
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index edd1146e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1624 +0,0 @@
-2016-09-06 10:31:15,988 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:31:15,989 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00010290625 Gbytes /!\ 
-2016-09-06 10:31:20,994 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:31:20,996 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:31:21,043 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,043 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,044 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:31:21,044 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:31:21,044 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:21,044 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:21,045 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,045 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,045 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,045 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,045 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:21,045 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:21,045 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,045 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,080 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:21,080 DEBUG: Start:	 Training
-2016-09-06 10:31:21,082 DEBUG: Info:	 Time for Training: 0.0389211177826[s]
-2016-09-06 10:31:21,082 DEBUG: Done:	 Training
-2016-09-06 10:31:21,082 DEBUG: Start:	 Predicting
-2016-09-06 10:31:21,085 DEBUG: Done:	 Predicting
-2016-09-06 10:31:21,085 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:21,086 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:21,086 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.938095238095
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.938095238095
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,087 INFO: Done:	 Result Analysis
-2016-09-06 10:31:21,095 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:21,095 DEBUG: Start:	 Training
-2016-09-06 10:31:21,098 DEBUG: Info:	 Time for Training: 0.0554401874542[s]
-2016-09-06 10:31:21,098 DEBUG: Done:	 Training
-2016-09-06 10:31:21,099 DEBUG: Start:	 Predicting
-2016-09-06 10:31:21,101 DEBUG: Done:	 Predicting
-2016-09-06 10:31:21,101 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:21,103 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:21,103 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,104 INFO: Done:	 Result Analysis
-2016-09-06 10:31:21,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:31:21,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:31:21,197 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:21,197 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:21,197 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,197 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,198 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,198 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,198 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:21,198 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:21,198 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,198 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,230 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:21,230 DEBUG: Start:	 Training
-2016-09-06 10:31:21,231 DEBUG: Info:	 Time for Training: 0.0347671508789[s]
-2016-09-06 10:31:21,231 DEBUG: Done:	 Training
-2016-09-06 10:31:21,231 DEBUG: Start:	 Predicting
-2016-09-06 10:31:21,238 DEBUG: Done:	 Predicting
-2016-09-06 10:31:21,238 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:21,239 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:21,239 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,240 INFO: Done:	 Result Analysis
-2016-09-06 10:31:21,497 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:21,497 DEBUG: Start:	 Training
-2016-09-06 10:31:21,540 DEBUG: Info:	 Time for Training: 0.343922138214[s]
-2016-09-06 10:31:21,540 DEBUG: Done:	 Training
-2016-09-06 10:31:21,540 DEBUG: Start:	 Predicting
-2016-09-06 10:31:21,546 DEBUG: Done:	 Predicting
-2016-09-06 10:31:21,546 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:21,548 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:21,548 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 17, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,548 INFO: Done:	 Result Analysis
-2016-09-06 10:31:21,646 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,646 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,646 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:31:21,646 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:31:21,646 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:21,647 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:21,647 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,647 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,647 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,647 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,647 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:21,647 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:21,647 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,647 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,692 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:21,692 DEBUG: Start:	 Training
-2016-09-06 10:31:21,693 DEBUG: Info:	 Time for Training: 0.0476639270782[s]
-2016-09-06 10:31:21,693 DEBUG: Done:	 Training
-2016-09-06 10:31:21,693 DEBUG: Start:	 Predicting
-2016-09-06 10:31:21,698 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:21,698 DEBUG: Start:	 Training
-2016-09-06 10:31:21,710 DEBUG: Done:	 Predicting
-2016-09-06 10:31:21,710 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:21,713 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:21,713 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,713 INFO: Done:	 Result Analysis
-2016-09-06 10:31:21,721 DEBUG: Info:	 Time for Training: 0.0758979320526[s]
-2016-09-06 10:31:21,722 DEBUG: Done:	 Training
-2016-09-06 10:31:21,722 DEBUG: Start:	 Predicting
-2016-09-06 10:31:21,725 DEBUG: Done:	 Predicting
-2016-09-06 10:31:21,725 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:21,726 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:21,726 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,727 INFO: Done:	 Result Analysis
-2016-09-06 10:31:21,797 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,797 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,798 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:31:21,798 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:31:21,798 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:21,798 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:21,799 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,799 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,799 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,799 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,799 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:21,799 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:21,799 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,799 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,869 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:21,869 DEBUG: Start:	 Training
-2016-09-06 10:31:21,877 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:21,877 DEBUG: Start:	 Training
-2016-09-06 10:31:21,892 DEBUG: Info:	 Time for Training: 0.0960838794708[s]
-2016-09-06 10:31:21,893 DEBUG: Done:	 Training
-2016-09-06 10:31:21,893 DEBUG: Start:	 Predicting
-2016-09-06 10:31:21,899 DEBUG: Done:	 Predicting
-2016-09-06 10:31:21,900 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:21,900 DEBUG: Info:	 Time for Training: 0.103469848633[s]
-2016-09-06 10:31:21,900 DEBUG: Done:	 Training
-2016-09-06 10:31:21,900 DEBUG: Start:	 Predicting
-2016-09-06 10:31:21,901 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:21,901 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,901 INFO: Done:	 Result Analysis
-2016-09-06 10:31:21,903 DEBUG: Done:	 Predicting
-2016-09-06 10:31:21,903 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:21,905 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:21,905 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,905 INFO: Done:	 Result Analysis
-2016-09-06 10:31:22,048 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,048 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,049 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:31:22,049 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:31:22,049 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:22,049 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:22,051 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,051 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,051 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,051 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,051 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:22,051 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:22,051 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,051 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,102 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:22,102 DEBUG: Start:	 Training
-2016-09-06 10:31:22,104 DEBUG: Info:	 Time for Training: 0.0569620132446[s]
-2016-09-06 10:31:22,105 DEBUG: Done:	 Training
-2016-09-06 10:31:22,105 DEBUG: Start:	 Predicting
-2016-09-06 10:31:22,108 DEBUG: Done:	 Predicting
-2016-09-06 10:31:22,109 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:22,111 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:22,111 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.838095238095
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.838095238095
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,111 INFO: Done:	 Result Analysis
-2016-09-06 10:31:22,121 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:22,121 DEBUG: Start:	 Training
-2016-09-06 10:31:22,125 DEBUG: Info:	 Time for Training: 0.0774049758911[s]
-2016-09-06 10:31:22,125 DEBUG: Done:	 Training
-2016-09-06 10:31:22,125 DEBUG: Start:	 Predicting
-2016-09-06 10:31:22,128 DEBUG: Done:	 Predicting
-2016-09-06 10:31:22,128 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:22,130 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:22,130 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,130 INFO: Done:	 Result Analysis
-2016-09-06 10:31:22,194 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,194 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,194 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:31:22,194 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:31:22,194 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:22,194 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:22,195 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,195 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,195 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,195 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,196 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:22,196 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:22,196 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,196 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,248 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:22,248 DEBUG: Start:	 Training
-2016-09-06 10:31:22,248 DEBUG: Info:	 Time for Training: 0.0551888942719[s]
-2016-09-06 10:31:22,248 DEBUG: Done:	 Training
-2016-09-06 10:31:22,249 DEBUG: Start:	 Predicting
-2016-09-06 10:31:22,255 DEBUG: Done:	 Predicting
-2016-09-06 10:31:22,256 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:22,257 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:22,257 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,257 INFO: Done:	 Result Analysis
-2016-09-06 10:31:22,555 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:22,555 DEBUG: Start:	 Training
-2016-09-06 10:31:22,602 DEBUG: Info:	 Time for Training: 0.409327983856[s]
-2016-09-06 10:31:22,603 DEBUG: Done:	 Training
-2016-09-06 10:31:22,603 DEBUG: Start:	 Predicting
-2016-09-06 10:31:22,609 DEBUG: Done:	 Predicting
-2016-09-06 10:31:22,609 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:22,610 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:22,610 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 17, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,611 INFO: Done:	 Result Analysis
-2016-09-06 10:31:22,751 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,751 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,752 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:31:22,752 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:31:22,752 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:22,752 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:22,753 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,753 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,753 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,753 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,753 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:22,753 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:22,753 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,753 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,823 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:22,823 DEBUG: Start:	 Training
-2016-09-06 10:31:22,824 DEBUG: Info:	 Time for Training: 0.0737769603729[s]
-2016-09-06 10:31:22,824 DEBUG: Done:	 Training
-2016-09-06 10:31:22,825 DEBUG: Start:	 Predicting
-2016-09-06 10:31:22,828 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:22,828 DEBUG: Start:	 Training
-2016-09-06 10:31:22,836 DEBUG: Done:	 Predicting
-2016-09-06 10:31:22,837 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:22,839 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:22,839 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,839 INFO: Done:	 Result Analysis
-2016-09-06 10:31:22,852 DEBUG: Info:	 Time for Training: 0.10190820694[s]
-2016-09-06 10:31:22,853 DEBUG: Done:	 Training
-2016-09-06 10:31:22,853 DEBUG: Start:	 Predicting
-2016-09-06 10:31:22,856 DEBUG: Done:	 Predicting
-2016-09-06 10:31:22,856 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:22,857 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:22,857 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,858 INFO: Done:	 Result Analysis
-2016-09-06 10:31:22,994 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,994 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,994 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:31:22,994 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:31:22,994 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:22,994 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:22,996 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,996 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,996 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,996 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,996 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:22,996 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:22,996 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,996 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,070 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:23,070 DEBUG: Start:	 Training
-2016-09-06 10:31:23,077 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:23,077 DEBUG: Start:	 Training
-2016-09-06 10:31:23,093 DEBUG: Info:	 Time for Training: 0.10053896904[s]
-2016-09-06 10:31:23,094 DEBUG: Done:	 Training
-2016-09-06 10:31:23,094 DEBUG: Start:	 Predicting
-2016-09-06 10:31:23,101 DEBUG: Done:	 Predicting
-2016-09-06 10:31:23,101 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:23,102 DEBUG: Info:	 Time for Training: 0.108783006668[s]
-2016-09-06 10:31:23,102 DEBUG: Done:	 Training
-2016-09-06 10:31:23,102 DEBUG: Start:	 Predicting
-2016-09-06 10:31:23,103 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:23,104 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,104 INFO: Done:	 Result Analysis
-2016-09-06 10:31:23,107 DEBUG: Done:	 Predicting
-2016-09-06 10:31:23,107 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:23,108 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:23,108 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,108 INFO: Done:	 Result Analysis
-2016-09-06 10:31:23,242 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,243 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:31:23,243 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:23,243 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,243 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:31:23,244 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:23,244 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,244 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,244 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:23,244 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,245 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,245 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,245 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:23,245 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,282 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:23,282 DEBUG: Start:	 Training
-2016-09-06 10:31:23,283 DEBUG: Info:	 Time for Training: 0.0416178703308[s]
-2016-09-06 10:31:23,283 DEBUG: Done:	 Training
-2016-09-06 10:31:23,284 DEBUG: Start:	 Predicting
-2016-09-06 10:31:23,286 DEBUG: Done:	 Predicting
-2016-09-06 10:31:23,286 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:23,288 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:23,288 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.785714285714
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.785714285714
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,288 INFO: Done:	 Result Analysis
-2016-09-06 10:31:23,292 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:23,292 DEBUG: Start:	 Training
-2016-09-06 10:31:23,296 DEBUG: Info:	 Time for Training: 0.0540850162506[s]
-2016-09-06 10:31:23,296 DEBUG: Done:	 Training
-2016-09-06 10:31:23,296 DEBUG: Start:	 Predicting
-2016-09-06 10:31:23,299 DEBUG: Done:	 Predicting
-2016-09-06 10:31:23,299 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:23,301 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:23,301 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,301 INFO: Done:	 Result Analysis
-2016-09-06 10:31:23,391 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,391 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,391 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:31:23,391 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:31:23,391 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:23,391 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:23,392 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,392 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,392 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,392 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,393 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:23,393 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:23,393 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,393 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,430 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:23,430 DEBUG: Start:	 Training
-2016-09-06 10:31:23,431 DEBUG: Info:	 Time for Training: 0.0408799648285[s]
-2016-09-06 10:31:23,431 DEBUG: Done:	 Training
-2016-09-06 10:31:23,431 DEBUG: Start:	 Predicting
-2016-09-06 10:31:23,439 DEBUG: Done:	 Predicting
-2016-09-06 10:31:23,439 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:23,440 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:23,441 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,441 INFO: Done:	 Result Analysis
-2016-09-06 10:31:23,693 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:23,693 DEBUG: Start:	 Training
-2016-09-06 10:31:23,736 DEBUG: Info:	 Time for Training: 0.345639944077[s]
-2016-09-06 10:31:23,736 DEBUG: Done:	 Training
-2016-09-06 10:31:23,736 DEBUG: Start:	 Predicting
-2016-09-06 10:31:23,742 DEBUG: Done:	 Predicting
-2016-09-06 10:31:23,742 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:23,743 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:23,743 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.961904761905
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 17, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.961904761905
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,743 INFO: Done:	 Result Analysis
-2016-09-06 10:31:23,843 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,843 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,844 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:31:23,844 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:31:23,844 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:23,844 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:23,845 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,845 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,845 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,845 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,845 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:23,845 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:23,845 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,845 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,914 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:23,914 DEBUG: Start:	 Training
-2016-09-06 10:31:23,915 DEBUG: Info:	 Time for Training: 0.0723230838776[s]
-2016-09-06 10:31:23,915 DEBUG: Done:	 Training
-2016-09-06 10:31:23,915 DEBUG: Start:	 Predicting
-2016-09-06 10:31:23,917 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:23,917 DEBUG: Start:	 Training
-2016-09-06 10:31:23,927 DEBUG: Done:	 Predicting
-2016-09-06 10:31:23,927 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:23,929 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:23,929 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,930 INFO: Done:	 Result Analysis
-2016-09-06 10:31:23,939 DEBUG: Info:	 Time for Training: 0.0961401462555[s]
-2016-09-06 10:31:23,939 DEBUG: Done:	 Training
-2016-09-06 10:31:23,939 DEBUG: Start:	 Predicting
-2016-09-06 10:31:23,942 DEBUG: Done:	 Predicting
-2016-09-06 10:31:23,942 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:23,943 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:23,943 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,943 INFO: Done:	 Result Analysis
-2016-09-06 10:31:24,085 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,085 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,085 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:31:24,085 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:31:24,085 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:24,085 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:24,086 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:24,086 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:24,086 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:24,086 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:24,086 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:24,086 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:24,086 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,086 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,144 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:24,144 DEBUG: Start:	 Training
-2016-09-06 10:31:24,149 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:24,149 DEBUG: Start:	 Training
-2016-09-06 10:31:24,161 DEBUG: Info:	 Time for Training: 0.0771129131317[s]
-2016-09-06 10:31:24,161 DEBUG: Done:	 Training
-2016-09-06 10:31:24,161 DEBUG: Start:	 Predicting
-2016-09-06 10:31:24,164 DEBUG: Done:	 Predicting
-2016-09-06 10:31:24,165 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:24,166 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:24,166 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:31:24,166 INFO: Done:	 Result Analysis
-2016-09-06 10:31:24,172 DEBUG: Info:	 Time for Training: 0.0878200531006[s]
-2016-09-06 10:31:24,172 DEBUG: Done:	 Training
-2016-09-06 10:31:24,172 DEBUG: Start:	 Predicting
-2016-09-06 10:31:24,178 DEBUG: Done:	 Predicting
-2016-09-06 10:31:24,178 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:24,179 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:24,179 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:31:24,180 INFO: Done:	 Result Analysis
-2016-09-06 10:31:24,332 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,332 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:31:24,332 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:24,333 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,333 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:24,333 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:31:24,333 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:24,333 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:24,333 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:24,333 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,334 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:24,334 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:24,334 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:24,334 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,384 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:24,384 DEBUG: Start:	 Training
-2016-09-06 10:31:24,386 DEBUG: Info:	 Time for Training: 0.0551080703735[s]
-2016-09-06 10:31:24,386 DEBUG: Done:	 Training
-2016-09-06 10:31:24,387 DEBUG: Start:	 Predicting
-2016-09-06 10:31:24,389 DEBUG: Done:	 Predicting
-2016-09-06 10:31:24,389 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:24,391 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:24,391 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.919047619048
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:31:24,392 INFO: Done:	 Result Analysis
-2016-09-06 10:31:24,418 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:24,418 DEBUG: Start:	 Training
-2016-09-06 10:31:24,423 DEBUG: Info:	 Time for Training: 0.0905458927155[s]
-2016-09-06 10:31:24,423 DEBUG: Done:	 Training
-2016-09-06 10:31:24,423 DEBUG: Start:	 Predicting
-2016-09-06 10:31:24,427 DEBUG: Done:	 Predicting
-2016-09-06 10:31:24,428 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:24,430 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:24,430 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:31:24,431 INFO: Done:	 Result Analysis
-2016-09-06 10:31:24,595 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,595 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:31:24,596 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:24,596 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:24,597 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:24,597 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:24,597 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,600 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,601 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:31:24,601 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:24,602 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:24,602 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:24,602 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:24,602 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,652 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:24,652 DEBUG: Start:	 Training
-2016-09-06 10:31:24,653 DEBUG: Info:	 Time for Training: 0.0590059757233[s]
-2016-09-06 10:31:24,653 DEBUG: Done:	 Training
-2016-09-06 10:31:24,653 DEBUG: Start:	 Predicting
-2016-09-06 10:31:24,664 DEBUG: Done:	 Predicting
-2016-09-06 10:31:24,665 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:24,667 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:24,667 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:31:24,667 INFO: Done:	 Result Analysis
-2016-09-06 10:31:24,960 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:24,960 DEBUG: Start:	 Training
-2016-09-06 10:31:25,007 DEBUG: Info:	 Time for Training: 0.407965898514[s]
-2016-09-06 10:31:25,008 DEBUG: Done:	 Training
-2016-09-06 10:31:25,008 DEBUG: Start:	 Predicting
-2016-09-06 10:31:25,014 DEBUG: Done:	 Predicting
-2016-09-06 10:31:25,014 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:25,015 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:25,016 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 17, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:31:25,016 INFO: Done:	 Result Analysis
-2016-09-06 10:31:25,149 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:25,149 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:25,149 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:31:25,149 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:31:25,149 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:25,149 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:31:25,150 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:25,150 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:25,150 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:25,150 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:25,151 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:25,151 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:31:25,151 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:25,151 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:31:25,220 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:25,220 DEBUG: Start:	 Training
-2016-09-06 10:31:25,221 DEBUG: Info:	 Time for Training: 0.0730149745941[s]
-2016-09-06 10:31:25,222 DEBUG: Done:	 Training
-2016-09-06 10:31:25,222 DEBUG: Start:	 Predicting
-2016-09-06 10:31:25,226 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:31:25,226 DEBUG: Start:	 Training
-2016-09-06 10:31:25,234 DEBUG: Done:	 Predicting
-2016-09-06 10:31:25,235 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:25,236 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:25,236 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:25,237 INFO: Done:	 Result Analysis
-2016-09-06 10:31:25,251 DEBUG: Info:	 Time for Training: 0.103055000305[s]
-2016-09-06 10:31:25,252 DEBUG: Done:	 Training
-2016-09-06 10:31:25,253 DEBUG: Start:	 Predicting
-2016-09-06 10:31:25,259 DEBUG: Done:	 Predicting
-2016-09-06 10:31:25,259 DEBUG: Start:	 Getting Results
-2016-09-06 10:31:25,261 DEBUG: Done:	 Getting Results
-2016-09-06 10:31:25,261 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:31:25,262 INFO: Done:	 Result Analysis
-2016-09-06 10:31:25,546 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:31:25,546 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:31:25,547 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 10:31:25,547 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:31:25,547 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:31:25,547 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 10:31:25,548 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-06 10:31:25,548 INFO: Info:	 Shape of View2 :(300, 5)
-2016-09-06 10:31:25,548 INFO: Info:	 Shape of View1 :(300, 10)
-2016-09-06 10:31:25,548 INFO: Info:	 Shape of View3 :(300, 13)
-2016-09-06 10:31:25,548 INFO: Done:	 Read Database Files
-2016-09-06 10:31:25,549 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:31:25,549 INFO: Info:	 Shape of View2 :(300, 5)
-2016-09-06 10:31:25,549 INFO: Info:	 Shape of View3 :(300, 13)
-2016-09-06 10:31:25,549 INFO: Done:	 Read Database Files
-2016-09-06 10:31:25,550 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:31:25,553 INFO: Done:	 Determine validation split
-2016-09-06 10:31:25,553 INFO: Start:	 Determine 5 folds
-2016-09-06 10:31:25,554 INFO: Done:	 Determine validation split
-2016-09-06 10:31:25,554 INFO: Start:	 Determine 5 folds
-2016-09-06 10:31:25,561 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:31:25,561 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:31:25,561 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:31:25,561 INFO: Done:	 Determine folds
-2016-09-06 10:31:25,561 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:31:25,561 INFO: Start:	 Classification
-2016-09-06 10:31:25,561 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:31:25,561 INFO: 	Start:	 Fold number 1
-2016-09-06 10:31:25,561 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:31:25,561 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:31:25,562 INFO: Done:	 Determine folds
-2016-09-06 10:31:25,562 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:31:25,562 INFO: Start:	 Classification
-2016-09-06 10:31:25,562 INFO: 	Start:	 Fold number 1
-2016-09-06 10:31:25,604 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:31:25,618 DEBUG: 			View 0 : 0.538461538462
-2016-09-06 10:31:25,626 DEBUG: 			View 1 : 0.550295857988
-2016-09-06 10:31:25,634 DEBUG: 			View 2 : 0.520710059172
-2016-09-06 10:31:25,642 DEBUG: 			View 3 : 0.508875739645
-2016-09-06 10:31:25,674 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:25,752 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:31:25,760 DEBUG: 			View 0 : 0.715976331361
-2016-09-06 10:31:25,767 DEBUG: 			View 1 : 0.715976331361
-2016-09-06 10:31:25,774 DEBUG: 			View 2 : 0.704142011834
-2016-09-06 10:31:25,782 DEBUG: 			View 3 : 0.751479289941
-2016-09-06 10:31:25,819 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:25,964 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:31:25,980 DEBUG: 			View 0 : 0.715976331361
-2016-09-06 10:31:25,987 DEBUG: 			View 1 : 0.715976331361
-2016-09-06 10:31:25,994 DEBUG: 			View 2 : 0.704142011834
-2016-09-06 10:31:26,001 DEBUG: 			View 3 : 0.751479289941
-2016-09-06 10:31:26,040 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:26,244 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:31:26,251 DEBUG: 			View 0 : 0.692307692308
-2016-09-06 10:31:26,258 DEBUG: 			View 1 : 0.573964497041
-2016-09-06 10:31:26,265 DEBUG: 			View 2 : 0.686390532544
-2016-09-06 10:31:26,272 DEBUG: 			View 3 : 0.633136094675
-2016-09-06 10:31:26,313 DEBUG: 			 Best view : 		View2
-2016-09-06 10:31:26,583 INFO: 	Start: 	 Classification
-2016-09-06 10:31:27,038 INFO: 	Done: 	 Fold number 1
-2016-09-06 10:31:27,038 INFO: 	Start:	 Fold number 2
-2016-09-06 10:31:27,068 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:31:27,075 DEBUG: 			View 0 : 0.514792899408
-2016-09-06 10:31:27,082 DEBUG: 			View 1 : 0.538461538462
-2016-09-06 10:31:27,089 DEBUG: 			View 2 : 0.473372781065
-2016-09-06 10:31:27,096 DEBUG: 			View 3 : 0.497041420118
-2016-09-06 10:31:27,127 DEBUG: 			 Best view : 		View1
-2016-09-06 10:31:27,203 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:31:27,210 DEBUG: 			View 0 : 0.674556213018
-2016-09-06 10:31:27,217 DEBUG: 			View 1 : 0.727810650888
-2016-09-06 10:31:27,224 DEBUG: 			View 2 : 0.644970414201
-2016-09-06 10:31:27,232 DEBUG: 			View 3 : 0.686390532544
-2016-09-06 10:31:27,268 DEBUG: 			 Best view : 		View1
-2016-09-06 10:31:27,409 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:31:27,416 DEBUG: 			View 0 : 0.674556213018
-2016-09-06 10:31:27,423 DEBUG: 			View 1 : 0.727810650888
-2016-09-06 10:31:27,430 DEBUG: 			View 2 : 0.644970414201
-2016-09-06 10:31:27,437 DEBUG: 			View 3 : 0.686390532544
-2016-09-06 10:31:27,478 DEBUG: 			 Best view : 		View1
-2016-09-06 10:31:27,682 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:31:27,689 DEBUG: 			View 0 : 0.644970414201
-2016-09-06 10:31:27,696 DEBUG: 			View 1 : 0.692307692308
-2016-09-06 10:31:27,703 DEBUG: 			View 2 : 0.650887573964
-2016-09-06 10:31:27,711 DEBUG: 			View 3 : 0.698224852071
-2016-09-06 10:31:27,752 DEBUG: 			 Best view : 		View1
-2016-09-06 10:31:28,021 INFO: 	Start: 	 Classification
-2016-09-06 10:31:28,472 INFO: 	Done: 	 Fold number 2
-2016-09-06 10:31:28,472 INFO: 	Start:	 Fold number 3
-2016-09-06 10:31:28,501 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:31:28,508 DEBUG: 			View 0 : 0.497041420118
-2016-09-06 10:31:28,514 DEBUG: 			View 1 : 0.485207100592
-2016-09-06 10:31:28,521 DEBUG: 			View 2 : 0.491124260355
-2016-09-06 10:31:28,528 DEBUG: 			View 3 : 0.568047337278
-2016-09-06 10:31:28,558 DEBUG: 			 Best view : 		View0
-2016-09-06 10:31:28,633 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:31:28,640 DEBUG: 			View 0 : 0.674556213018
-2016-09-06 10:31:28,647 DEBUG: 			View 1 : 0.597633136095
-2016-09-06 10:31:28,654 DEBUG: 			View 2 : 0.692307692308
-2016-09-06 10:31:28,661 DEBUG: 			View 3 : 0.751479289941
-2016-09-06 10:31:28,697 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:28,836 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:31:28,844 DEBUG: 			View 0 : 0.674556213018
-2016-09-06 10:31:28,851 DEBUG: 			View 1 : 0.597633136095
-2016-09-06 10:31:28,857 DEBUG: 			View 2 : 0.692307692308
-2016-09-06 10:31:28,865 DEBUG: 			View 3 : 0.751479289941
-2016-09-06 10:31:28,903 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:29,107 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:31:29,114 DEBUG: 			View 0 : 0.656804733728
-2016-09-06 10:31:29,121 DEBUG: 			View 1 : 0.639053254438
-2016-09-06 10:31:29,128 DEBUG: 			View 2 : 0.674556213018
-2016-09-06 10:31:29,136 DEBUG: 			View 3 : 0.792899408284
-2016-09-06 10:31:29,177 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:29,444 INFO: 	Start: 	 Classification
-2016-09-06 10:31:29,896 INFO: 	Done: 	 Fold number 3
-2016-09-06 10:31:29,896 INFO: 	Start:	 Fold number 4
-2016-09-06 10:31:29,924 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:31:29,931 DEBUG: 			View 0 : 0.479289940828
-2016-09-06 10:31:29,938 DEBUG: 			View 1 : 0.479289940828
-2016-09-06 10:31:29,945 DEBUG: 			View 2 : 0.479289940828
-2016-09-06 10:31:29,951 DEBUG: 			View 3 : 0.479289940828
-2016-09-06 10:31:29,951 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 10:31:29,982 DEBUG: 			 Best view : 		View0
-2016-09-06 10:31:30,058 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:31:30,065 DEBUG: 			View 0 : 0.627218934911
-2016-09-06 10:31:30,072 DEBUG: 			View 1 : 0.686390532544
-2016-09-06 10:31:30,080 DEBUG: 			View 2 : 0.686390532544
-2016-09-06 10:31:30,087 DEBUG: 			View 3 : 0.751479289941
-2016-09-06 10:31:30,123 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:30,265 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:31:30,272 DEBUG: 			View 0 : 0.627218934911
-2016-09-06 10:31:30,280 DEBUG: 			View 1 : 0.686390532544
-2016-09-06 10:31:30,287 DEBUG: 			View 2 : 0.686390532544
-2016-09-06 10:31:30,294 DEBUG: 			View 3 : 0.751479289941
-2016-09-06 10:31:30,334 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:30,537 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:31:30,544 DEBUG: 			View 0 : 0.615384615385
-2016-09-06 10:31:30,551 DEBUG: 			View 1 : 0.603550295858
-2016-09-06 10:31:30,559 DEBUG: 			View 2 : 0.644970414201
-2016-09-06 10:31:30,566 DEBUG: 			View 3 : 0.662721893491
-2016-09-06 10:31:30,607 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:30,876 INFO: 	Start: 	 Classification
-2016-09-06 10:31:31,332 INFO: 	Done: 	 Fold number 4
-2016-09-06 10:31:31,332 INFO: 	Start:	 Fold number 5
-2016-09-06 10:31:31,361 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:31:31,368 DEBUG: 			View 0 : 0.491124260355
-2016-09-06 10:31:31,375 DEBUG: 			View 1 : 0.479289940828
-2016-09-06 10:31:31,382 DEBUG: 			View 2 : 0.479289940828
-2016-09-06 10:31:31,388 DEBUG: 			View 3 : 0.526627218935
-2016-09-06 10:31:31,418 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:31,493 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:31:31,501 DEBUG: 			View 0 : 0.668639053254
-2016-09-06 10:31:31,508 DEBUG: 			View 1 : 0.656804733728
-2016-09-06 10:31:31,515 DEBUG: 			View 2 : 0.656804733728
-2016-09-06 10:31:31,523 DEBUG: 			View 3 : 0.715976331361
-2016-09-06 10:31:31,559 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:31,698 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:31:31,705 DEBUG: 			View 0 : 0.668639053254
-2016-09-06 10:31:31,712 DEBUG: 			View 1 : 0.656804733728
-2016-09-06 10:31:31,719 DEBUG: 			View 2 : 0.656804733728
-2016-09-06 10:31:31,727 DEBUG: 			View 3 : 0.715976331361
-2016-09-06 10:31:31,765 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:31,969 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:31:31,977 DEBUG: 			View 0 : 0.633136094675
-2016-09-06 10:31:31,984 DEBUG: 			View 1 : 0.556213017751
-2016-09-06 10:31:31,991 DEBUG: 			View 2 : 0.568047337278
-2016-09-06 10:31:31,998 DEBUG: 			View 3 : 0.639053254438
-2016-09-06 10:31:32,039 DEBUG: 			 Best view : 		View3
-2016-09-06 10:31:32,311 INFO: 	Start: 	 Classification
-2016-09-06 10:31:32,766 INFO: 	Done: 	 Fold number 5
-2016-09-06 10:31:32,767 INFO: Done:	 Classification
-2016-09-06 10:31:32,767 INFO: Info:	 Time for Classification: 7[s]
-2016-09-06 10:31:32,767 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 10:31:35,218 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 73.9644970414
-	-On Test : 48.5714285714
-	-On Validation : 51.6853932584Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 8), View1 of shape (300, 10), View2 of shape (300, 5), View3 of shape (300, 13)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:06        0:00:00
-	          Total        0:00:19        0:00:02
-	So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.266272189349
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.255621301775
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.261538461538
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.26449704142
-			- Percentage of time chosen : 0.3
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.250887573964
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.268639053254
-			- Percentage of time chosen : 0.4
-		- On View2 : 
-			- Mean average Accuracy : 0.241420118343
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.256804733728
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.250295857988
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.231952662722
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.255029585799
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.286390532544
-			- Percentage of time chosen : 0.3
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.234911242604
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.245562130178
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.249704142012
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.26449704142
-			- Percentage of time chosen : 0.3
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.246153846154
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.234911242604
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.236094674556
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.259763313609
-			- Percentage of time chosen : 0.4
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View3
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 72.7810650888
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.9213483146
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 71.5976331361
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 72.7810650888
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.9213483146
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 71.5976331361
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 72.7810650888
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.9213483146
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 71.5976331361
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-2016-09-06 10:31:35,405 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 47001d62..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 03adc80f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.938095238095
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.938095238095
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c7e9d432..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 461f44c9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 17, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 207b9a7e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ba87e538..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3c15e799..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8f4eb2ff..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a51fc411..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a89da4e8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.838095238095
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.838095238095
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f62702a6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2ca48124..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 17, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5dfa38e3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7a807ce4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c696ff7a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 12a61d1e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.785714285714
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.785714285714
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7afc9baf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8b74e159..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.961904761905
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 17, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.961904761905
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5368fdbf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 66e58177..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4959e4f4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e1f9847a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 474fe79a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e82c8d88..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.919047619048
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bb1b14bb..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b9e72cea..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c25b7951..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 06e42109..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 17, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ec5c4978..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 39374ee4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2481
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index 129780daaf421ae71fa74885fd4baaf13677ad38..0000000000000000000000000000000000000000
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 1d66943e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,235 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 73.9644970414
-	-On Test : 48.5714285714
-	-On Validation : 51.6853932584Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 8), View1 of shape (300, 10), View2 of shape (300, 5), View3 of shape (300, 13)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:05        0:00:00
-	         Fold 5        0:00:06        0:00:00
-	          Total        0:00:19        0:00:02
-	So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.266272189349
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.255621301775
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.261538461538
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.26449704142
-			- Percentage of time chosen : 0.3
-	- Fold 1, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.250887573964
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.268639053254
-			- Percentage of time chosen : 0.4
-		- On View2 : 
-			- Mean average Accuracy : 0.241420118343
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.256804733728
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.250295857988
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.231952662722
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.255029585799
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.286390532544
-			- Percentage of time chosen : 0.3
-	- Fold 3, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.234911242604
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.245562130178
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.249704142012
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.26449704142
-			- Percentage of time chosen : 0.3
-	- Fold 4, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.246153846154
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.234911242604
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.236094674556
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.259763313609
-			- Percentage of time chosen : 0.4
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View3
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 72.7810650888
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.9213483146
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 71.5976331361
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 72.7810650888
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.9213483146
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 71.5976331361
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 72.7810650888
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 62.9213483146
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 75.1479289941
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 71.5976331361
-			Accuracy on test : 0.0
-			Accuracy on validation : 49.4382022472
-			Selected View : View3
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 47.9289940828
-			Accuracy on test : 0.0
-			Accuracy on validation : 47.191011236
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index df4e9476..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1580 +0,0 @@
-2016-09-06 10:34:09,127 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:34:09,127 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.0001380625 Gbytes /!\ 
-2016-09-06 10:34:14,141 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:34:14,144 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:34:14,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:34:14,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:34:14,197 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:14,197 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:14,197 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,197 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,197 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,197 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,198 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:14,198 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:14,198 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,198 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,227 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:14,227 DEBUG: Start:	 Training
-2016-09-06 10:34:14,228 DEBUG: Info:	 Time for Training: 0.0323169231415[s]
-2016-09-06 10:34:14,228 DEBUG: Done:	 Training
-2016-09-06 10:34:14,228 DEBUG: Start:	 Predicting
-2016-09-06 10:34:14,231 DEBUG: Done:	 Predicting
-2016-09-06 10:34:14,231 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:14,232 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:14,233 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.652380952381
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,233 INFO: Done:	 Result Analysis
-2016-09-06 10:34:14,245 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:14,245 DEBUG: Start:	 Training
-2016-09-06 10:34:14,249 DEBUG: Info:	 Time for Training: 0.0530989170074[s]
-2016-09-06 10:34:14,249 DEBUG: Done:	 Training
-2016-09-06 10:34:14,249 DEBUG: Start:	 Predicting
-2016-09-06 10:34:14,252 DEBUG: Done:	 Predicting
-2016-09-06 10:34:14,252 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:14,254 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:14,254 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,254 INFO: Done:	 Result Analysis
-2016-09-06 10:34:14,344 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,344 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,344 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:34:14,344 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:34:14,345 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:14,345 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:14,345 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,345 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,345 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,345 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,345 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:14,345 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:14,345 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,345 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,378 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:14,379 DEBUG: Start:	 Training
-2016-09-06 10:34:14,379 DEBUG: Info:	 Time for Training: 0.0353238582611[s]
-2016-09-06 10:34:14,379 DEBUG: Done:	 Training
-2016-09-06 10:34:14,379 DEBUG: Start:	 Predicting
-2016-09-06 10:34:14,386 DEBUG: Done:	 Predicting
-2016-09-06 10:34:14,386 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:14,387 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:14,387 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 34
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,388 INFO: Done:	 Result Analysis
-2016-09-06 10:34:14,487 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:14,487 DEBUG: Start:	 Training
-2016-09-06 10:34:14,505 DEBUG: Info:	 Time for Training: 0.160764932632[s]
-2016-09-06 10:34:14,505 DEBUG: Done:	 Training
-2016-09-06 10:34:14,505 DEBUG: Start:	 Predicting
-2016-09-06 10:34:14,509 DEBUG: Done:	 Predicting
-2016-09-06 10:34:14,509 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:14,510 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:14,510 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,510 INFO: Done:	 Result Analysis
-2016-09-06 10:34:14,599 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,599 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,599 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:34:14,599 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:34:14,599 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:14,599 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:14,600 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,600 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,600 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,600 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,601 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:14,601 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:14,601 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,601 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,648 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:14,648 DEBUG: Start:	 Training
-2016-09-06 10:34:14,649 DEBUG: Info:	 Time for Training: 0.0514440536499[s]
-2016-09-06 10:34:14,649 DEBUG: Done:	 Training
-2016-09-06 10:34:14,649 DEBUG: Start:	 Predicting
-2016-09-06 10:34:14,651 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:14,651 DEBUG: Start:	 Training
-2016-09-06 10:34:14,653 DEBUG: Done:	 Predicting
-2016-09-06 10:34:14,654 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:14,655 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:14,655 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,656 INFO: Done:	 Result Analysis
-2016-09-06 10:34:14,670 DEBUG: Info:	 Time for Training: 0.0719361305237[s]
-2016-09-06 10:34:14,670 DEBUG: Done:	 Training
-2016-09-06 10:34:14,670 DEBUG: Start:	 Predicting
-2016-09-06 10:34:14,673 DEBUG: Done:	 Predicting
-2016-09-06 10:34:14,673 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:14,675 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:14,675 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.471428571429
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,675 INFO: Done:	 Result Analysis
-2016-09-06 10:34:14,747 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,748 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:34:14,748 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:14,748 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,748 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,749 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,749 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:34:14,749 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:14,749 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:14,749 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,750 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,750 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,750 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:14,750 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,803 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:14,803 DEBUG: Start:	 Training
-2016-09-06 10:34:14,803 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:14,804 DEBUG: Start:	 Training
-2016-09-06 10:34:14,821 DEBUG: Info:	 Time for Training: 0.0726821422577[s]
-2016-09-06 10:34:14,821 DEBUG: Done:	 Training
-2016-09-06 10:34:14,821 DEBUG: Start:	 Predicting
-2016-09-06 10:34:14,821 DEBUG: Info:	 Time for Training: 0.0746259689331[s]
-2016-09-06 10:34:14,822 DEBUG: Done:	 Training
-2016-09-06 10:34:14,822 DEBUG: Start:	 Predicting
-2016-09-06 10:34:14,825 DEBUG: Done:	 Predicting
-2016-09-06 10:34:14,825 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:14,826 DEBUG: Done:	 Predicting
-2016-09-06 10:34:14,826 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:14,826 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:14,827 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,827 INFO: Done:	 Result Analysis
-2016-09-06 10:34:14,827 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:14,827 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,828 INFO: Done:	 Result Analysis
-2016-09-06 10:34:14,892 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,893 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:34:14,893 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,893 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:14,893 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:34:14,893 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:14,894 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:14,894 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:14,894 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:14,894 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:14,894 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:14,894 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,894 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:14,895 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,927 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:14,927 DEBUG: Start:	 Training
-2016-09-06 10:34:14,928 DEBUG: Info:	 Time for Training: 0.036386013031[s]
-2016-09-06 10:34:14,928 DEBUG: Done:	 Training
-2016-09-06 10:34:14,928 DEBUG: Start:	 Predicting
-2016-09-06 10:34:14,931 DEBUG: Done:	 Predicting
-2016-09-06 10:34:14,932 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:14,933 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:14,933 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,933 INFO: Done:	 Result Analysis
-2016-09-06 10:34:14,948 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:14,948 DEBUG: Start:	 Training
-2016-09-06 10:34:14,952 DEBUG: Info:	 Time for Training: 0.0607821941376[s]
-2016-09-06 10:34:14,953 DEBUG: Done:	 Training
-2016-09-06 10:34:14,953 DEBUG: Start:	 Predicting
-2016-09-06 10:34:14,955 DEBUG: Done:	 Predicting
-2016-09-06 10:34:14,956 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:14,957 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:14,957 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,958 INFO: Done:	 Result Analysis
-2016-09-06 10:34:15,039 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,039 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,039 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:34:15,039 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:34:15,040 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:15,040 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:15,040 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,040 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,040 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,040 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,040 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:15,040 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:15,040 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,040 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,073 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:15,073 DEBUG: Start:	 Training
-2016-09-06 10:34:15,074 DEBUG: Info:	 Time for Training: 0.0351400375366[s]
-2016-09-06 10:34:15,074 DEBUG: Done:	 Training
-2016-09-06 10:34:15,074 DEBUG: Start:	 Predicting
-2016-09-06 10:34:15,081 DEBUG: Done:	 Predicting
-2016-09-06 10:34:15,081 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:15,083 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:15,083 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 34
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,083 INFO: Done:	 Result Analysis
-2016-09-06 10:34:15,182 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:15,182 DEBUG: Start:	 Training
-2016-09-06 10:34:15,200 DEBUG: Info:	 Time for Training: 0.161078929901[s]
-2016-09-06 10:34:15,200 DEBUG: Done:	 Training
-2016-09-06 10:34:15,200 DEBUG: Start:	 Predicting
-2016-09-06 10:34:15,204 DEBUG: Done:	 Predicting
-2016-09-06 10:34:15,204 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:15,205 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:15,205 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.685714285714
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.685714285714
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,205 INFO: Done:	 Result Analysis
-2016-09-06 10:34:15,291 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,291 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:34:15,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:34:15,292 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:15,292 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:15,293 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,293 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,294 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,294 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,294 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:15,294 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:15,294 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,294 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,362 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:15,362 DEBUG: Start:	 Training
-2016-09-06 10:34:15,363 DEBUG: Info:	 Time for Training: 0.07275390625[s]
-2016-09-06 10:34:15,363 DEBUG: Done:	 Training
-2016-09-06 10:34:15,363 DEBUG: Start:	 Predicting
-2016-09-06 10:34:15,369 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:15,369 DEBUG: Start:	 Training
-2016-09-06 10:34:15,378 DEBUG: Done:	 Predicting
-2016-09-06 10:34:15,378 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:15,380 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:15,380 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.633333333333
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.633333333333
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,381 INFO: Done:	 Result Analysis
-2016-09-06 10:34:15,396 DEBUG: Info:	 Time for Training: 0.105423927307[s]
-2016-09-06 10:34:15,396 DEBUG: Done:	 Training
-2016-09-06 10:34:15,396 DEBUG: Start:	 Predicting
-2016-09-06 10:34:15,399 DEBUG: Done:	 Predicting
-2016-09-06 10:34:15,399 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:15,401 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:15,401 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.388888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.388888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,401 INFO: Done:	 Result Analysis
-2016-09-06 10:34:15,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:34:15,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:34:15,536 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:15,536 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:15,537 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,537 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,537 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,537 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,537 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:15,537 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:15,537 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,537 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,607 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:15,607 DEBUG: Start:	 Training
-2016-09-06 10:34:15,616 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:15,616 DEBUG: Start:	 Training
-2016-09-06 10:34:15,632 DEBUG: Info:	 Time for Training: 0.0972080230713[s]
-2016-09-06 10:34:15,632 DEBUG: Done:	 Training
-2016-09-06 10:34:15,632 DEBUG: Start:	 Predicting
-2016-09-06 10:34:15,641 DEBUG: Done:	 Predicting
-2016-09-06 10:34:15,641 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:15,643 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:15,643 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,643 DEBUG: Info:	 Time for Training: 0.10800909996[s]
-2016-09-06 10:34:15,643 DEBUG: Done:	 Training
-2016-09-06 10:34:15,643 INFO: Done:	 Result Analysis
-2016-09-06 10:34:15,643 DEBUG: Start:	 Predicting
-2016-09-06 10:34:15,647 DEBUG: Done:	 Predicting
-2016-09-06 10:34:15,648 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:15,649 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:15,649 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,649 INFO: Done:	 Result Analysis
-2016-09-06 10:34:15,791 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,791 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,792 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:34:15,792 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:34:15,792 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:15,792 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:15,793 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:15,793 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:15,793 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:15,793 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:15,793 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:15,793 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:15,793 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,793 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,840 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:15,840 DEBUG: Start:	 Training
-2016-09-06 10:34:15,841 DEBUG: Info:	 Time for Training: 0.0506250858307[s]
-2016-09-06 10:34:15,842 DEBUG: Done:	 Training
-2016-09-06 10:34:15,842 DEBUG: Start:	 Predicting
-2016-09-06 10:34:15,845 DEBUG: Done:	 Predicting
-2016-09-06 10:34:15,846 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:15,848 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:15,848 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,848 INFO: Done:	 Result Analysis
-2016-09-06 10:34:15,867 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:15,867 DEBUG: Start:	 Training
-2016-09-06 10:34:15,873 DEBUG: Info:	 Time for Training: 0.081845998764[s]
-2016-09-06 10:34:15,873 DEBUG: Done:	 Training
-2016-09-06 10:34:15,873 DEBUG: Start:	 Predicting
-2016-09-06 10:34:15,876 DEBUG: Done:	 Predicting
-2016-09-06 10:34:15,876 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:15,877 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:15,878 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,878 INFO: Done:	 Result Analysis
-2016-09-06 10:34:15,935 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,935 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,936 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:34:15,936 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:34:15,936 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:15,936 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:15,937 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:15,937 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:15,937 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:15,937 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:15,937 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:15,937 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:15,937 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,937 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,986 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:15,986 DEBUG: Start:	 Training
-2016-09-06 10:34:15,987 DEBUG: Info:	 Time for Training: 0.0520849227905[s]
-2016-09-06 10:34:15,987 DEBUG: Done:	 Training
-2016-09-06 10:34:15,987 DEBUG: Start:	 Predicting
-2016-09-06 10:34:15,997 DEBUG: Done:	 Predicting
-2016-09-06 10:34:15,997 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:16,000 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:16,000 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 34
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,000 INFO: Done:	 Result Analysis
-2016-09-06 10:34:16,100 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:16,100 DEBUG: Start:	 Training
-2016-09-06 10:34:16,117 DEBUG: Info:	 Time for Training: 0.182558059692[s]
-2016-09-06 10:34:16,117 DEBUG: Done:	 Training
-2016-09-06 10:34:16,117 DEBUG: Start:	 Predicting
-2016-09-06 10:34:16,121 DEBUG: Done:	 Predicting
-2016-09-06 10:34:16,121 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:16,123 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:16,123 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.680952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.680952380952
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,123 INFO: Done:	 Result Analysis
-2016-09-06 10:34:16,184 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,184 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,185 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:34:16,185 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:34:16,185 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:16,185 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:16,185 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:16,185 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:16,185 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:16,186 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:16,186 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:16,186 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:16,186 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,186 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,231 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:16,232 DEBUG: Start:	 Training
-2016-09-06 10:34:16,232 DEBUG: Info:	 Time for Training: 0.0485727787018[s]
-2016-09-06 10:34:16,232 DEBUG: Done:	 Training
-2016-09-06 10:34:16,233 DEBUG: Start:	 Predicting
-2016-09-06 10:34:16,239 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:16,239 DEBUG: Start:	 Training
-2016-09-06 10:34:16,248 DEBUG: Done:	 Predicting
-2016-09-06 10:34:16,248 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:16,250 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:16,250 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,250 INFO: Done:	 Result Analysis
-2016-09-06 10:34:16,262 DEBUG: Info:	 Time for Training: 0.0779819488525[s]
-2016-09-06 10:34:16,262 DEBUG: Done:	 Training
-2016-09-06 10:34:16,262 DEBUG: Start:	 Predicting
-2016-09-06 10:34:16,265 DEBUG: Done:	 Predicting
-2016-09-06 10:34:16,266 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:16,267 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:16,267 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,267 INFO: Done:	 Result Analysis
-2016-09-06 10:34:16,341 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,341 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,342 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:34:16,342 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:34:16,342 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:16,342 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:16,343 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:16,343 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:16,343 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:16,343 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:16,343 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:16,343 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:16,344 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,344 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,409 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:16,410 DEBUG: Start:	 Training
-2016-09-06 10:34:16,413 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:16,413 DEBUG: Start:	 Training
-2016-09-06 10:34:16,431 DEBUG: Info:	 Time for Training: 0.0899150371552[s]
-2016-09-06 10:34:16,431 DEBUG: Done:	 Training
-2016-09-06 10:34:16,431 DEBUG: Start:	 Predicting
-2016-09-06 10:34:16,431 DEBUG: Info:	 Time for Training: 0.0902900695801[s]
-2016-09-06 10:34:16,431 DEBUG: Done:	 Training
-2016-09-06 10:34:16,431 DEBUG: Start:	 Predicting
-2016-09-06 10:34:16,435 DEBUG: Done:	 Predicting
-2016-09-06 10:34:16,436 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:16,437 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:16,437 DEBUG: Done:	 Predicting
-2016-09-06 10:34:16,437 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,437 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:16,437 INFO: Done:	 Result Analysis
-2016-09-06 10:34:16,439 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:16,439 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,439 INFO: Done:	 Result Analysis
-2016-09-06 10:34:16,585 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,585 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:34:16,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:34:16,586 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:16,586 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:16,586 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,586 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,586 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,586 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,586 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:16,586 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:16,587 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,587 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,617 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:16,617 DEBUG: Start:	 Training
-2016-09-06 10:34:16,618 DEBUG: Info:	 Time for Training: 0.0332248210907[s]
-2016-09-06 10:34:16,618 DEBUG: Done:	 Training
-2016-09-06 10:34:16,618 DEBUG: Start:	 Predicting
-2016-09-06 10:34:16,621 DEBUG: Done:	 Predicting
-2016-09-06 10:34:16,621 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:16,622 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:16,622 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,623 INFO: Done:	 Result Analysis
-2016-09-06 10:34:16,636 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:16,636 DEBUG: Start:	 Training
-2016-09-06 10:34:16,640 DEBUG: Info:	 Time for Training: 0.054888010025[s]
-2016-09-06 10:34:16,640 DEBUG: Done:	 Training
-2016-09-06 10:34:16,640 DEBUG: Start:	 Predicting
-2016-09-06 10:34:16,643 DEBUG: Done:	 Predicting
-2016-09-06 10:34:16,643 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:16,645 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:16,645 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,645 INFO: Done:	 Result Analysis
-2016-09-06 10:34:16,736 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,736 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,736 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:34:16,736 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:34:16,737 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:16,737 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:16,737 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,737 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,737 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,737 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,737 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:16,737 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:16,738 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,738 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,771 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:16,771 DEBUG: Start:	 Training
-2016-09-06 10:34:16,772 DEBUG: Info:	 Time for Training: 0.0359029769897[s]
-2016-09-06 10:34:16,772 DEBUG: Done:	 Training
-2016-09-06 10:34:16,772 DEBUG: Start:	 Predicting
-2016-09-06 10:34:16,779 DEBUG: Done:	 Predicting
-2016-09-06 10:34:16,779 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:16,780 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:16,780 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 34
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,780 INFO: Done:	 Result Analysis
-2016-09-06 10:34:16,879 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:16,879 DEBUG: Start:	 Training
-2016-09-06 10:34:16,896 DEBUG: Info:	 Time for Training: 0.160787820816[s]
-2016-09-06 10:34:16,897 DEBUG: Done:	 Training
-2016-09-06 10:34:16,897 DEBUG: Start:	 Predicting
-2016-09-06 10:34:16,900 DEBUG: Done:	 Predicting
-2016-09-06 10:34:16,900 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:16,902 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:16,902 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.709523809524
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.709523809524
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,902 INFO: Done:	 Result Analysis
-2016-09-06 10:34:16,988 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,988 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,989 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:34:16,989 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:34:16,989 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:16,989 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:34:16,990 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,990 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,990 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,990 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,990 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:16,990 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:34:16,990 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,990 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:34:17,060 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:17,061 DEBUG: Start:	 Training
-2016-09-06 10:34:17,062 DEBUG: Info:	 Time for Training: 0.0743260383606[s]
-2016-09-06 10:34:17,062 DEBUG: Done:	 Training
-2016-09-06 10:34:17,062 DEBUG: Start:	 Predicting
-2016-09-06 10:34:17,066 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:34:17,066 DEBUG: Start:	 Training
-2016-09-06 10:34:17,076 DEBUG: Done:	 Predicting
-2016-09-06 10:34:17,077 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:17,080 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:17,080 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:34:17,081 INFO: Done:	 Result Analysis
-2016-09-06 10:34:17,094 DEBUG: Info:	 Time for Training: 0.106731176376[s]
-2016-09-06 10:34:17,094 DEBUG: Done:	 Training
-2016-09-06 10:34:17,094 DEBUG: Start:	 Predicting
-2016-09-06 10:34:17,097 DEBUG: Done:	 Predicting
-2016-09-06 10:34:17,097 DEBUG: Start:	 Getting Results
-2016-09-06 10:34:17,099 DEBUG: Done:	 Getting Results
-2016-09-06 10:34:17,099 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:34:17,099 INFO: Done:	 Result Analysis
-2016-09-06 10:34:17,389 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:34:17,389 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:34:17,390 INFO: Info:	 Shape of View0 :(300, 9)
-2016-09-06 10:34:17,390 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:34:17,390 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:34:17,390 INFO: Info:	 Shape of View1 :(300, 15)
-2016-09-06 10:34:17,391 INFO: Info:	 Shape of View0 :(300, 9)
-2016-09-06 10:34:17,391 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 10:34:17,392 INFO: Info:	 Shape of View1 :(300, 15)
-2016-09-06 10:34:17,392 INFO: Info:	 Shape of View3 :(300, 10)
-2016-09-06 10:34:17,392 INFO: Done:	 Read Database Files
-2016-09-06 10:34:17,392 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:34:17,392 INFO: Info:	 Shape of View2 :(300, 17)
-2016-09-06 10:34:17,393 INFO: Info:	 Shape of View3 :(300, 10)
-2016-09-06 10:34:17,393 INFO: Done:	 Read Database Files
-2016-09-06 10:34:17,393 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:34:17,397 INFO: Done:	 Determine validation split
-2016-09-06 10:34:17,398 INFO: Start:	 Determine 5 folds
-2016-09-06 10:34:17,398 INFO: Done:	 Determine validation split
-2016-09-06 10:34:17,398 INFO: Start:	 Determine 5 folds
-2016-09-06 10:34:17,407 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:34:17,408 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:34:17,408 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:34:17,408 INFO: Done:	 Determine folds
-2016-09-06 10:34:17,408 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:34:17,408 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:34:17,408 INFO: Start:	 Classification
-2016-09-06 10:34:17,408 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:34:17,408 INFO: 	Start:	 Fold number 1
-2016-09-06 10:34:17,408 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:34:17,408 INFO: Done:	 Determine folds
-2016-09-06 10:34:17,408 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:34:17,408 INFO: Start:	 Classification
-2016-09-06 10:34:17,408 INFO: 	Start:	 Fold number 1
-2016-09-06 10:34:17,439 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:34:17,447 DEBUG: 			View 0 : 0.526627218935
-2016-09-06 10:34:17,455 DEBUG: 			View 1 : 0.455621301775
-2016-09-06 10:34:17,463 DEBUG: 			View 2 : 0.473372781065
-2016-09-06 10:34:17,471 DEBUG: 			View 3 : 0.485207100592
-2016-09-06 10:34:17,504 DEBUG: 			 Best view : 		View2
-2016-09-06 10:34:17,582 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:34:17,590 DEBUG: 			View 0 : 0.710059171598
-2016-09-06 10:34:17,598 DEBUG: 			View 1 : 0.763313609467
-2016-09-06 10:34:17,605 DEBUG: 			View 2 : 0.775147928994
-2016-09-06 10:34:17,613 DEBUG: 			View 3 : 0.715976331361
-2016-09-06 10:34:17,650 DEBUG: 			 Best view : 		View2
-2016-09-06 10:34:17,793 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:34:17,807 DEBUG: 			View 0 : 0.710059171598
-2016-09-06 10:34:17,815 DEBUG: 			View 1 : 0.763313609467
-2016-09-06 10:34:17,824 DEBUG: 			View 2 : 0.775147928994
-2016-09-06 10:34:17,831 DEBUG: 			View 3 : 0.715976331361
-2016-09-06 10:34:17,875 DEBUG: 			 Best view : 		View2
-2016-09-06 10:34:18,092 DEBUG: 		Start:	 Iteration 4
-2016-09-06 10:34:18,099 DEBUG: 			View 0 : 0.662721893491
-2016-09-06 10:34:18,107 DEBUG: 			View 1 : 0.662721893491
-2016-09-06 10:34:18,114 DEBUG: 			View 2 : 0.633136094675
-2016-09-06 10:34:18,121 DEBUG: 			View 3 : 0.680473372781
-2016-09-06 10:34:18,162 DEBUG: 			 Best view : 		View3
-2016-09-06 10:34:18,430 INFO: 	Start: 	 Classification
-2016-09-06 10:34:18,888 INFO: 	Done: 	 Fold number 1
-2016-09-06 10:34:18,888 INFO: 	Start:	 Fold number 2
-2016-09-06 10:34:18,917 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:34:18,924 DEBUG: 			View 0 : 0.556213017751
-2016-09-06 10:34:18,931 DEBUG: 			View 1 : 0.603550295858
-2016-09-06 10:34:18,939 DEBUG: 			View 2 : 0.497041420118
-2016-09-06 10:34:18,946 DEBUG: 			View 3 : 0.491124260355
-2016-09-06 10:34:18,976 DEBUG: 			 Best view : 		View1
-2016-09-06 10:34:19,051 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:34:19,059 DEBUG: 			View 0 : 0.715976331361
-2016-09-06 10:34:19,066 DEBUG: 			View 1 : 0.680473372781
-2016-09-06 10:34:19,073 DEBUG: 			View 2 : 0.710059171598
-2016-09-06 10:34:19,080 DEBUG: 			View 3 : 0.704142011834
-2016-09-06 10:34:19,116 DEBUG: 			 Best view : 		View0
-2016-09-06 10:34:19,257 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:34:19,264 DEBUG: 			View 0 : 0.715976331361
-2016-09-06 10:34:19,272 DEBUG: 			View 1 : 0.680473372781
-2016-09-06 10:34:19,279 DEBUG: 			View 2 : 0.710059171598
-2016-09-06 10:34:19,287 DEBUG: 			View 3 : 0.704142011834
-2016-09-06 10:34:19,325 DEBUG: 			 Best view : 		View0
-2016-09-06 10:34:19,529 INFO: 	Start: 	 Classification
-2016-09-06 10:34:19,873 INFO: 	Done: 	 Fold number 2
-2016-09-06 10:34:19,874 INFO: 	Start:	 Fold number 3
-2016-09-06 10:34:19,903 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:34:19,909 DEBUG: 			View 0 : 0.544378698225
-2016-09-06 10:34:19,916 DEBUG: 			View 1 : 0.544378698225
-2016-09-06 10:34:19,923 DEBUG: 			View 2 : 0.544378698225
-2016-09-06 10:34:19,930 DEBUG: 			View 3 : 0.544378698225
-2016-09-06 10:34:19,961 DEBUG: 			 Best view : 		View0
-2016-09-06 10:34:20,036 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:34:20,043 DEBUG: 			View 0 : 0.692307692308
-2016-09-06 10:34:20,051 DEBUG: 			View 1 : 0.692307692308
-2016-09-06 10:34:20,058 DEBUG: 			View 2 : 0.769230769231
-2016-09-06 10:34:20,065 DEBUG: 			View 3 : 0.733727810651
-2016-09-06 10:34:20,101 DEBUG: 			 Best view : 		View2
-2016-09-06 10:34:20,241 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:34:20,249 DEBUG: 			View 0 : 0.692307692308
-2016-09-06 10:34:20,256 DEBUG: 			View 1 : 0.692307692308
-2016-09-06 10:34:20,264 DEBUG: 			View 2 : 0.769230769231
-2016-09-06 10:34:20,271 DEBUG: 			View 3 : 0.733727810651
-2016-09-06 10:34:20,310 DEBUG: 			 Best view : 		View2
-2016-09-06 10:34:20,513 INFO: 	Start: 	 Classification
-2016-09-06 10:34:20,853 INFO: 	Done: 	 Fold number 3
-2016-09-06 10:34:20,853 INFO: 	Start:	 Fold number 4
-2016-09-06 10:34:20,882 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:34:20,889 DEBUG: 			View 0 : 0.585798816568
-2016-09-06 10:34:20,896 DEBUG: 			View 1 : 0.502958579882
-2016-09-06 10:34:20,904 DEBUG: 			View 2 : 0.538461538462
-2016-09-06 10:34:20,911 DEBUG: 			View 3 : 0.544378698225
-2016-09-06 10:34:20,940 DEBUG: 			 Best view : 		View3
-2016-09-06 10:34:21,016 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:34:21,023 DEBUG: 			View 0 : 0.692307692308
-2016-09-06 10:34:21,031 DEBUG: 			View 1 : 0.704142011834
-2016-09-06 10:34:21,038 DEBUG: 			View 2 : 0.650887573964
-2016-09-06 10:34:21,045 DEBUG: 			View 3 : 0.698224852071
-2016-09-06 10:34:21,081 DEBUG: 			 Best view : 		View1
-2016-09-06 10:34:21,221 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:34:21,228 DEBUG: 			View 0 : 0.692307692308
-2016-09-06 10:34:21,235 DEBUG: 			View 1 : 0.704142011834
-2016-09-06 10:34:21,243 DEBUG: 			View 2 : 0.650887573964
-2016-09-06 10:34:21,250 DEBUG: 			View 3 : 0.698224852071
-2016-09-06 10:34:21,289 DEBUG: 			 Best view : 		View1
-2016-09-06 10:34:21,493 INFO: 	Start: 	 Classification
-2016-09-06 10:34:21,834 INFO: 	Done: 	 Fold number 4
-2016-09-06 10:34:21,834 INFO: 	Start:	 Fold number 5
-2016-09-06 10:34:21,864 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:34:21,871 DEBUG: 			View 0 : 0.455621301775
-2016-09-06 10:34:21,877 DEBUG: 			View 1 : 0.455621301775
-2016-09-06 10:34:21,884 DEBUG: 			View 2 : 0.455621301775
-2016-09-06 10:34:21,891 DEBUG: 			View 3 : 0.455621301775
-2016-09-06 10:34:21,891 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 10:34:21,920 DEBUG: 			 Best view : 		View0
-2016-09-06 10:34:21,995 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:34:22,002 DEBUG: 			View 0 : 0.680473372781
-2016-09-06 10:34:22,010 DEBUG: 			View 1 : 0.733727810651
-2016-09-06 10:34:22,017 DEBUG: 			View 2 : 0.745562130178
-2016-09-06 10:34:22,024 DEBUG: 			View 3 : 0.710059171598
-2016-09-06 10:34:22,060 DEBUG: 			 Best view : 		View2
-2016-09-06 10:34:22,200 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:34:22,206 DEBUG: 			View 0 : 0.680473372781
-2016-09-06 10:34:22,214 DEBUG: 			View 1 : 0.733727810651
-2016-09-06 10:34:22,221 DEBUG: 			View 2 : 0.745562130178
-2016-09-06 10:34:22,228 DEBUG: 			View 3 : 0.710059171598
-2016-09-06 10:34:22,267 DEBUG: 			 Best view : 		View2
-2016-09-06 10:34:22,470 INFO: 	Start: 	 Classification
-2016-09-06 10:34:22,812 INFO: 	Done: 	 Fold number 5
-2016-09-06 10:34:22,812 INFO: Done:	 Classification
-2016-09-06 10:34:22,812 INFO: Info:	 Time for Classification: 5[s]
-2016-09-06 10:34:22,812 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 10:34:24,767 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 74.201183432
-	-On Test : 49.5238095238
-	-On Validation : 52.808988764Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 9), View1 of shape (300, 15), View2 of shape (300, 17), View3 of shape (300, 10)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:04        0:00:00
-	         Fold 5        0:00:05        0:00:00
-	          Total        0:00:15        0:00:01
-	So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.260946745562
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.26449704142
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.265680473373
-			- Percentage of time chosen : 0.3
-		- On View3 : 
-			- Mean average Accuracy : 0.259763313609
-			- Percentage of time chosen : 0.1
-	- Fold 1, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.198816568047
-			- Percentage of time chosen : 0.9
-		- On View1 : 
-			- Mean average Accuracy : 0.196449704142
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.191715976331
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.189940828402
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.192899408284
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.192899408284
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.208284023669
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.201183431953
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.197041420118
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.191124260355
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.184023668639
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.194082840237
-			- Percentage of time chosen : 0.1
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.181656804734
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.192307692308
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.194674556213
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.187573964497
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 77.5147928994
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 71.5976331361
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 76.9230769231
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.808988764
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 70.4142011834
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 74.5562130178
-			Accuracy on test : 0.0
-			Accuracy on validation : 46.0674157303
-			Selected View : View2
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 77.5147928994
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 71.5976331361
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 76.9230769231
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.808988764
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 70.4142011834
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 74.5562130178
-			Accuracy on test : 0.0
-			Accuracy on validation : 46.0674157303
-			Selected View : View2
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 77.5147928994
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-2016-09-06 10:34:24,951 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b21ed4f8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 77474e0f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 09eb9bd9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 34
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75aeeab8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index de64a87e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8accfa21..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.471428571429
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8d98bcf9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ebca06a9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 9)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ca874bc8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.611111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 33f3d2e5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ef3258f8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 34
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 42abfcbf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.685714285714
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.685714285714
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 098bc8e6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.633333333333
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.633333333333
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f8a69440..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.388888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.388888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 85ebfdd4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d4cd5d9a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e41e726a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3c3a4f96..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9bf78e66..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 34
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0b0dc718..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.709523809524
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.709523809524
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fe4a10e8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index eaf4be41..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7a8b79bf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b1b7d9bc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 17)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dc3c519a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6bbe2181..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 10)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2978
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index f514bd75..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,215 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 74.201183432
-	-On Test : 49.5238095238
-	-On Validation : 52.808988764Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 9), View1 of shape (300, 15), View2 of shape (300, 17), View3 of shape (300, 10)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:01        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:04        0:00:00
-	         Fold 5        0:00:05        0:00:00
-	          Total        0:00:15        0:00:01
-	So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 5
-		- On View0 : 
-			- Mean average Accuracy : 0.260946745562
-			- Percentage of time chosen : 0.6
-		- On View1 : 
-			- Mean average Accuracy : 0.26449704142
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.265680473373
-			- Percentage of time chosen : 0.3
-		- On View3 : 
-			- Mean average Accuracy : 0.259763313609
-			- Percentage of time chosen : 0.1
-	- Fold 1, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.198816568047
-			- Percentage of time chosen : 0.9
-		- On View1 : 
-			- Mean average Accuracy : 0.196449704142
-			- Percentage of time chosen : 0.1
-		- On View2 : 
-			- Mean average Accuracy : 0.191715976331
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.189940828402
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.192899408284
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.192899408284
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.208284023669
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.201183431953
-			- Percentage of time chosen : 0.0
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.197041420118
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.191124260355
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.184023668639
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.194082840237
-			- Percentage of time chosen : 0.1
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.181656804734
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.192307692308
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.194674556213
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.187573964497
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View1
-		 Fold 3
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View3
-		 Fold 5
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 77.5147928994
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 71.5976331361
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 76.9230769231
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.808988764
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 70.4142011834
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 74.5562130178
-			Accuracy on test : 0.0
-			Accuracy on validation : 46.0674157303
-			Selected View : View2
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 77.5147928994
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View2
-		 Fold 2
-			Accuracy on train : 71.5976331361
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 76.9230769231
-			Accuracy on test : 0.0
-			Accuracy on validation : 52.808988764
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 70.4142011834
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View1
-		 Fold 5
-			Accuracy on train : 74.5562130178
-			Accuracy on test : 0.0
-			Accuracy on validation : 46.0674157303
-			Selected View : View2
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 77.5147928994
-			Accuracy on test : 0.0
-			Accuracy on validation : 50.5617977528
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
-	- Iteration 5
-		 Fold 1
-			Accuracy on train : 54.4378698225
-			Accuracy on test : 0.0
-			Accuracy on validation : 53.9325842697
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index e5263038..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1570 +0,0 @@
-2016-09-06 10:37:42,137 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:37:42,137 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00010290625 Gbytes /!\ 
-2016-09-06 10:37:47,151 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:37:47,154 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:37:47,209 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,210 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:37:47,210 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:47,211 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,211 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,211 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,211 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:47,211 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:37:47,211 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,211 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:47,212 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,213 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,213 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:47,213 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,247 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:47,247 DEBUG: Start:	 Training
-2016-09-06 10:37:47,248 DEBUG: Info:	 Time for Training: 0.0386531352997[s]
-2016-09-06 10:37:47,248 DEBUG: Done:	 Training
-2016-09-06 10:37:47,248 DEBUG: Start:	 Predicting
-2016-09-06 10:37:47,251 DEBUG: Done:	 Predicting
-2016-09-06 10:37:47,251 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:47,252 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:47,252 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.633333333333
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.633333333333
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,253 INFO: Done:	 Result Analysis
-2016-09-06 10:37:47,259 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:47,259 DEBUG: Start:	 Training
-2016-09-06 10:37:47,262 DEBUG: Info:	 Time for Training: 0.0534279346466[s]
-2016-09-06 10:37:47,262 DEBUG: Done:	 Training
-2016-09-06 10:37:47,262 DEBUG: Start:	 Predicting
-2016-09-06 10:37:47,265 DEBUG: Done:	 Predicting
-2016-09-06 10:37:47,265 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:47,267 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:47,267 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,267 INFO: Done:	 Result Analysis
-2016-09-06 10:37:47,354 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,354 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,354 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:37:47,354 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:37:47,354 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:47,354 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:47,355 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,355 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,355 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,355 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,355 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:47,355 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:47,355 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,355 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,386 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:47,386 DEBUG: Start:	 Training
-2016-09-06 10:37:47,387 DEBUG: Info:	 Time for Training: 0.0330820083618[s]
-2016-09-06 10:37:47,387 DEBUG: Done:	 Training
-2016-09-06 10:37:47,387 DEBUG: Start:	 Predicting
-2016-09-06 10:37:47,391 DEBUG: Done:	 Predicting
-2016-09-06 10:37:47,391 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:47,392 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:47,392 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.67619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,392 INFO: Done:	 Result Analysis
-2016-09-06 10:37:47,513 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:47,513 DEBUG: Start:	 Training
-2016-09-06 10:37:47,534 DEBUG: Info:	 Time for Training: 0.180033922195[s]
-2016-09-06 10:37:47,534 DEBUG: Done:	 Training
-2016-09-06 10:37:47,534 DEBUG: Start:	 Predicting
-2016-09-06 10:37:47,538 DEBUG: Done:	 Predicting
-2016-09-06 10:37:47,538 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:47,539 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:47,539 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.766666666667
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.766666666667
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,540 INFO: Done:	 Result Analysis
-2016-09-06 10:37:47,604 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,604 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,604 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:37:47,604 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:37:47,604 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:47,604 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:47,605 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,605 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,605 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,605 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,605 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:47,605 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:47,606 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,606 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,651 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:47,651 DEBUG: Start:	 Training
-2016-09-06 10:37:47,652 DEBUG: Info:	 Time for Training: 0.0492498874664[s]
-2016-09-06 10:37:47,652 DEBUG: Done:	 Training
-2016-09-06 10:37:47,652 DEBUG: Start:	 Predicting
-2016-09-06 10:37:47,662 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:47,662 DEBUG: Start:	 Training
-2016-09-06 10:37:47,663 DEBUG: Done:	 Predicting
-2016-09-06 10:37:47,664 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:47,665 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:47,666 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,666 INFO: Done:	 Result Analysis
-2016-09-06 10:37:47,682 DEBUG: Info:	 Time for Training: 0.0788521766663[s]
-2016-09-06 10:37:47,682 DEBUG: Done:	 Training
-2016-09-06 10:37:47,682 DEBUG: Start:	 Predicting
-2016-09-06 10:37:47,685 DEBUG: Done:	 Predicting
-2016-09-06 10:37:47,685 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:47,686 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:47,686 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,687 INFO: Done:	 Result Analysis
-2016-09-06 10:37:47,750 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,750 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:37:47,750 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:47,751 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,751 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,751 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,751 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:37:47,751 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:47,751 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:47,751 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,752 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,752 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,752 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:47,752 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,803 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:47,803 DEBUG: Start:	 Training
-2016-09-06 10:37:47,807 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:47,807 DEBUG: Start:	 Training
-2016-09-06 10:37:47,822 DEBUG: Info:	 Time for Training: 0.0717940330505[s]
-2016-09-06 10:37:47,823 DEBUG: Done:	 Training
-2016-09-06 10:37:47,823 DEBUG: Start:	 Predicting
-2016-09-06 10:37:47,827 DEBUG: Info:	 Time for Training: 0.07728099823[s]
-2016-09-06 10:37:47,827 DEBUG: Done:	 Training
-2016-09-06 10:37:47,827 DEBUG: Start:	 Predicting
-2016-09-06 10:37:47,828 DEBUG: Done:	 Predicting
-2016-09-06 10:37:47,828 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:47,829 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:47,829 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,830 INFO: Done:	 Result Analysis
-2016-09-06 10:37:47,831 DEBUG: Done:	 Predicting
-2016-09-06 10:37:47,831 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:47,833 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:47,833 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.366666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.366666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,833 INFO: Done:	 Result Analysis
-2016-09-06 10:37:47,901 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,901 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,902 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:37:47,902 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:37:47,902 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:47,902 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:47,903 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:47,903 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:47,903 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:47,903 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:47,903 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:47,903 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:47,903 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,903 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,948 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:47,948 DEBUG: Start:	 Training
-2016-09-06 10:37:47,949 DEBUG: Info:	 Time for Training: 0.0490880012512[s]
-2016-09-06 10:37:47,950 DEBUG: Done:	 Training
-2016-09-06 10:37:47,950 DEBUG: Start:	 Predicting
-2016-09-06 10:37:47,953 DEBUG: Done:	 Predicting
-2016-09-06 10:37:47,953 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:47,955 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:47,955 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.738095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,956 INFO: Done:	 Result Analysis
-2016-09-06 10:37:47,964 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:47,964 DEBUG: Start:	 Training
-2016-09-06 10:37:47,968 DEBUG: Info:	 Time for Training: 0.067459821701[s]
-2016-09-06 10:37:47,968 DEBUG: Done:	 Training
-2016-09-06 10:37:47,968 DEBUG: Start:	 Predicting
-2016-09-06 10:37:47,971 DEBUG: Done:	 Predicting
-2016-09-06 10:37:47,971 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:47,973 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:47,973 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,973 INFO: Done:	 Result Analysis
-2016-09-06 10:37:48,050 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,050 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,050 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:37:48,050 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:37:48,050 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:48,050 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:48,051 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,051 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,051 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,051 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,051 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:48,051 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:48,051 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,051 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,081 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:48,081 DEBUG: Start:	 Training
-2016-09-06 10:37:48,082 DEBUG: Info:	 Time for Training: 0.0320069789886[s]
-2016-09-06 10:37:48,082 DEBUG: Done:	 Training
-2016-09-06 10:37:48,082 DEBUG: Start:	 Predicting
-2016-09-06 10:37:48,087 DEBUG: Done:	 Predicting
-2016-09-06 10:37:48,087 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:48,088 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:48,088 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.657142857143
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.657142857143
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,088 INFO: Done:	 Result Analysis
-2016-09-06 10:37:48,209 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:48,209 DEBUG: Start:	 Training
-2016-09-06 10:37:48,230 DEBUG: Info:	 Time for Training: 0.180283069611[s]
-2016-09-06 10:37:48,230 DEBUG: Done:	 Training
-2016-09-06 10:37:48,230 DEBUG: Start:	 Predicting
-2016-09-06 10:37:48,234 DEBUG: Done:	 Predicting
-2016-09-06 10:37:48,234 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:48,236 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:48,236 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.833333333333
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.833333333333
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,236 INFO: Done:	 Result Analysis
-2016-09-06 10:37:48,304 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,304 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,305 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:37:48,305 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:37:48,305 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:48,305 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:48,306 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,306 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,307 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,307 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,307 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:48,307 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:48,307 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,307 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,363 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:48,363 DEBUG: Start:	 Training
-2016-09-06 10:37:48,364 DEBUG: Info:	 Time for Training: 0.0610771179199[s]
-2016-09-06 10:37:48,364 DEBUG: Done:	 Training
-2016-09-06 10:37:48,364 DEBUG: Start:	 Predicting
-2016-09-06 10:37:48,371 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:48,371 DEBUG: Start:	 Training
-2016-09-06 10:37:48,389 DEBUG: Done:	 Predicting
-2016-09-06 10:37:48,390 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:48,391 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:48,391 DEBUG: Info:	 Time for Training: 0.088534116745[s]
-2016-09-06 10:37:48,391 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,391 DEBUG: Done:	 Training
-2016-09-06 10:37:48,392 DEBUG: Start:	 Predicting
-2016-09-06 10:37:48,392 INFO: Done:	 Result Analysis
-2016-09-06 10:37:48,395 DEBUG: Done:	 Predicting
-2016-09-06 10:37:48,395 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:48,397 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:48,397 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,397 INFO: Done:	 Result Analysis
-2016-09-06 10:37:48,548 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,548 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:37:48,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:37:48,548 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:48,548 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:48,549 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,549 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,549 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,549 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,549 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:48,549 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:48,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,600 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:48,600 DEBUG: Start:	 Training
-2016-09-06 10:37:48,603 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:48,603 DEBUG: Start:	 Training
-2016-09-06 10:37:48,612 DEBUG: Info:	 Time for Training: 0.0646460056305[s]
-2016-09-06 10:37:48,612 DEBUG: Done:	 Training
-2016-09-06 10:37:48,612 DEBUG: Start:	 Predicting
-2016-09-06 10:37:48,616 DEBUG: Done:	 Predicting
-2016-09-06 10:37:48,616 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:48,617 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:48,617 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,618 INFO: Done:	 Result Analysis
-2016-09-06 10:37:48,622 DEBUG: Info:	 Time for Training: 0.0746550559998[s]
-2016-09-06 10:37:48,622 DEBUG: Done:	 Training
-2016-09-06 10:37:48,622 DEBUG: Start:	 Predicting
-2016-09-06 10:37:48,628 DEBUG: Done:	 Predicting
-2016-09-06 10:37:48,628 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:48,629 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:48,629 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,629 INFO: Done:	 Result Analysis
-2016-09-06 10:37:48,696 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,696 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:37:48,696 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:48,696 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,696 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:37:48,697 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:48,697 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:48,697 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:48,697 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:48,697 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:48,697 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,698 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:48,698 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:48,698 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,734 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:48,735 DEBUG: Start:	 Training
-2016-09-06 10:37:48,736 DEBUG: Info:	 Time for Training: 0.0404579639435[s]
-2016-09-06 10:37:48,736 DEBUG: Done:	 Training
-2016-09-06 10:37:48,736 DEBUG: Start:	 Predicting
-2016-09-06 10:37:48,739 DEBUG: Done:	 Predicting
-2016-09-06 10:37:48,739 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:48,740 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:48,741 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.690476190476
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.690476190476
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,741 INFO: Done:	 Result Analysis
-2016-09-06 10:37:48,750 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:48,750 DEBUG: Start:	 Training
-2016-09-06 10:37:48,754 DEBUG: Info:	 Time for Training: 0.059161901474[s]
-2016-09-06 10:37:48,754 DEBUG: Done:	 Training
-2016-09-06 10:37:48,754 DEBUG: Start:	 Predicting
-2016-09-06 10:37:48,757 DEBUG: Done:	 Predicting
-2016-09-06 10:37:48,757 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:48,759 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:48,759 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,760 INFO: Done:	 Result Analysis
-2016-09-06 10:37:48,847 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,847 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:37:48,847 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:48,848 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:48,848 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:48,848 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:48,848 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,848 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,848 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:37:48,849 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:48,849 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:48,849 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:48,849 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:48,850 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,879 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:48,879 DEBUG: Start:	 Training
-2016-09-06 10:37:48,880 DEBUG: Info:	 Time for Training: 0.0334219932556[s]
-2016-09-06 10:37:48,880 DEBUG: Done:	 Training
-2016-09-06 10:37:48,880 DEBUG: Start:	 Predicting
-2016-09-06 10:37:48,885 DEBUG: Done:	 Predicting
-2016-09-06 10:37:48,885 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:48,887 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:48,887 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.690476190476
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.690476190476
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,887 INFO: Done:	 Result Analysis
-2016-09-06 10:37:49,024 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:49,024 DEBUG: Start:	 Training
-2016-09-06 10:37:49,046 DEBUG: Info:	 Time for Training: 0.198237895966[s]
-2016-09-06 10:37:49,046 DEBUG: Done:	 Training
-2016-09-06 10:37:49,046 DEBUG: Start:	 Predicting
-2016-09-06 10:37:49,050 DEBUG: Done:	 Predicting
-2016-09-06 10:37:49,051 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:49,053 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:49,053 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.809523809524
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.809523809524
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,053 INFO: Done:	 Result Analysis
-2016-09-06 10:37:49,200 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,200 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,200 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:37:49,200 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:37:49,200 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:49,200 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:49,201 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:49,201 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:49,201 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:49,201 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:49,202 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:49,202 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:49,202 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,202 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,272 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:49,273 DEBUG: Start:	 Training
-2016-09-06 10:37:49,274 DEBUG: Info:	 Time for Training: 0.0749552249908[s]
-2016-09-06 10:37:49,274 DEBUG: Done:	 Training
-2016-09-06 10:37:49,274 DEBUG: Start:	 Predicting
-2016-09-06 10:37:49,279 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:49,279 DEBUG: Start:	 Training
-2016-09-06 10:37:49,293 DEBUG: Done:	 Predicting
-2016-09-06 10:37:49,293 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:49,295 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:49,295 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,295 INFO: Done:	 Result Analysis
-2016-09-06 10:37:49,299 DEBUG: Info:	 Time for Training: 0.0997180938721[s]
-2016-09-06 10:37:49,299 DEBUG: Done:	 Training
-2016-09-06 10:37:49,299 DEBUG: Start:	 Predicting
-2016-09-06 10:37:49,302 DEBUG: Done:	 Predicting
-2016-09-06 10:37:49,302 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:49,304 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:49,304 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,304 INFO: Done:	 Result Analysis
-2016-09-06 10:37:49,450 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,450 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,450 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:37:49,450 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:37:49,450 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:49,450 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:49,451 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:49,451 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:49,451 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:49,451 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:49,452 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:49,452 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:49,452 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,452 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,501 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:49,501 DEBUG: Start:	 Training
-2016-09-06 10:37:49,501 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:49,502 DEBUG: Start:	 Training
-2016-09-06 10:37:49,515 DEBUG: Info:	 Time for Training: 0.0661950111389[s]
-2016-09-06 10:37:49,516 DEBUG: Done:	 Training
-2016-09-06 10:37:49,516 DEBUG: Start:	 Predicting
-2016-09-06 10:37:49,518 DEBUG: Info:	 Time for Training: 0.0691938400269[s]
-2016-09-06 10:37:49,519 DEBUG: Done:	 Training
-2016-09-06 10:37:49,519 DEBUG: Start:	 Predicting
-2016-09-06 10:37:49,520 DEBUG: Done:	 Predicting
-2016-09-06 10:37:49,520 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:49,522 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:49,522 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,522 INFO: Done:	 Result Analysis
-2016-09-06 10:37:49,525 DEBUG: Done:	 Predicting
-2016-09-06 10:37:49,525 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:49,526 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:49,526 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,526 INFO: Done:	 Result Analysis
-2016-09-06 10:37:49,597 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,598 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:37:49,598 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:49,598 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,598 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,598 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:37:49,598 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:49,598 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,599 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:49,599 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,599 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,599 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,599 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:49,599 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,629 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:49,629 DEBUG: Start:	 Training
-2016-09-06 10:37:49,629 DEBUG: Info:	 Time for Training: 0.0316710472107[s]
-2016-09-06 10:37:49,629 DEBUG: Done:	 Training
-2016-09-06 10:37:49,630 DEBUG: Start:	 Predicting
-2016-09-06 10:37:49,632 DEBUG: Done:	 Predicting
-2016-09-06 10:37:49,632 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:49,634 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:49,634 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.742857142857
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.742857142857
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,634 INFO: Done:	 Result Analysis
-2016-09-06 10:37:49,645 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:49,645 DEBUG: Start:	 Training
-2016-09-06 10:37:49,648 DEBUG: Info:	 Time for Training: 0.0510141849518[s]
-2016-09-06 10:37:49,648 DEBUG: Done:	 Training
-2016-09-06 10:37:49,648 DEBUG: Start:	 Predicting
-2016-09-06 10:37:49,651 DEBUG: Done:	 Predicting
-2016-09-06 10:37:49,651 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:49,653 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:49,653 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,653 INFO: Done:	 Result Analysis
-2016-09-06 10:37:49,750 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,750 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,751 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:37:49,751 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:37:49,751 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:49,751 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:49,751 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,751 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,752 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,752 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,752 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:49,752 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:49,752 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,752 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,784 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:49,784 DEBUG: Start:	 Training
-2016-09-06 10:37:49,785 DEBUG: Info:	 Time for Training: 0.0350430011749[s]
-2016-09-06 10:37:49,785 DEBUG: Done:	 Training
-2016-09-06 10:37:49,785 DEBUG: Start:	 Predicting
-2016-09-06 10:37:49,789 DEBUG: Done:	 Predicting
-2016-09-06 10:37:49,789 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:49,790 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:49,791 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.709523809524
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.709523809524
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,791 INFO: Done:	 Result Analysis
-2016-09-06 10:37:49,912 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:49,912 DEBUG: Start:	 Training
-2016-09-06 10:37:49,932 DEBUG: Info:	 Time for Training: 0.182338953018[s]
-2016-09-06 10:37:49,932 DEBUG: Done:	 Training
-2016-09-06 10:37:49,932 DEBUG: Start:	 Predicting
-2016-09-06 10:37:49,936 DEBUG: Done:	 Predicting
-2016-09-06 10:37:49,936 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:49,938 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:49,938 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.8
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.8
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,938 INFO: Done:	 Result Analysis
-2016-09-06 10:37:49,997 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,997 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,998 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:37:49,998 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:37:49,998 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:49,998 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:37:49,999 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,999 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,999 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,999 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,999 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:49,999 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:37:49,999 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,999 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:37:50,048 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:50,048 DEBUG: Start:	 Training
-2016-09-06 10:37:50,053 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:37:50,053 DEBUG: Start:	 Training
-2016-09-06 10:37:50,053 DEBUG: Info:	 Time for Training: 0.056587934494[s]
-2016-09-06 10:37:50,054 DEBUG: Done:	 Training
-2016-09-06 10:37:50,054 DEBUG: Start:	 Predicting
-2016-09-06 10:37:50,064 DEBUG: Info:	 Time for Training: 0.0674622058868[s]
-2016-09-06 10:37:50,064 DEBUG: Done:	 Training
-2016-09-06 10:37:50,065 DEBUG: Start:	 Predicting
-2016-09-06 10:37:50,068 DEBUG: Done:	 Predicting
-2016-09-06 10:37:50,068 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:50,069 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:50,069 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:37:50,069 INFO: Done:	 Result Analysis
-2016-09-06 10:37:50,075 DEBUG: Done:	 Predicting
-2016-09-06 10:37:50,076 DEBUG: Start:	 Getting Results
-2016-09-06 10:37:50,079 DEBUG: Done:	 Getting Results
-2016-09-06 10:37:50,079 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:37:50,079 INFO: Done:	 Result Analysis
-2016-09-06 10:37:50,290 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:37:50,290 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:37:50,291 INFO: Info:	 Shape of View0 :(300, 5)
-2016-09-06 10:37:50,292 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 10:37:50,292 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:37:50,292 INFO: Info:	 Shape of View2 :(300, 14)
-2016-09-06 10:37:50,292 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:37:50,293 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 10:37:50,293 INFO: Done:	 Read Database Files
-2016-09-06 10:37:50,293 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:37:50,294 INFO: Info:	 Shape of View0 :(300, 5)
-2016-09-06 10:37:50,295 INFO: Info:	 Shape of View1 :(300, 12)
-2016-09-06 10:37:50,295 INFO: Info:	 Shape of View2 :(300, 14)
-2016-09-06 10:37:50,296 INFO: Info:	 Shape of View3 :(300, 5)
-2016-09-06 10:37:50,296 INFO: Done:	 Read Database Files
-2016-09-06 10:37:50,296 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:37:50,299 INFO: Done:	 Determine validation split
-2016-09-06 10:37:50,299 INFO: Start:	 Determine 5 folds
-2016-09-06 10:37:50,301 INFO: Done:	 Determine validation split
-2016-09-06 10:37:50,301 INFO: Start:	 Determine 5 folds
-2016-09-06 10:37:50,308 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:37:50,308 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:37:50,308 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:37:50,308 INFO: Done:	 Determine folds
-2016-09-06 10:37:50,308 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:37:50,308 INFO: Start:	 Classification
-2016-09-06 10:37:50,309 INFO: 	Start:	 Fold number 1
-2016-09-06 10:37:50,310 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:37:50,310 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:37:50,310 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:37:50,310 INFO: Done:	 Determine folds
-2016-09-06 10:37:50,310 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:37:50,310 INFO: Start:	 Classification
-2016-09-06 10:37:50,310 INFO: 	Start:	 Fold number 1
-2016-09-06 10:37:50,340 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:37:50,348 DEBUG: 			View 0 : 0.431952662722
-2016-09-06 10:37:50,355 DEBUG: 			View 1 : 0.431952662722
-2016-09-06 10:37:50,363 DEBUG: 			View 2 : 0.431952662722
-2016-09-06 10:37:50,370 DEBUG: 			View 3 : 0.431952662722
-2016-09-06 10:37:50,371 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 10:37:50,403 DEBUG: 			 Best view : 		View0
-2016-09-06 10:37:50,482 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:37:50,489 DEBUG: 			View 0 : 0.721893491124
-2016-09-06 10:37:50,497 DEBUG: 			View 1 : 0.710059171598
-2016-09-06 10:37:50,505 DEBUG: 			View 2 : 0.680473372781
-2016-09-06 10:37:50,513 DEBUG: 			View 3 : 0.739644970414
-2016-09-06 10:37:50,551 DEBUG: 			 Best view : 		View3
-2016-09-06 10:37:50,697 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:37:50,712 DEBUG: 			View 0 : 0.721893491124
-2016-09-06 10:37:50,722 DEBUG: 			View 1 : 0.710059171598
-2016-09-06 10:37:50,730 DEBUG: 			View 2 : 0.680473372781
-2016-09-06 10:37:50,737 DEBUG: 			View 3 : 0.739644970414
-2016-09-06 10:37:50,776 DEBUG: 			 Best view : 		View3
-2016-09-06 10:37:50,980 INFO: 	Start: 	 Classification
-2016-09-06 10:37:51,321 INFO: 	Done: 	 Fold number 1
-2016-09-06 10:37:51,321 INFO: 	Start:	 Fold number 2
-2016-09-06 10:37:51,351 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:37:51,358 DEBUG: 			View 0 : 0.431952662722
-2016-09-06 10:37:51,365 DEBUG: 			View 1 : 0.431952662722
-2016-09-06 10:37:51,372 DEBUG: 			View 2 : 0.431952662722
-2016-09-06 10:37:51,380 DEBUG: 			View 3 : 0.431952662722
-2016-09-06 10:37:51,380 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 10:37:51,410 DEBUG: 			 Best view : 		View0
-2016-09-06 10:37:51,489 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:37:51,496 DEBUG: 			View 0 : 0.710059171598
-2016-09-06 10:37:51,504 DEBUG: 			View 1 : 0.644970414201
-2016-09-06 10:37:51,511 DEBUG: 			View 2 : 0.721893491124
-2016-09-06 10:37:51,518 DEBUG: 			View 3 : 0.674556213018
-2016-09-06 10:37:51,555 DEBUG: 			 Best view : 		View2
-2016-09-06 10:37:51,694 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:37:51,701 DEBUG: 			View 0 : 0.710059171598
-2016-09-06 10:37:51,709 DEBUG: 			View 1 : 0.644970414201
-2016-09-06 10:37:51,716 DEBUG: 			View 2 : 0.721893491124
-2016-09-06 10:37:51,723 DEBUG: 			View 3 : 0.674556213018
-2016-09-06 10:37:51,762 DEBUG: 			 Best view : 		View2
-2016-09-06 10:37:51,964 INFO: 	Start: 	 Classification
-2016-09-06 10:37:52,305 INFO: 	Done: 	 Fold number 2
-2016-09-06 10:37:52,305 INFO: 	Start:	 Fold number 3
-2016-09-06 10:37:52,333 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:37:52,340 DEBUG: 			View 0 : 0.414201183432
-2016-09-06 10:37:52,347 DEBUG: 			View 1 : 0.467455621302
-2016-09-06 10:37:52,355 DEBUG: 			View 2 : 0.461538461538
-2016-09-06 10:37:52,361 DEBUG: 			View 3 : 0.514792899408
-2016-09-06 10:37:52,391 DEBUG: 			 Best view : 		View2
-2016-09-06 10:37:52,466 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:37:52,473 DEBUG: 			View 0 : 0.710059171598
-2016-09-06 10:37:52,481 DEBUG: 			View 1 : 0.721893491124
-2016-09-06 10:37:52,489 DEBUG: 			View 2 : 0.704142011834
-2016-09-06 10:37:52,495 DEBUG: 			View 3 : 0.733727810651
-2016-09-06 10:37:52,532 DEBUG: 			 Best view : 		View3
-2016-09-06 10:37:52,672 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:37:52,679 DEBUG: 			View 0 : 0.710059171598
-2016-09-06 10:37:52,686 DEBUG: 			View 1 : 0.721893491124
-2016-09-06 10:37:52,694 DEBUG: 			View 2 : 0.704142011834
-2016-09-06 10:37:52,701 DEBUG: 			View 3 : 0.733727810651
-2016-09-06 10:37:52,739 DEBUG: 			 Best view : 		View3
-2016-09-06 10:37:52,941 INFO: 	Start: 	 Classification
-2016-09-06 10:37:53,279 INFO: 	Done: 	 Fold number 3
-2016-09-06 10:37:53,279 INFO: 	Start:	 Fold number 4
-2016-09-06 10:37:53,308 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:37:53,315 DEBUG: 			View 0 : 0.568047337278
-2016-09-06 10:37:53,322 DEBUG: 			View 1 : 0.568047337278
-2016-09-06 10:37:53,328 DEBUG: 			View 2 : 0.568047337278
-2016-09-06 10:37:53,335 DEBUG: 			View 3 : 0.568047337278
-2016-09-06 10:37:53,365 DEBUG: 			 Best view : 		View0
-2016-09-06 10:37:53,439 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:37:53,446 DEBUG: 			View 0 : 0.662721893491
-2016-09-06 10:37:53,454 DEBUG: 			View 1 : 0.715976331361
-2016-09-06 10:37:53,461 DEBUG: 			View 2 : 0.739644970414
-2016-09-06 10:37:53,468 DEBUG: 			View 3 : 0.692307692308
-2016-09-06 10:37:53,504 DEBUG: 			 Best view : 		View2
-2016-09-06 10:37:53,643 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:37:53,650 DEBUG: 			View 0 : 0.662721893491
-2016-09-06 10:37:53,658 DEBUG: 			View 1 : 0.715976331361
-2016-09-06 10:37:53,665 DEBUG: 			View 2 : 0.739644970414
-2016-09-06 10:37:53,673 DEBUG: 			View 3 : 0.692307692308
-2016-09-06 10:37:53,711 DEBUG: 			 Best view : 		View2
-2016-09-06 10:37:53,914 INFO: 	Start: 	 Classification
-2016-09-06 10:37:54,254 INFO: 	Done: 	 Fold number 4
-2016-09-06 10:37:54,254 INFO: 	Start:	 Fold number 5
-2016-09-06 10:37:54,282 DEBUG: 		Start:	 Iteration 1
-2016-09-06 10:37:54,289 DEBUG: 			View 0 : 0.431952662722
-2016-09-06 10:37:54,296 DEBUG: 			View 1 : 0.431952662722
-2016-09-06 10:37:54,303 DEBUG: 			View 2 : 0.431952662722
-2016-09-06 10:37:54,309 DEBUG: 			View 3 : 0.431952662722
-2016-09-06 10:37:54,310 WARNING: 		WARNING:	All bad for iteration 0
-2016-09-06 10:37:54,340 DEBUG: 			 Best view : 		View0
-2016-09-06 10:37:54,415 DEBUG: 		Start:	 Iteration 2
-2016-09-06 10:37:54,423 DEBUG: 			View 0 : 0.656804733728
-2016-09-06 10:37:54,430 DEBUG: 			View 1 : 0.733727810651
-2016-09-06 10:37:54,437 DEBUG: 			View 2 : 0.692307692308
-2016-09-06 10:37:54,444 DEBUG: 			View 3 : 0.710059171598
-2016-09-06 10:37:54,480 DEBUG: 			 Best view : 		View1
-2016-09-06 10:37:54,620 DEBUG: 		Start:	 Iteration 3
-2016-09-06 10:37:54,627 DEBUG: 			View 0 : 0.656804733728
-2016-09-06 10:37:54,634 DEBUG: 			View 1 : 0.733727810651
-2016-09-06 10:37:54,642 DEBUG: 			View 2 : 0.692307692308
-2016-09-06 10:37:54,649 DEBUG: 			View 3 : 0.710059171598
-2016-09-06 10:37:54,688 DEBUG: 			 Best view : 		View1
-2016-09-06 10:37:54,891 INFO: 	Start: 	 Classification
-2016-09-06 10:37:55,231 INFO: 	Done: 	 Fold number 5
-2016-09-06 10:37:55,231 INFO: Done:	 Classification
-2016-09-06 10:37:55,231 INFO: Info:	 Time for Classification: 4[s]
-2016-09-06 10:37:55,231 INFO: Start:	 Result Analysis for Mumbo
-2016-09-06 10:37:57,067 INFO: 		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 73.2544378698
-	-On Test : 46.1904761905
-	-On Validation : 57.0786516854Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 5), View1 of shape (300, 12), View2 of shape (300, 14), View3 of shape (300, 5)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:01        0:00:00
-	         Fold 3        0:00:02        0:00:00
-	         Fold 4        0:00:03        0:00:00
-	         Fold 5        0:00:04        0:00:00
-	          Total        0:00:13        0:00:01
-	So a total classification time of 0:00:04.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.187573964497
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.185207100592
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.179289940828
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.191124260355
-			- Percentage of time chosen : 0.2
-	- Fold 1, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.185207100592
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.172189349112
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.187573964497
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.178106508876
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.183431952663
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.191124260355
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.186982248521
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.198224852071
-			- Percentage of time chosen : 0.2
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.189349112426
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.2
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.204733727811
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.195266272189
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.174556213018
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.189940828402
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.181656804734
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.185207100592
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 73.9644970414
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.2921348315
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 72.1893491124
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 73.3727810651
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 73.9644970414
-			Accuracy on test : 0.0
-			Accuracy on validation : 64.0449438202
-			Selected View : View2
-		 Fold 5
-			Accuracy on train : 73.3727810651
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.6853932584
-			Selected View : View1
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 73.9644970414
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.2921348315
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 72.1893491124
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 73.3727810651
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 73.3727810651
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.6741573034
-			Selected View : View2
-		 Fold 5
-			Accuracy on train : 73.3727810651
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.6853932584
-			Selected View : View1
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-2016-09-06 10:37:57,248 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e15b39c3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d6b6f1ee..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.738095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8033aa1c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.67619047619
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 594945a8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.766666666667
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.766666666667
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 63c6ae28..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8b456925..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 00b45eb3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.366666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.366666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c2d3d627..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d49ccc57..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 96bde0ec..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.690476190476
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.690476190476
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 40e8b33c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.690476190476
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.690476190476
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 61507bca..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.833333333333
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.833333333333
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f8a94e87..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8ebd07f4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cb5ad814..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e113ccef..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d2a1e680..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d8ec68e9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.742857142857
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.742857142857
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a85fe8bb..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.709523809524
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.709523809524
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bd9b6ea7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.8
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.8
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0baeab05..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5d435b56..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 78a5329c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8f3e16ae..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c6c250a5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 367caf75..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 4648
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
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diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 65472fd4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,209 +0,0 @@
-		Result for Multiview classification with Mumbo
-
-Average accuracy :
-	-On Train : 73.2544378698
-	-On Test : 46.1904761905
-	-On Validation : 57.0786516854Dataset info :
-	-Dataset name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : View0 of shape (300, 5), View1 of shape (300, 12), View2 of shape (300, 14), View3 of shape (300, 5)
-	-5 folds
-	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration : 
-	-Used 1 core(s)
-	-Iterations : min 1, max 10, threshold 0.01
-	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
-		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:01        0:00:00
-	         Fold 3        0:00:02        0:00:00
-	         Fold 4        0:00:03        0:00:00
-	         Fold 5        0:00:04        0:00:00
-	          Total        0:00:13        0:00:01
-	So a total classification time of 0:00:04.
-
-
-
-Mean average accuracies and stats for each fold :
-	- Fold 0, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.187573964497
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.185207100592
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.179289940828
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.191124260355
-			- Percentage of time chosen : 0.2
-	- Fold 1, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.185207100592
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.172189349112
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.187573964497
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.178106508876
-			- Percentage of time chosen : 0.0
-	- Fold 2, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.183431952663
-			- Percentage of time chosen : 0.7
-		- On View1 : 
-			- Mean average Accuracy : 0.191124260355
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.186982248521
-			- Percentage of time chosen : 0.1
-		- On View3 : 
-			- Mean average Accuracy : 0.198224852071
-			- Percentage of time chosen : 0.2
-	- Fold 3, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.189349112426
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.2
-			- Percentage of time chosen : 0.0
-		- On View2 : 
-			- Mean average Accuracy : 0.204733727811
-			- Percentage of time chosen : 0.2
-		- On View3 : 
-			- Mean average Accuracy : 0.195266272189
-			- Percentage of time chosen : 0.0
-	- Fold 4, used 4
-		- On View0 : 
-			- Mean average Accuracy : 0.174556213018
-			- Percentage of time chosen : 0.8
-		- On View1 : 
-			- Mean average Accuracy : 0.189940828402
-			- Percentage of time chosen : 0.2
-		- On View2 : 
-			- Mean average Accuracy : 0.181656804734
-			- Percentage of time chosen : 0.0
-		- On View3 : 
-			- Mean average Accuracy : 0.185207100592
-			- Percentage of time chosen : 0.0
-
- For each iteration : 
-	- Iteration 1
-		 Fold 1
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View2
-		 Fold 4
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-	- Iteration 2
-		 Fold 1
-			Accuracy on train : 73.9644970414
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.2921348315
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 72.1893491124
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 73.3727810651
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 73.9644970414
-			Accuracy on test : 0.0
-			Accuracy on validation : 64.0449438202
-			Selected View : View2
-		 Fold 5
-			Accuracy on train : 73.3727810651
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.6853932584
-			Selected View : View1
-	- Iteration 3
-		 Fold 1
-			Accuracy on train : 73.9644970414
-			Accuracy on test : 0.0
-			Accuracy on validation : 66.2921348315
-			Selected View : View3
-		 Fold 2
-			Accuracy on train : 72.1893491124
-			Accuracy on test : 0.0
-			Accuracy on validation : 48.3146067416
-			Selected View : View2
-		 Fold 3
-			Accuracy on train : 73.3727810651
-			Accuracy on test : 0.0
-			Accuracy on validation : 58.4269662921
-			Selected View : View3
-		 Fold 4
-			Accuracy on train : 73.3727810651
-			Accuracy on test : 0.0
-			Accuracy on validation : 60.6741573034
-			Selected View : View2
-		 Fold 5
-			Accuracy on train : 73.3727810651
-			Accuracy on test : 0.0
-			Accuracy on validation : 51.6853932584
-			Selected View : View1
-	- Iteration 4
-		 Fold 1
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 2
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 3
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 4
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
-		 Fold 5
-			Accuracy on train : 56.8047337278
-			Accuracy on test : 0.0
-			Accuracy on validation : 57.3033707865
-			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index bc6f192d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1250 +0,0 @@
-2016-09-06 10:39:29,831 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:39:29,831 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000170875 Gbytes /!\ 
-2016-09-06 10:39:34,843 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:39:34,844 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:39:34,896 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:34,896 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:39:34,896 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:34,897 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:34,897 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:39:34,897 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:34,897 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:34,898 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:34,898 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:34,898 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:34,898 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:34,898 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:34,899 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:34,899 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:34,942 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:34,943 DEBUG: Start:	 Training
-2016-09-06 10:39:34,946 DEBUG: Info:	 Time for Training: 0.0496671199799[s]
-2016-09-06 10:39:34,946 DEBUG: Done:	 Training
-2016-09-06 10:39:34,946 DEBUG: Start:	 Predicting
-2016-09-06 10:39:34,948 DEBUG: Done:	 Predicting
-2016-09-06 10:39:34,949 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:34,950 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:34,950 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:39:34,950 INFO: Done:	 Result Analysis
-2016-09-06 10:39:34,952 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:34,952 DEBUG: Start:	 Training
-2016-09-06 10:39:34,957 DEBUG: Info:	 Time for Training: 0.0618000030518[s]
-2016-09-06 10:39:34,957 DEBUG: Done:	 Training
-2016-09-06 10:39:34,957 DEBUG: Start:	 Predicting
-2016-09-06 10:39:34,960 DEBUG: Done:	 Predicting
-2016-09-06 10:39:34,961 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:34,962 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:34,963 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:34,963 INFO: Done:	 Result Analysis
-2016-09-06 10:39:35,046 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,046 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,046 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:39:35,046 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:39:35,046 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:35,046 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:35,047 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,047 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,047 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,047 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,047 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:35,047 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:35,047 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,047 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,081 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:35,081 DEBUG: Start:	 Training
-2016-09-06 10:39:35,081 DEBUG: Info:	 Time for Training: 0.0358710289001[s]
-2016-09-06 10:39:35,081 DEBUG: Done:	 Training
-2016-09-06 10:39:35,081 DEBUG: Start:	 Predicting
-2016-09-06 10:39:35,089 DEBUG: Done:	 Predicting
-2016-09-06 10:39:35,089 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:35,091 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:35,091 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,091 INFO: Done:	 Result Analysis
-2016-09-06 10:39:35,328 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:35,328 DEBUG: Start:	 Training
-2016-09-06 10:39:35,368 DEBUG: Info:	 Time for Training: 0.322957992554[s]
-2016-09-06 10:39:35,369 DEBUG: Done:	 Training
-2016-09-06 10:39:35,369 DEBUG: Start:	 Predicting
-2016-09-06 10:39:35,374 DEBUG: Done:	 Predicting
-2016-09-06 10:39:35,374 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:35,376 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:35,376 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 15, max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,376 INFO: Done:	 Result Analysis
-2016-09-06 10:39:35,496 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,496 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,496 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:39:35,496 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:39:35,497 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:35,497 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:35,498 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,498 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,498 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,498 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,498 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:35,498 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:35,498 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,498 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,546 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:35,546 DEBUG: Start:	 Training
-2016-09-06 10:39:35,547 DEBUG: Info:	 Time for Training: 0.0519301891327[s]
-2016-09-06 10:39:35,547 DEBUG: Done:	 Training
-2016-09-06 10:39:35,547 DEBUG: Start:	 Predicting
-2016-09-06 10:39:35,553 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:35,554 DEBUG: Start:	 Training
-2016-09-06 10:39:35,565 DEBUG: Done:	 Predicting
-2016-09-06 10:39:35,565 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:35,567 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:35,567 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,567 INFO: Done:	 Result Analysis
-2016-09-06 10:39:35,575 DEBUG: Info:	 Time for Training: 0.0798330307007[s]
-2016-09-06 10:39:35,575 DEBUG: Done:	 Training
-2016-09-06 10:39:35,575 DEBUG: Start:	 Predicting
-2016-09-06 10:39:35,579 DEBUG: Done:	 Predicting
-2016-09-06 10:39:35,579 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:35,581 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:35,581 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,581 INFO: Done:	 Result Analysis
-2016-09-06 10:39:35,643 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,643 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,644 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:39:35,644 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:39:35,644 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:35,644 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:35,645 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,645 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,645 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,646 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:35,646 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,646 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,646 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:35,646 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,698 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:35,699 DEBUG: Start:	 Training
-2016-09-06 10:39:35,701 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:35,701 DEBUG: Start:	 Training
-2016-09-06 10:39:35,716 DEBUG: Info:	 Time for Training: 0.0735998153687[s]
-2016-09-06 10:39:35,717 DEBUG: Done:	 Training
-2016-09-06 10:39:35,717 DEBUG: Start:	 Predicting
-2016-09-06 10:39:35,720 DEBUG: Info:	 Time for Training: 0.0770709514618[s]
-2016-09-06 10:39:35,720 DEBUG: Done:	 Training
-2016-09-06 10:39:35,720 DEBUG: Start:	 Predicting
-2016-09-06 10:39:35,723 DEBUG: Done:	 Predicting
-2016-09-06 10:39:35,723 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:35,724 DEBUG: Done:	 Predicting
-2016-09-06 10:39:35,724 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:35,724 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,724 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:35,724 INFO: Done:	 Result Analysis
-2016-09-06 10:39:35,726 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:35,726 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,726 INFO: Done:	 Result Analysis
-2016-09-06 10:39:35,791 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,791 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,791 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:39:35,791 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:39:35,791 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:35,791 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:35,792 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:35,792 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:35,792 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:35,792 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:35,792 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:35,792 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:35,792 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,792 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,826 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:35,827 DEBUG: Start:	 Training
-2016-09-06 10:39:35,828 DEBUG: Info:	 Time for Training: 0.0375211238861[s]
-2016-09-06 10:39:35,828 DEBUG: Done:	 Training
-2016-09-06 10:39:35,828 DEBUG: Start:	 Predicting
-2016-09-06 10:39:35,831 DEBUG: Done:	 Predicting
-2016-09-06 10:39:35,831 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:35,832 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:35,832 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,833 INFO: Done:	 Result Analysis
-2016-09-06 10:39:35,840 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:35,840 DEBUG: Start:	 Training
-2016-09-06 10:39:35,843 DEBUG: Info:	 Time for Training: 0.0527670383453[s]
-2016-09-06 10:39:35,843 DEBUG: Done:	 Training
-2016-09-06 10:39:35,843 DEBUG: Start:	 Predicting
-2016-09-06 10:39:35,846 DEBUG: Done:	 Predicting
-2016-09-06 10:39:35,846 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:35,848 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:35,848 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,848 INFO: Done:	 Result Analysis
-2016-09-06 10:39:35,945 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,945 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,945 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:39:35,945 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:39:35,945 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:35,945 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:35,946 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:35,946 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:35,947 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:35,947 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:35,947 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:35,947 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:35,947 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,947 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,998 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:35,998 DEBUG: Start:	 Training
-2016-09-06 10:39:35,999 DEBUG: Info:	 Time for Training: 0.0549540519714[s]
-2016-09-06 10:39:35,999 DEBUG: Done:	 Training
-2016-09-06 10:39:35,999 DEBUG: Start:	 Predicting
-2016-09-06 10:39:36,008 DEBUG: Done:	 Predicting
-2016-09-06 10:39:36,008 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:36,011 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:36,011 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,011 INFO: Done:	 Result Analysis
-2016-09-06 10:39:36,240 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:36,240 DEBUG: Start:	 Training
-2016-09-06 10:39:36,279 DEBUG: Info:	 Time for Training: 0.334888935089[s]
-2016-09-06 10:39:36,279 DEBUG: Done:	 Training
-2016-09-06 10:39:36,279 DEBUG: Start:	 Predicting
-2016-09-06 10:39:36,284 DEBUG: Done:	 Predicting
-2016-09-06 10:39:36,285 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:36,286 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:36,286 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 15, max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,286 INFO: Done:	 Result Analysis
-2016-09-06 10:39:36,394 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,394 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,394 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:39:36,394 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:39:36,394 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:36,394 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:36,395 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:36,395 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:36,395 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:36,395 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:36,395 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:36,395 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,395 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:36,396 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,441 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:36,441 DEBUG: Start:	 Training
-2016-09-06 10:39:36,442 DEBUG: Info:	 Time for Training: 0.0489950180054[s]
-2016-09-06 10:39:36,442 DEBUG: Done:	 Training
-2016-09-06 10:39:36,442 DEBUG: Start:	 Predicting
-2016-09-06 10:39:36,445 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:36,445 DEBUG: Start:	 Training
-2016-09-06 10:39:36,463 DEBUG: Info:	 Time for Training: 0.0693230628967[s]
-2016-09-06 10:39:36,463 DEBUG: Done:	 Training
-2016-09-06 10:39:36,463 DEBUG: Start:	 Predicting
-2016-09-06 10:39:36,466 DEBUG: Done:	 Predicting
-2016-09-06 10:39:36,466 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:36,467 DEBUG: Done:	 Predicting
-2016-09-06 10:39:36,467 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:36,468 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:36,469 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,469 INFO: Done:	 Result Analysis
-2016-09-06 10:39:36,469 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:36,469 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.377777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,470 INFO: Done:	 Result Analysis
-2016-09-06 10:39:36,547 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,547 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:39:36,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:39:36,548 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:36,548 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:36,550 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:36,550 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:36,550 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:36,550 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:36,550 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:36,550 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:36,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,603 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:36,603 DEBUG: Start:	 Training
-2016-09-06 10:39:36,613 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:36,614 DEBUG: Start:	 Training
-2016-09-06 10:39:36,619 DEBUG: Info:	 Time for Training: 0.0724880695343[s]
-2016-09-06 10:39:36,619 DEBUG: Done:	 Training
-2016-09-06 10:39:36,619 DEBUG: Start:	 Predicting
-2016-09-06 10:39:36,624 DEBUG: Done:	 Predicting
-2016-09-06 10:39:36,624 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:36,626 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:36,626 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,626 INFO: Done:	 Result Analysis
-2016-09-06 10:39:36,631 DEBUG: Info:	 Time for Training: 0.0851111412048[s]
-2016-09-06 10:39:36,631 DEBUG: Done:	 Training
-2016-09-06 10:39:36,632 DEBUG: Start:	 Predicting
-2016-09-06 10:39:36,635 DEBUG: Done:	 Predicting
-2016-09-06 10:39:36,635 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:36,637 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:36,637 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,638 INFO: Done:	 Result Analysis
-2016-09-06 10:39:36,795 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,795 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:39:36,795 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:36,796 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,796 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:36,797 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:39:36,797 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:36,797 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:36,797 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:36,797 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,798 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:36,798 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:36,798 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:36,798 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,840 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:36,840 DEBUG: Start:	 Training
-2016-09-06 10:39:36,844 DEBUG: Info:	 Time for Training: 0.0481259822845[s]
-2016-09-06 10:39:36,844 DEBUG: Done:	 Training
-2016-09-06 10:39:36,844 DEBUG: Start:	 Predicting
-2016-09-06 10:39:36,846 DEBUG: Done:	 Predicting
-2016-09-06 10:39:36,847 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:36,848 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:36,848 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,848 INFO: Done:	 Result Analysis
-2016-09-06 10:39:36,851 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:36,851 DEBUG: Start:	 Training
-2016-09-06 10:39:36,856 DEBUG: Info:	 Time for Training: 0.0621771812439[s]
-2016-09-06 10:39:36,856 DEBUG: Done:	 Training
-2016-09-06 10:39:36,856 DEBUG: Start:	 Predicting
-2016-09-06 10:39:36,859 DEBUG: Done:	 Predicting
-2016-09-06 10:39:36,860 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:36,862 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:36,862 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,862 INFO: Done:	 Result Analysis
-2016-09-06 10:39:36,939 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,939 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,940 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:39:36,940 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:39:36,940 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:36,940 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:36,940 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:36,940 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:36,940 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:36,940 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:36,940 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:36,940 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:36,941 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,941 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,974 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:36,974 DEBUG: Start:	 Training
-2016-09-06 10:39:36,974 DEBUG: Info:	 Time for Training: 0.0355970859528[s]
-2016-09-06 10:39:36,975 DEBUG: Done:	 Training
-2016-09-06 10:39:36,975 DEBUG: Start:	 Predicting
-2016-09-06 10:39:36,983 DEBUG: Done:	 Predicting
-2016-09-06 10:39:36,983 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:36,984 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:36,984 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,984 INFO: Done:	 Result Analysis
-2016-09-06 10:39:37,218 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:37,219 DEBUG: Start:	 Training
-2016-09-06 10:39:37,260 DEBUG: Info:	 Time for Training: 0.320749998093[s]
-2016-09-06 10:39:37,260 DEBUG: Done:	 Training
-2016-09-06 10:39:37,260 DEBUG: Start:	 Predicting
-2016-09-06 10:39:37,265 DEBUG: Done:	 Predicting
-2016-09-06 10:39:37,265 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:37,267 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:37,267 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 15, max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,267 INFO: Done:	 Result Analysis
-2016-09-06 10:39:37,387 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,387 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,387 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:39:37,387 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:39:37,387 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:37,387 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:37,388 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:37,388 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:37,388 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:37,388 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:37,388 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:37,388 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:37,388 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,388 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,439 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:37,439 DEBUG: Start:	 Training
-2016-09-06 10:39:37,440 DEBUG: Info:	 Time for Training: 0.0537350177765[s]
-2016-09-06 10:39:37,440 DEBUG: Done:	 Training
-2016-09-06 10:39:37,440 DEBUG: Start:	 Predicting
-2016-09-06 10:39:37,440 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:37,440 DEBUG: Start:	 Training
-2016-09-06 10:39:37,460 DEBUG: Done:	 Predicting
-2016-09-06 10:39:37,461 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:37,464 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:37,464 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,465 INFO: Done:	 Result Analysis
-2016-09-06 10:39:37,469 DEBUG: Info:	 Time for Training: 0.082640171051[s]
-2016-09-06 10:39:37,469 DEBUG: Done:	 Training
-2016-09-06 10:39:37,469 DEBUG: Start:	 Predicting
-2016-09-06 10:39:37,473 DEBUG: Done:	 Predicting
-2016-09-06 10:39:37,473 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:37,474 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:37,474 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,475 INFO: Done:	 Result Analysis
-2016-09-06 10:39:37,532 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,532 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,532 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:39:37,532 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:39:37,532 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:37,532 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:37,533 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:37,533 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:37,533 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:37,533 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:37,533 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:37,533 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:37,533 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,533 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,582 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:37,582 DEBUG: Start:	 Training
-2016-09-06 10:39:37,587 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:37,588 DEBUG: Start:	 Training
-2016-09-06 10:39:37,601 DEBUG: Info:	 Time for Training: 0.0697820186615[s]
-2016-09-06 10:39:37,601 DEBUG: Done:	 Training
-2016-09-06 10:39:37,601 DEBUG: Start:	 Predicting
-2016-09-06 10:39:37,607 DEBUG: Info:	 Time for Training: 0.0757231712341[s]
-2016-09-06 10:39:37,607 DEBUG: Done:	 Training
-2016-09-06 10:39:37,607 DEBUG: Start:	 Predicting
-2016-09-06 10:39:37,607 DEBUG: Done:	 Predicting
-2016-09-06 10:39:37,607 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:37,609 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:37,609 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,609 INFO: Done:	 Result Analysis
-2016-09-06 10:39:37,611 DEBUG: Done:	 Predicting
-2016-09-06 10:39:37,611 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:37,613 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:37,613 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,613 INFO: Done:	 Result Analysis
-2016-09-06 10:39:37,681 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,681 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,681 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:39:37,681 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:39:37,682 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:37,682 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:37,682 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:37,682 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:37,683 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:37,683 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:37,683 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:37,683 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,683 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:37,683 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,723 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:37,723 DEBUG: Start:	 Training
-2016-09-06 10:39:37,725 DEBUG: Info:	 Time for Training: 0.0450918674469[s]
-2016-09-06 10:39:37,726 DEBUG: Done:	 Training
-2016-09-06 10:39:37,726 DEBUG: Start:	 Predicting
-2016-09-06 10:39:37,728 DEBUG: Done:	 Predicting
-2016-09-06 10:39:37,728 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:37,730 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:37,730 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,730 INFO: Done:	 Result Analysis
-2016-09-06 10:39:37,739 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:37,739 DEBUG: Start:	 Training
-2016-09-06 10:39:37,743 DEBUG: Info:	 Time for Training: 0.0631110668182[s]
-2016-09-06 10:39:37,744 DEBUG: Done:	 Training
-2016-09-06 10:39:37,744 DEBUG: Start:	 Predicting
-2016-09-06 10:39:37,746 DEBUG: Done:	 Predicting
-2016-09-06 10:39:37,747 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:37,748 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:37,749 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,749 INFO: Done:	 Result Analysis
-2016-09-06 10:39:37,835 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,835 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:39:37,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:39:37,836 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:37,836 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:37,836 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:37,837 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:37,837 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:37,837 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:37,837 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,837 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:37,837 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:37,837 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,875 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:37,875 DEBUG: Start:	 Training
-2016-09-06 10:39:37,876 DEBUG: Info:	 Time for Training: 0.0416429042816[s]
-2016-09-06 10:39:37,876 DEBUG: Done:	 Training
-2016-09-06 10:39:37,877 DEBUG: Start:	 Predicting
-2016-09-06 10:39:37,885 DEBUG: Done:	 Predicting
-2016-09-06 10:39:37,886 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:37,887 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:37,887 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,887 INFO: Done:	 Result Analysis
-2016-09-06 10:39:38,178 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:38,178 DEBUG: Start:	 Training
-2016-09-06 10:39:38,218 DEBUG: Info:	 Time for Training: 0.383457183838[s]
-2016-09-06 10:39:38,218 DEBUG: Done:	 Training
-2016-09-06 10:39:38,218 DEBUG: Start:	 Predicting
-2016-09-06 10:39:38,224 DEBUG: Done:	 Predicting
-2016-09-06 10:39:38,224 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:38,225 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:38,226 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 15, max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:38,226 INFO: Done:	 Result Analysis
-2016-09-06 10:39:38,289 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:38,289 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:38,289 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:39:38,289 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:39:38,289 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:38,289 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:39:38,290 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:38,290 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:38,291 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:38,291 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:38,291 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:38,291 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:39:38,291 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:38,291 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:39:38,363 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:38,363 DEBUG: Start:	 Training
-2016-09-06 10:39:38,365 DEBUG: Info:	 Time for Training: 0.076936006546[s]
-2016-09-06 10:39:38,365 DEBUG: Done:	 Training
-2016-09-06 10:39:38,365 DEBUG: Start:	 Predicting
-2016-09-06 10:39:38,371 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:39:38,371 DEBUG: Start:	 Training
-2016-09-06 10:39:38,377 DEBUG: Done:	 Predicting
-2016-09-06 10:39:38,378 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:38,380 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:38,380 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:38,380 INFO: Done:	 Result Analysis
-2016-09-06 10:39:38,396 DEBUG: Info:	 Time for Training: 0.108675003052[s]
-2016-09-06 10:39:38,397 DEBUG: Done:	 Training
-2016-09-06 10:39:38,397 DEBUG: Start:	 Predicting
-2016-09-06 10:39:38,400 DEBUG: Done:	 Predicting
-2016-09-06 10:39:38,401 DEBUG: Start:	 Getting Results
-2016-09-06 10:39:38,402 DEBUG: Done:	 Getting Results
-2016-09-06 10:39:38,402 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.409523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:39:38,402 INFO: Done:	 Result Analysis
-2016-09-06 10:39:38,680 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:39:38,680 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:39:38,681 INFO: Info:	 Shape of View0 :(300, 18)
-2016-09-06 10:39:38,681 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:39:38,682 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:39:38,682 INFO: Info:	 Shape of View1 :(300, 8)
-2016-09-06 10:39:38,683 INFO: Info:	 Shape of View0 :(300, 18)
-2016-09-06 10:39:38,683 INFO: Info:	 Shape of View2 :(300, 19)
-2016-09-06 10:39:38,684 INFO: Info:	 Shape of View1 :(300, 8)
-2016-09-06 10:39:38,684 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 10:39:38,684 INFO: Done:	 Read Database Files
-2016-09-06 10:39:38,684 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:39:38,684 INFO: Info:	 Shape of View2 :(300, 19)
-2016-09-06 10:39:38,685 INFO: Info:	 Shape of View3 :(300, 20)
-2016-09-06 10:39:38,685 INFO: Done:	 Read Database Files
-2016-09-06 10:39:38,685 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:39:38,691 INFO: Done:	 Determine validation split
-2016-09-06 10:39:38,691 INFO: Start:	 Determine 5 folds
-2016-09-06 10:39:38,691 INFO: Done:	 Determine validation split
-2016-09-06 10:39:38,692 INFO: Start:	 Determine 5 folds
-2016-09-06 10:39:38,707 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:39:38,707 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:39:38,707 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:39:38,707 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 10:39:38,707 INFO: Done:	 Determine folds
-2016-09-06 10:39:38,707 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 10:39:38,707 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:39:38,707 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:39:38,708 INFO: Done:	 Determine folds
-2016-09-06 10:39:38,707 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 10:39:38,708 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:39:38,708 DEBUG: 	Start:	 Random search for Adaboost with 30 iterations
-2016-09-06 10:39:38,708 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 10:39:38,708 DEBUG: 	Start:	 Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7c2f9b7f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 225bfa22..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ce956518..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e4d36bfb..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9d6ed1f4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ce22fa3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 15, max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 00f4dc53..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cdf61b88..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5a41a5a4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6ccf3592..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 13e69855..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 50dc139e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3df2f276..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c50be0fa..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 15, max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9737bc60..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 37755036..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.377777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c60418d9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 61f4324f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 291654d0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d4f31147..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2f3557c2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 43
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 38a3f4d6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 15, max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8c91b519..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 006bed3d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9d41e795..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 482a6b43..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9df5e00e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 15, max_depth : 11
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 69d5a4ea..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 241a8b39..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.409523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7957
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 12848a0f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1250 +0,0 @@
-2016-09-06 10:40:15,413 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:40:15,414 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000114625 Gbytes /!\ 
-2016-09-06 10:40:20,424 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 10:40:20,425 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 10:40:20,483 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:20,483 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:20,484 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:40:20,484 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:40:20,484 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:20,484 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:20,485 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:20,485 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:20,485 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:20,485 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:20,485 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:20,485 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:20,485 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:20,486 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:20,546 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:20,546 DEBUG: Start:	 Training
-2016-09-06 10:40:20,549 DEBUG: Info:	 Time for Training: 0.0666189193726[s]
-2016-09-06 10:40:20,549 DEBUG: Done:	 Training
-2016-09-06 10:40:20,549 DEBUG: Start:	 Predicting
-2016-09-06 10:40:20,553 DEBUG: Done:	 Predicting
-2016-09-06 10:40:20,553 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:20,556 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:20,556 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.933333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.933333333333
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:20,556 INFO: Done:	 Result Analysis
-2016-09-06 10:40:20,558 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:20,558 DEBUG: Start:	 Training
-2016-09-06 10:40:20,565 DEBUG: Info:	 Time for Training: 0.0827748775482[s]
-2016-09-06 10:40:20,565 DEBUG: Done:	 Training
-2016-09-06 10:40:20,566 DEBUG: Start:	 Predicting
-2016-09-06 10:40:20,570 DEBUG: Done:	 Predicting
-2016-09-06 10:40:20,570 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:20,573 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:20,573 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:40:20,574 INFO: Done:	 Result Analysis
-2016-09-06 10:40:20,633 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:20,633 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:20,633 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:40:20,633 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:40:20,633 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:20,633 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:20,634 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:20,634 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:20,634 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:20,634 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:20,634 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:20,634 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:20,634 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:20,634 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:20,688 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:20,688 DEBUG: Start:	 Training
-2016-09-06 10:40:20,689 DEBUG: Info:	 Time for Training: 0.0570049285889[s]
-2016-09-06 10:40:20,689 DEBUG: Done:	 Training
-2016-09-06 10:40:20,689 DEBUG: Start:	 Predicting
-2016-09-06 10:40:20,698 DEBUG: Done:	 Predicting
-2016-09-06 10:40:20,698 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:20,700 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:20,701 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:40:20,701 INFO: Done:	 Result Analysis
-2016-09-06 10:40:21,198 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:21,198 DEBUG: Start:	 Training
-2016-09-06 10:40:21,275 DEBUG: Info:	 Time for Training: 0.643486976624[s]
-2016-09-06 10:40:21,276 DEBUG: Done:	 Training
-2016-09-06 10:40:21,276 DEBUG: Start:	 Predicting
-2016-09-06 10:40:21,285 DEBUG: Done:	 Predicting
-2016-09-06 10:40:21,286 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:21,287 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:21,287 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 29, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,287 INFO: Done:	 Result Analysis
-2016-09-06 10:40:21,386 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,386 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:40:21,386 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:21,387 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,387 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:21,387 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:21,387 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:40:21,388 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:21,388 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:21,388 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,389 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:21,389 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:21,389 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:21,389 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,433 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:21,433 DEBUG: Start:	 Training
-2016-09-06 10:40:21,434 DEBUG: Info:	 Time for Training: 0.0487298965454[s]
-2016-09-06 10:40:21,434 DEBUG: Done:	 Training
-2016-09-06 10:40:21,434 DEBUG: Start:	 Predicting
-2016-09-06 10:40:21,446 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:21,446 DEBUG: Start:	 Training
-2016-09-06 10:40:21,453 DEBUG: Done:	 Predicting
-2016-09-06 10:40:21,453 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:21,455 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:21,455 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,455 INFO: Done:	 Result Analysis
-2016-09-06 10:40:21,468 DEBUG: Info:	 Time for Training: 0.0820069313049[s]
-2016-09-06 10:40:21,468 DEBUG: Done:	 Training
-2016-09-06 10:40:21,468 DEBUG: Start:	 Predicting
-2016-09-06 10:40:21,472 DEBUG: Done:	 Predicting
-2016-09-06 10:40:21,472 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:21,473 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:21,473 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,473 INFO: Done:	 Result Analysis
-2016-09-06 10:40:21,527 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,527 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,527 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:40:21,527 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:40:21,527 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:21,527 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:21,528 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:21,528 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:21,528 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:21,528 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:21,528 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:21,528 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:21,529 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,529 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,580 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:21,580 DEBUG: Start:	 Training
-2016-09-06 10:40:21,590 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:21,590 DEBUG: Start:	 Training
-2016-09-06 10:40:21,597 DEBUG: Info:	 Time for Training: 0.0706040859222[s]
-2016-09-06 10:40:21,597 DEBUG: Done:	 Training
-2016-09-06 10:40:21,597 DEBUG: Start:	 Predicting
-2016-09-06 10:40:21,603 DEBUG: Done:	 Predicting
-2016-09-06 10:40:21,603 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:21,605 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:21,605 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,605 INFO: Done:	 Result Analysis
-2016-09-06 10:40:21,610 DEBUG: Info:	 Time for Training: 0.0829930305481[s]
-2016-09-06 10:40:21,610 DEBUG: Done:	 Training
-2016-09-06 10:40:21,610 DEBUG: Start:	 Predicting
-2016-09-06 10:40:21,614 DEBUG: Done:	 Predicting
-2016-09-06 10:40:21,614 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:21,615 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:21,615 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,615 INFO: Done:	 Result Analysis
-2016-09-06 10:40:21,678 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,678 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:40:21,678 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:21,678 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,679 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:40:21,679 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:21,679 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:21,679 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:21,679 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:21,679 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:21,679 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:21,679 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,679 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:21,679 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,715 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:21,715 DEBUG: Start:	 Training
-2016-09-06 10:40:21,717 DEBUG: Info:	 Time for Training: 0.0389919281006[s]
-2016-09-06 10:40:21,717 DEBUG: Done:	 Training
-2016-09-06 10:40:21,717 DEBUG: Start:	 Predicting
-2016-09-06 10:40:21,720 DEBUG: Done:	 Predicting
-2016-09-06 10:40:21,720 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:21,721 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:21,721 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.814285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.814285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,721 INFO: Done:	 Result Analysis
-2016-09-06 10:40:21,730 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:21,730 DEBUG: Start:	 Training
-2016-09-06 10:40:21,734 DEBUG: Info:	 Time for Training: 0.056380033493[s]
-2016-09-06 10:40:21,734 DEBUG: Done:	 Training
-2016-09-06 10:40:21,734 DEBUG: Start:	 Predicting
-2016-09-06 10:40:21,737 DEBUG: Done:	 Predicting
-2016-09-06 10:40:21,737 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:21,740 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:21,740 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,741 INFO: Done:	 Result Analysis
-2016-09-06 10:40:21,832 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,832 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,833 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:40:21,833 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:40:21,833 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:21,833 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:21,834 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:21,834 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:21,834 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:21,834 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:21,835 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:21,835 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:21,835 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,835 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,881 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:21,882 DEBUG: Start:	 Training
-2016-09-06 10:40:21,883 DEBUG: Info:	 Time for Training: 0.0516860485077[s]
-2016-09-06 10:40:21,883 DEBUG: Done:	 Training
-2016-09-06 10:40:21,883 DEBUG: Start:	 Predicting
-2016-09-06 10:40:21,889 DEBUG: Done:	 Predicting
-2016-09-06 10:40:21,890 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:21,892 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:21,892 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.652380952381
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,892 INFO: Done:	 Result Analysis
-2016-09-06 10:40:22,343 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:22,343 DEBUG: Start:	 Training
-2016-09-06 10:40:22,417 DEBUG: Info:	 Time for Training: 0.585797071457[s]
-2016-09-06 10:40:22,417 DEBUG: Done:	 Training
-2016-09-06 10:40:22,417 DEBUG: Start:	 Predicting
-2016-09-06 10:40:22,425 DEBUG: Done:	 Predicting
-2016-09-06 10:40:22,425 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:22,427 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:22,427 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 29, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:40:22,427 INFO: Done:	 Result Analysis
-2016-09-06 10:40:22,574 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,574 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,574 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:40:22,574 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:40:22,574 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:22,574 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:22,575 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:22,575 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:22,575 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:22,575 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:22,575 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:22,575 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:22,575 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:22,575 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:22,619 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:22,619 DEBUG: Start:	 Training
-2016-09-06 10:40:22,620 DEBUG: Info:	 Time for Training: 0.0461812019348[s]
-2016-09-06 10:40:22,620 DEBUG: Done:	 Training
-2016-09-06 10:40:22,620 DEBUG: Start:	 Predicting
-2016-09-06 10:40:22,624 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:22,624 DEBUG: Start:	 Training
-2016-09-06 10:40:22,642 DEBUG: Done:	 Predicting
-2016-09-06 10:40:22,642 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:22,643 DEBUG: Info:	 Time for Training: 0.0691771507263[s]
-2016-09-06 10:40:22,643 DEBUG: Done:	 Training
-2016-09-06 10:40:22,643 DEBUG: Start:	 Predicting
-2016-09-06 10:40:22,644 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:22,644 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:40:22,644 INFO: Done:	 Result Analysis
-2016-09-06 10:40:22,646 DEBUG: Done:	 Predicting
-2016-09-06 10:40:22,647 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:22,648 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:22,648 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:22,648 INFO: Done:	 Result Analysis
-2016-09-06 10:40:22,725 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,725 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,726 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:40:22,726 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:40:22,726 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:22,726 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:22,727 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:22,727 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:22,727 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:22,727 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:22,727 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:22,727 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:22,727 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:22,727 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:22,798 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:22,798 DEBUG: Start:	 Training
-2016-09-06 10:40:22,807 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:22,807 DEBUG: Start:	 Training
-2016-09-06 10:40:22,822 DEBUG: Info:	 Time for Training: 0.0971689224243[s]
-2016-09-06 10:40:22,822 DEBUG: Done:	 Training
-2016-09-06 10:40:22,822 DEBUG: Start:	 Predicting
-2016-09-06 10:40:22,829 DEBUG: Done:	 Predicting
-2016-09-06 10:40:22,829 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:22,831 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:22,831 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:22,832 INFO: Done:	 Result Analysis
-2016-09-06 10:40:22,833 DEBUG: Info:	 Time for Training: 0.108348846436[s]
-2016-09-06 10:40:22,833 DEBUG: Done:	 Training
-2016-09-06 10:40:22,833 DEBUG: Start:	 Predicting
-2016-09-06 10:40:22,836 DEBUG: Done:	 Predicting
-2016-09-06 10:40:22,836 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:22,837 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:22,838 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:40:22,838 INFO: Done:	 Result Analysis
-2016-09-06 10:40:22,968 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,968 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:40:22,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:40:22,968 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:22,968 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:22,969 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:22,969 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:22,969 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:22,969 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:22,969 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:22,969 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:22,969 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:22,969 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:23,006 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:23,006 DEBUG: Start:	 Training
-2016-09-06 10:40:23,008 DEBUG: Info:	 Time for Training: 0.0404770374298[s]
-2016-09-06 10:40:23,008 DEBUG: Done:	 Training
-2016-09-06 10:40:23,008 DEBUG: Start:	 Predicting
-2016-09-06 10:40:23,010 DEBUG: Done:	 Predicting
-2016-09-06 10:40:23,011 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:23,012 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:23,012 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,012 INFO: Done:	 Result Analysis
-2016-09-06 10:40:23,021 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:23,021 DEBUG: Start:	 Training
-2016-09-06 10:40:23,025 DEBUG: Info:	 Time for Training: 0.0578100681305[s]
-2016-09-06 10:40:23,025 DEBUG: Done:	 Training
-2016-09-06 10:40:23,025 DEBUG: Start:	 Predicting
-2016-09-06 10:40:23,028 DEBUG: Done:	 Predicting
-2016-09-06 10:40:23,028 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:23,030 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:23,030 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,031 INFO: Done:	 Result Analysis
-2016-09-06 10:40:23,122 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:23,122 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:23,122 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:40:23,122 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:40:23,123 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:23,123 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:23,124 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:23,124 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:23,124 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:23,124 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:23,124 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:23,124 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:23,124 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:23,124 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:23,174 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:23,175 DEBUG: Start:	 Training
-2016-09-06 10:40:23,175 DEBUG: Info:	 Time for Training: 0.0543978214264[s]
-2016-09-06 10:40:23,176 DEBUG: Done:	 Training
-2016-09-06 10:40:23,176 DEBUG: Start:	 Predicting
-2016-09-06 10:40:23,184 DEBUG: Done:	 Predicting
-2016-09-06 10:40:23,184 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:23,186 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:23,186 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,187 INFO: Done:	 Result Analysis
-2016-09-06 10:40:23,643 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:23,643 DEBUG: Start:	 Training
-2016-09-06 10:40:23,719 DEBUG: Info:	 Time for Training: 0.597998142242[s]
-2016-09-06 10:40:23,719 DEBUG: Done:	 Training
-2016-09-06 10:40:23,719 DEBUG: Start:	 Predicting
-2016-09-06 10:40:23,727 DEBUG: Done:	 Predicting
-2016-09-06 10:40:23,727 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:23,729 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:23,729 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 29, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,729 INFO: Done:	 Result Analysis
-2016-09-06 10:40:23,867 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:23,868 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:40:23,868 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:23,869 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:23,869 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:23,869 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:23,869 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:23,873 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:23,873 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:40:23,874 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:23,875 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:23,875 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:23,875 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:23,875 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:23,915 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:23,915 DEBUG: Start:	 Training
-2016-09-06 10:40:23,916 DEBUG: Info:	 Time for Training: 0.0492420196533[s]
-2016-09-06 10:40:23,916 DEBUG: Done:	 Training
-2016-09-06 10:40:23,916 DEBUG: Start:	 Predicting
-2016-09-06 10:40:23,927 DEBUG: Done:	 Predicting
-2016-09-06 10:40:23,928 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:23,929 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:23,929 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,929 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:23,929 INFO: Done:	 Result Analysis
-2016-09-06 10:40:23,929 DEBUG: Start:	 Training
-2016-09-06 10:40:23,950 DEBUG: Info:	 Time for Training: 0.0777189731598[s]
-2016-09-06 10:40:23,950 DEBUG: Done:	 Training
-2016-09-06 10:40:23,950 DEBUG: Start:	 Predicting
-2016-09-06 10:40:23,953 DEBUG: Done:	 Predicting
-2016-09-06 10:40:23,953 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:23,955 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:23,955 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,955 INFO: Done:	 Result Analysis
-2016-09-06 10:40:24,016 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,016 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,016 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:40:24,016 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:40:24,016 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:24,016 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:24,017 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:24,017 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:24,017 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:24,017 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:24,017 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:24,017 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:24,017 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,017 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,065 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:24,065 DEBUG: Start:	 Training
-2016-09-06 10:40:24,072 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:24,072 DEBUG: Start:	 Training
-2016-09-06 10:40:24,082 DEBUG: Info:	 Time for Training: 0.066547870636[s]
-2016-09-06 10:40:24,082 DEBUG: Done:	 Training
-2016-09-06 10:40:24,082 DEBUG: Start:	 Predicting
-2016-09-06 10:40:24,088 DEBUG: Done:	 Predicting
-2016-09-06 10:40:24,088 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:24,090 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:24,090 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:24,090 INFO: Done:	 Result Analysis
-2016-09-06 10:40:24,093 DEBUG: Info:	 Time for Training: 0.0777978897095[s]
-2016-09-06 10:40:24,093 DEBUG: Done:	 Training
-2016-09-06 10:40:24,093 DEBUG: Start:	 Predicting
-2016-09-06 10:40:24,099 DEBUG: Done:	 Predicting
-2016-09-06 10:40:24,099 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:24,100 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:24,100 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:40:24,101 INFO: Done:	 Result Analysis
-2016-09-06 10:40:24,162 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,163 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:40:24,163 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:24,163 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,163 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:40:24,163 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:24,164 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:24,164 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:24,164 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:24,164 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:24,164 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:24,164 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:24,164 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,164 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,198 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:24,198 DEBUG: Start:	 Training
-2016-09-06 10:40:24,200 DEBUG: Info:	 Time for Training: 0.0375709533691[s]
-2016-09-06 10:40:24,200 DEBUG: Done:	 Training
-2016-09-06 10:40:24,200 DEBUG: Start:	 Predicting
-2016-09-06 10:40:24,202 DEBUG: Done:	 Predicting
-2016-09-06 10:40:24,202 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:24,204 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:24,204 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:40:24,204 INFO: Done:	 Result Analysis
-2016-09-06 10:40:24,213 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:24,213 DEBUG: Start:	 Training
-2016-09-06 10:40:24,216 DEBUG: Info:	 Time for Training: 0.0543420314789[s]
-2016-09-06 10:40:24,216 DEBUG: Done:	 Training
-2016-09-06 10:40:24,217 DEBUG: Start:	 Predicting
-2016-09-06 10:40:24,220 DEBUG: Done:	 Predicting
-2016-09-06 10:40:24,220 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:24,222 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:24,222 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:40:24,222 INFO: Done:	 Result Analysis
-2016-09-06 10:40:24,312 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,312 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,312 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:40:24,312 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:40:24,312 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:24,312 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:24,313 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:24,313 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:24,313 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:24,313 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:24,313 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:24,313 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:24,313 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,313 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,346 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:24,346 DEBUG: Start:	 Training
-2016-09-06 10:40:24,346 DEBUG: Info:	 Time for Training: 0.0354690551758[s]
-2016-09-06 10:40:24,347 DEBUG: Done:	 Training
-2016-09-06 10:40:24,347 DEBUG: Start:	 Predicting
-2016-09-06 10:40:24,352 DEBUG: Done:	 Predicting
-2016-09-06 10:40:24,352 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:24,353 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:24,353 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.67619047619
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:40:24,353 INFO: Done:	 Result Analysis
-2016-09-06 10:40:24,910 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:24,910 DEBUG: Start:	 Training
-2016-09-06 10:40:24,992 DEBUG: Info:	 Time for Training: 0.680758953094[s]
-2016-09-06 10:40:24,992 DEBUG: Done:	 Training
-2016-09-06 10:40:24,992 DEBUG: Start:	 Predicting
-2016-09-06 10:40:25,001 DEBUG: Done:	 Predicting
-2016-09-06 10:40:25,001 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:25,003 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:25,003 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 29, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:40:25,003 INFO: Done:	 Result Analysis
-2016-09-06 10:40:25,067 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:25,067 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:40:25,067 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:25,068 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:25,068 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:25,068 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:25,068 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:25,068 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:40:25,068 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:25,068 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 10:40:25,069 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:25,069 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:25,069 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 10:40:25,069 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 10:40:25,122 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:25,122 DEBUG: Start:	 Training
-2016-09-06 10:40:25,122 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 10:40:25,123 DEBUG: Start:	 Training
-2016-09-06 10:40:25,123 DEBUG: Info:	 Time for Training: 0.056645154953[s]
-2016-09-06 10:40:25,123 DEBUG: Done:	 Training
-2016-09-06 10:40:25,123 DEBUG: Start:	 Predicting
-2016-09-06 10:40:25,137 DEBUG: Done:	 Predicting
-2016-09-06 10:40:25,137 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:25,139 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:25,139 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:25,139 INFO: Done:	 Result Analysis
-2016-09-06 10:40:25,140 DEBUG: Info:	 Time for Training: 0.0731339454651[s]
-2016-09-06 10:40:25,140 DEBUG: Done:	 Training
-2016-09-06 10:40:25,141 DEBUG: Start:	 Predicting
-2016-09-06 10:40:25,143 DEBUG: Done:	 Predicting
-2016-09-06 10:40:25,144 DEBUG: Start:	 Getting Results
-2016-09-06 10:40:25,145 DEBUG: Done:	 Getting Results
-2016-09-06 10:40:25,145 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.442857142857
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.442857142857
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:40:25,145 INFO: Done:	 Result Analysis
-2016-09-06 10:40:25,362 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:40:25,362 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:40:25,363 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:40:25,363 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 10:40:25,363 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:40:25,363 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 10:40:25,364 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 10:40:25,364 INFO: Info:	 Shape of View2 :(300, 14)
-2016-09-06 10:40:25,364 INFO: Info:	 Shape of View1 :(300, 7)
-2016-09-06 10:40:25,364 INFO: Info:	 Shape of View3 :(300, 7)
-2016-09-06 10:40:25,364 INFO: Done:	 Read Database Files
-2016-09-06 10:40:25,365 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:40:25,365 INFO: Info:	 Shape of View2 :(300, 14)
-2016-09-06 10:40:25,365 INFO: Info:	 Shape of View3 :(300, 7)
-2016-09-06 10:40:25,365 INFO: Done:	 Read Database Files
-2016-09-06 10:40:25,365 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 10:40:25,370 INFO: Done:	 Determine validation split
-2016-09-06 10:40:25,370 INFO: Start:	 Determine 5 folds
-2016-09-06 10:40:25,371 INFO: Done:	 Determine validation split
-2016-09-06 10:40:25,371 INFO: Start:	 Determine 5 folds
-2016-09-06 10:40:25,381 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 10:40:25,382 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 10:40:25,382 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:40:25,382 INFO: Done:	 Determine folds
-2016-09-06 10:40:25,382 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 10:40:25,382 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 10:40:25,382 DEBUG: 	Start:	 Random search for Adaboost with 30 iterations
-2016-09-06 10:40:25,383 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 10:40:25,383 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 10:40:25,383 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 10:40:25,383 INFO: Done:	 Determine folds
-2016-09-06 10:40:25,383 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 10:40:25,384 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 10:40:25,384 DEBUG: 	Start:	 Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3faa7965..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c74ef47d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.933333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.933333333333
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5e8145f8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c2e5210e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0468fed6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.814285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.814285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 879e5454..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.652380952381
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8c5ade65..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 29, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.990476190476
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0a0d904e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 11b4dc01..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bc68c9fa..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bc4d679a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4fe0cec4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 29, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5b99d1af..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0fa62be2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9e75865c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 20513448..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c9dca693..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 26220077..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9a5e6d34..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bf83d84c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 29, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 05e8489b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c1e2e693..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8bffe493..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f1942cf7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 07177040..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.67619047619
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cb100076..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 29, max_depth : 9
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f433c885..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d0ca9fbd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3a184326..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.585714285714
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 76add94d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.442857142857
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 1181
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.442857142857
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110752-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-110752-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 16da2456..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110752-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,656 +0,0 @@
-2016-09-06 11:07:52,942 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:07:52,943 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000152125 Gbytes /!\ 
-2016-09-06 11:07:57,972 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 11:07:57,974 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 11:07:58,020 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,020 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,020 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:07:58,020 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:07:58,020 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:58,020 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:58,021 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,021 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,021 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,021 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,021 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:58,021 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:58,021 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,021 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,056 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:58,056 DEBUG: Start:	 Training
-2016-09-06 11:07:58,058 DEBUG: Info:	 Time for Training: 0.0386440753937[s]
-2016-09-06 11:07:58,058 DEBUG: Done:	 Training
-2016-09-06 11:07:58,058 DEBUG: Start:	 Predicting
-2016-09-06 11:07:58,061 DEBUG: Done:	 Predicting
-2016-09-06 11:07:58,061 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:58,062 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:58,062 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.961904761905
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.961904761905
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,063 INFO: Done:	 Result Analysis
-2016-09-06 11:07:58,071 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:58,071 DEBUG: Start:	 Training
-2016-09-06 11:07:58,075 DEBUG: Info:	 Time for Training: 0.0555868148804[s]
-2016-09-06 11:07:58,075 DEBUG: Done:	 Training
-2016-09-06 11:07:58,075 DEBUG: Start:	 Predicting
-2016-09-06 11:07:58,078 DEBUG: Done:	 Predicting
-2016-09-06 11:07:58,078 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:58,080 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:58,080 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,080 INFO: Done:	 Result Analysis
-2016-09-06 11:07:58,167 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,167 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,167 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:07:58,167 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:07:58,167 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:58,167 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:58,168 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,168 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,168 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,168 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,168 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:58,168 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:58,168 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,168 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,230 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:58,231 DEBUG: Start:	 Training
-2016-09-06 11:07:58,231 DEBUG: Info:	 Time for Training: 0.065171957016[s]
-2016-09-06 11:07:58,231 DEBUG: Done:	 Training
-2016-09-06 11:07:58,231 DEBUG: Start:	 Predicting
-2016-09-06 11:07:58,239 DEBUG: Done:	 Predicting
-2016-09-06 11:07:58,239 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:58,241 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:58,241 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,241 INFO: Done:	 Result Analysis
-2016-09-06 11:07:58,382 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:58,382 DEBUG: Start:	 Training
-2016-09-06 11:07:58,406 DEBUG: Info:	 Time for Training: 0.239724874496[s]
-2016-09-06 11:07:58,406 DEBUG: Done:	 Training
-2016-09-06 11:07:58,406 DEBUG: Start:	 Predicting
-2016-09-06 11:07:58,410 DEBUG: Done:	 Predicting
-2016-09-06 11:07:58,411 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:58,413 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:58,413 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 9, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,413 INFO: Done:	 Result Analysis
-2016-09-06 11:07:58,512 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,512 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:07:58,513 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:58,513 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,514 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,514 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:58,514 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,515 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,515 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:07:58,516 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:58,517 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,517 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,518 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:58,518 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,559 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:58,560 DEBUG: Start:	 Training
-2016-09-06 11:07:58,560 DEBUG: Info:	 Time for Training: 0.0485830307007[s]
-2016-09-06 11:07:58,560 DEBUG: Done:	 Training
-2016-09-06 11:07:58,560 DEBUG: Start:	 Predicting
-2016-09-06 11:07:58,574 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:58,574 DEBUG: Start:	 Training
-2016-09-06 11:07:58,576 DEBUG: Done:	 Predicting
-2016-09-06 11:07:58,576 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:58,578 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:58,578 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,579 INFO: Done:	 Result Analysis
-2016-09-06 11:07:58,600 DEBUG: Info:	 Time for Training: 0.0860497951508[s]
-2016-09-06 11:07:58,600 DEBUG: Done:	 Training
-2016-09-06 11:07:58,600 DEBUG: Start:	 Predicting
-2016-09-06 11:07:58,604 DEBUG: Done:	 Predicting
-2016-09-06 11:07:58,604 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:58,606 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:58,606 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,606 INFO: Done:	 Result Analysis
-2016-09-06 11:07:58,767 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,767 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:07:58,767 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:58,767 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,768 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:07:58,768 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:58,768 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,769 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,769 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,769 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:58,769 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,769 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,769 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:58,769 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,824 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:58,824 DEBUG: Start:	 Training
-2016-09-06 11:07:58,831 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:58,832 DEBUG: Start:	 Training
-2016-09-06 11:07:58,841 DEBUG: Info:	 Time for Training: 0.0746459960938[s]
-2016-09-06 11:07:58,841 DEBUG: Done:	 Training
-2016-09-06 11:07:58,841 DEBUG: Start:	 Predicting
-2016-09-06 11:07:58,846 DEBUG: Done:	 Predicting
-2016-09-06 11:07:58,846 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:58,847 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:58,847 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.388888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.388888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,848 INFO: Done:	 Result Analysis
-2016-09-06 11:07:58,850 DEBUG: Info:	 Time for Training: 0.0830399990082[s]
-2016-09-06 11:07:58,850 DEBUG: Done:	 Training
-2016-09-06 11:07:58,850 DEBUG: Start:	 Predicting
-2016-09-06 11:07:58,856 DEBUG: Done:	 Predicting
-2016-09-06 11:07:58,856 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:58,857 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:58,858 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,858 INFO: Done:	 Result Analysis
-2016-09-06 11:07:58,917 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,917 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:07:58,918 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:58,918 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,918 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:07:58,918 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:58,918 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:58,918 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:58,919 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:58,919 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,919 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:58,919 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:58,919 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:58,919 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,961 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:58,962 DEBUG: Start:	 Training
-2016-09-06 11:07:58,963 DEBUG: Info:	 Time for Training: 0.045686006546[s]
-2016-09-06 11:07:58,963 DEBUG: Done:	 Training
-2016-09-06 11:07:58,963 DEBUG: Start:	 Predicting
-2016-09-06 11:07:58,966 DEBUG: Done:	 Predicting
-2016-09-06 11:07:58,966 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:58,967 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:58,967 DEBUG: Start:	 Training
-2016-09-06 11:07:58,967 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:58,967 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.919047619048
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,968 INFO: Done:	 Result Analysis
-2016-09-06 11:07:58,971 DEBUG: Info:	 Time for Training: 0.0543868541718[s]
-2016-09-06 11:07:58,971 DEBUG: Done:	 Training
-2016-09-06 11:07:58,971 DEBUG: Start:	 Predicting
-2016-09-06 11:07:58,974 DEBUG: Done:	 Predicting
-2016-09-06 11:07:58,975 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:58,977 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:58,977 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,977 INFO: Done:	 Result Analysis
-2016-09-06 11:07:59,060 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,060 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:07:59,060 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,060 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:59,060 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:07:59,060 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:59,061 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,061 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,061 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,061 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:59,061 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,061 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,062 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:59,062 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,096 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:59,096 DEBUG: Start:	 Training
-2016-09-06 11:07:59,096 DEBUG: Info:	 Time for Training: 0.037360906601[s]
-2016-09-06 11:07:59,096 DEBUG: Done:	 Training
-2016-09-06 11:07:59,096 DEBUG: Start:	 Predicting
-2016-09-06 11:07:59,102 DEBUG: Done:	 Predicting
-2016-09-06 11:07:59,103 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:59,104 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:59,104 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,104 INFO: Done:	 Result Analysis
-2016-09-06 11:07:59,236 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:59,236 DEBUG: Start:	 Training
-2016-09-06 11:07:59,259 DEBUG: Info:	 Time for Training: 0.199690103531[s]
-2016-09-06 11:07:59,259 DEBUG: Done:	 Training
-2016-09-06 11:07:59,259 DEBUG: Start:	 Predicting
-2016-09-06 11:07:59,263 DEBUG: Done:	 Predicting
-2016-09-06 11:07:59,264 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:59,265 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:59,265 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 9, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.952380952381
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,265 INFO: Done:	 Result Analysis
-2016-09-06 11:07:59,404 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,404 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,405 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:07:59,405 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:07:59,405 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:59,405 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:59,405 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,405 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,405 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,405 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,405 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:59,406 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:59,406 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,406 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,451 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:59,451 DEBUG: Start:	 Training
-2016-09-06 11:07:59,452 DEBUG: Info:	 Time for Training: 0.0481390953064[s]
-2016-09-06 11:07:59,452 DEBUG: Done:	 Training
-2016-09-06 11:07:59,452 DEBUG: Start:	 Predicting
-2016-09-06 11:07:59,456 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:59,456 DEBUG: Start:	 Training
-2016-09-06 11:07:59,474 DEBUG: Info:	 Time for Training: 0.0696890354156[s]
-2016-09-06 11:07:59,474 DEBUG: Done:	 Training
-2016-09-06 11:07:59,474 DEBUG: Start:	 Predicting
-2016-09-06 11:07:59,477 DEBUG: Done:	 Predicting
-2016-09-06 11:07:59,477 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:59,478 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:59,478 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,479 INFO: Done:	 Result Analysis
-2016-09-06 11:07:59,482 DEBUG: Done:	 Predicting
-2016-09-06 11:07:59,482 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:59,485 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:59,485 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,485 INFO: Done:	 Result Analysis
-2016-09-06 11:07:59,567 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,567 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:07:59,567 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:59,568 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,568 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:07:59,568 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:59,568 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,568 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,568 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:59,568 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,568 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,569 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,569 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:07:59,569 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,618 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:59,618 DEBUG: Start:	 Training
-2016-09-06 11:07:59,623 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:07:59,623 DEBUG: Start:	 Training
-2016-09-06 11:07:59,635 DEBUG: Info:	 Time for Training: 0.0680291652679[s]
-2016-09-06 11:07:59,635 DEBUG: Done:	 Training
-2016-09-06 11:07:59,635 DEBUG: Start:	 Predicting
-2016-09-06 11:07:59,640 DEBUG: Done:	 Predicting
-2016-09-06 11:07:59,640 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:59,641 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:59,642 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,642 INFO: Done:	 Result Analysis
-2016-09-06 11:07:59,643 DEBUG: Info:	 Time for Training: 0.076847076416[s]
-2016-09-06 11:07:59,643 DEBUG: Done:	 Training
-2016-09-06 11:07:59,644 DEBUG: Start:	 Predicting
-2016-09-06 11:07:59,648 DEBUG: Done:	 Predicting
-2016-09-06 11:07:59,648 DEBUG: Start:	 Getting Results
-2016-09-06 11:07:59,650 DEBUG: Done:	 Getting Results
-2016-09-06 11:07:59,650 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,651 INFO: Done:	 Result Analysis
-2016-09-06 11:07:59,711 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,711 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,711 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:07:59,711 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:07:59,711 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:07:59,711 DEBUG: Start:	 Determine Train/Test split
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b61753b0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index da9c6aad..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.919047619048
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9642e2c6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 400c77ab..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 9, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4fc8ecdc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 62a99e6b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.538095238095
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1c8ccb4c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.388888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.388888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d3d4db5f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 14)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 80d058bd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1dc9c42a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 9, max_depth : 8
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.952380952381
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 48b1c2f4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cc29d1a9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ee1356b6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8d53f61e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 5160
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110833-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-110833-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 80fcf978..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110833-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,134 +0,0 @@
-2016-09-06 11:08:33,832 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:08:33,832 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00014040625 Gbytes /!\ 
-2016-09-06 11:08:38,847 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 11:08:38,850 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 11:08:38,908 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:08:38,908 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:08:38,908 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:08:38,908 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:08:38,909 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:08:38,909 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:08:38,909 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 11:08:38,910 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 11:08:38,910 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:08:38,910 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:08:38,910 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 11:08:38,911 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 11:08:38,911 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:08:38,911 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:08:38,951 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:08:38,952 DEBUG: Start:	 Training
-2016-09-06 11:08:38,954 DEBUG: Info:	 Time for Training: 0.0467619895935[s]
-2016-09-06 11:08:38,954 DEBUG: Done:	 Training
-2016-09-06 11:08:38,954 DEBUG: Start:	 Predicting
-2016-09-06 11:08:38,957 DEBUG: Done:	 Predicting
-2016-09-06 11:08:38,957 DEBUG: Start:	 Getting Results
-2016-09-06 11:08:38,958 DEBUG: Done:	 Getting Results
-2016-09-06 11:08:38,958 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 12
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:08:38,958 INFO: Done:	 Result Analysis
-2016-09-06 11:08:38,964 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:08:38,964 DEBUG: Start:	 Training
-2016-09-06 11:08:38,969 DEBUG: Info:	 Time for Training: 0.0624799728394[s]
-2016-09-06 11:08:38,969 DEBUG: Done:	 Training
-2016-09-06 11:08:38,969 DEBUG: Start:	 Predicting
-2016-09-06 11:08:38,972 DEBUG: Done:	 Predicting
-2016-09-06 11:08:38,972 DEBUG: Start:	 Getting Results
-2016-09-06 11:08:38,974 DEBUG: Done:	 Getting Results
-2016-09-06 11:08:38,974 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:08:38,974 INFO: Done:	 Result Analysis
-2016-09-06 11:08:39,063 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:08:39,063 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:08:39,063 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:08:39,063 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:08:39,063 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:08:39,063 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:08:39,064 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 11:08:39,064 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 11:08:39,064 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 11:08:39,064 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 11:08:39,064 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:08:39,064 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:08:39,064 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:08:39,064 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:08:39,094 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:08:39,094 DEBUG: Start:	 Training
-2016-09-06 11:08:39,094 DEBUG: Info:	 Time for Training: 0.0320620536804[s]
-2016-09-06 11:08:39,094 DEBUG: Done:	 Training
-2016-09-06 11:08:39,094 DEBUG: Start:	 Predicting
-2016-09-06 11:08:39,100 DEBUG: Done:	 Predicting
-2016-09-06 11:08:39,100 DEBUG: Start:	 Getting Results
-2016-09-06 11:08:39,101 DEBUG: Done:	 Getting Results
-2016-09-06 11:08:39,101 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 12
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.661904761905
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:08:39,102 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 224ed3b5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5c22b4f6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 12
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110839Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0542160b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 12
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.661904761905
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110946-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-110946-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 42c0bc03..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110946-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,134 +0,0 @@
-2016-09-06 11:09:46,919 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:09:46,919 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00013103125 Gbytes /!\ 
-2016-09-06 11:09:51,934 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 11:09:51,938 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 11:09:51,991 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:09:51,992 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:09:51,992 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:09:51,992 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:09:51,992 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:09:51,992 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:09:51,993 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 11:09:51,993 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 11:09:51,993 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 11:09:51,993 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 11:09:51,994 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:09:51,994 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:09:51,994 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:09:51,994 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:09:52,028 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:09:52,028 DEBUG: Start:	 Training
-2016-09-06 11:09:52,030 DEBUG: Info:	 Time for Training: 0.0394358634949[s]
-2016-09-06 11:09:52,030 DEBUG: Done:	 Training
-2016-09-06 11:09:52,030 DEBUG: Start:	 Predicting
-2016-09-06 11:09:52,033 DEBUG: Done:	 Predicting
-2016-09-06 11:09:52,033 DEBUG: Start:	 Getting Results
-2016-09-06 11:09:52,034 DEBUG: Done:	 Getting Results
-2016-09-06 11:09:52,034 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 22
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:09:52,035 INFO: Done:	 Result Analysis
-2016-09-06 11:09:52,043 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:09:52,043 DEBUG: Start:	 Training
-2016-09-06 11:09:52,047 DEBUG: Info:	 Time for Training: 0.0563831329346[s]
-2016-09-06 11:09:52,047 DEBUG: Done:	 Training
-2016-09-06 11:09:52,047 DEBUG: Start:	 Predicting
-2016-09-06 11:09:52,049 DEBUG: Done:	 Predicting
-2016-09-06 11:09:52,050 DEBUG: Start:	 Getting Results
-2016-09-06 11:09:52,051 DEBUG: Done:	 Getting Results
-2016-09-06 11:09:52,051 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 6, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:09:52,052 INFO: Done:	 Result Analysis
-2016-09-06 11:09:52,136 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:09:52,136 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:09:52,137 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:09:52,137 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:09:52,137 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:09:52,137 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:09:52,138 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 11:09:52,138 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 11:09:52,138 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 11:09:52,138 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 11:09:52,138 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:09:52,138 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:09:52,138 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:09:52,138 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:09:52,188 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:09:52,188 DEBUG: Start:	 Training
-2016-09-06 11:09:52,189 DEBUG: Info:	 Time for Training: 0.0529510974884[s]
-2016-09-06 11:09:52,189 DEBUG: Done:	 Training
-2016-09-06 11:09:52,189 DEBUG: Start:	 Predicting
-2016-09-06 11:09:52,198 DEBUG: Done:	 Predicting
-2016-09-06 11:09:52,199 DEBUG: Start:	 Getting Results
-2016-09-06 11:09:52,201 DEBUG: Done:	 Getting Results
-2016-09-06 11:09:52,201 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 29
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.642857142857
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:09:52,201 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2d4ed4e3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 6, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3f0d5254..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 22
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5deeea39..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 11)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 29
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.642857142857
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111029-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-111029-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 33c3b5eb..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111029-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,263 +0,0 @@
-2016-09-06 11:10:29,444 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:10:29,444 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00011228125 Gbytes /!\ 
-2016-09-06 11:10:34,459 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 11:10:34,463 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 11:10:34,517 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,518 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:10:34,518 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:10:34,518 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,518 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:10:34,518 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:10:34,519 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,519 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,519 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,519 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,519 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:10:34,519 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:10:34,519 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,519 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,552 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:10:34,552 DEBUG: Start:	 Training
-2016-09-06 11:10:34,553 DEBUG: Info:	 Time for Training: 0.0359370708466[s]
-2016-09-06 11:10:34,553 DEBUG: Done:	 Training
-2016-09-06 11:10:34,553 DEBUG: Start:	 Predicting
-2016-09-06 11:10:34,556 DEBUG: Done:	 Predicting
-2016-09-06 11:10:34,556 DEBUG: Start:	 Getting Results
-2016-09-06 11:10:34,557 DEBUG: Done:	 Getting Results
-2016-09-06 11:10:34,557 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 22
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,557 INFO: Done:	 Result Analysis
-2016-09-06 11:10:34,566 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:10:34,567 DEBUG: Start:	 Training
-2016-09-06 11:10:34,569 DEBUG: Info:	 Time for Training: 0.0532448291779[s]
-2016-09-06 11:10:34,570 DEBUG: Done:	 Training
-2016-09-06 11:10:34,570 DEBUG: Start:	 Predicting
-2016-09-06 11:10:34,572 DEBUG: Done:	 Predicting
-2016-09-06 11:10:34,572 DEBUG: Start:	 Getting Results
-2016-09-06 11:10:34,574 DEBUG: Done:	 Getting Results
-2016-09-06 11:10:34,574 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 6, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,575 INFO: Done:	 Result Analysis
-2016-09-06 11:10:34,658 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,658 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,658 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:10:34,658 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:10:34,658 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:10:34,658 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:10:34,659 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,659 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,659 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,659 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,659 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:10:34,659 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:10:34,659 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,659 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,689 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:10:34,689 DEBUG: Start:	 Training
-2016-09-06 11:10:34,690 DEBUG: Info:	 Time for Training: 0.0322139263153[s]
-2016-09-06 11:10:34,690 DEBUG: Done:	 Training
-2016-09-06 11:10:34,690 DEBUG: Start:	 Predicting
-2016-09-06 11:10:34,694 DEBUG: Done:	 Predicting
-2016-09-06 11:10:34,694 DEBUG: Start:	 Getting Results
-2016-09-06 11:10:34,695 DEBUG: Done:	 Getting Results
-2016-09-06 11:10:34,695 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.7
-accuracy_score on test : 0.388888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.7
-		- Score on test : 0.388888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,696 INFO: Done:	 Result Analysis
-2016-09-06 11:10:34,757 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:10:34,757 DEBUG: Start:	 Training
-2016-09-06 11:10:34,769 DEBUG: Info:	 Time for Training: 0.111187934875[s]
-2016-09-06 11:10:34,769 DEBUG: Done:	 Training
-2016-09-06 11:10:34,769 DEBUG: Start:	 Predicting
-2016-09-06 11:10:34,773 DEBUG: Done:	 Predicting
-2016-09-06 11:10:34,773 DEBUG: Start:	 Getting Results
-2016-09-06 11:10:34,774 DEBUG: Done:	 Getting Results
-2016-09-06 11:10:34,774 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.880952380952
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 22
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.880952380952
-		- Score on test : 0.377777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,775 INFO: Done:	 Result Analysis
-2016-09-06 11:10:34,909 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,909 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,909 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:10:34,909 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:10:34,909 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:10:34,909 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:10:34,910 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,910 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,910 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,910 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,910 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:10:34,910 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:10:34,911 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,911 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,957 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:10:34,957 DEBUG: Start:	 Training
-2016-09-06 11:10:34,958 DEBUG: Info:	 Time for Training: 0.0501399040222[s]
-2016-09-06 11:10:34,958 DEBUG: Done:	 Training
-2016-09-06 11:10:34,958 DEBUG: Start:	 Predicting
-2016-09-06 11:10:34,959 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:10:34,960 DEBUG: Start:	 Training
-2016-09-06 11:10:34,974 DEBUG: Done:	 Predicting
-2016-09-06 11:10:34,975 DEBUG: Start:	 Getting Results
-2016-09-06 11:10:34,976 DEBUG: Done:	 Getting Results
-2016-09-06 11:10:34,976 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,976 INFO: Done:	 Result Analysis
-2016-09-06 11:10:34,978 DEBUG: Info:	 Time for Training: 0.0697031021118[s]
-2016-09-06 11:10:34,978 DEBUG: Done:	 Training
-2016-09-06 11:10:34,978 DEBUG: Start:	 Predicting
-2016-09-06 11:10:34,981 DEBUG: Done:	 Predicting
-2016-09-06 11:10:34,981 DEBUG: Start:	 Getting Results
-2016-09-06 11:10:34,983 DEBUG: Done:	 Getting Results
-2016-09-06 11:10:34,983 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7236
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.471428571429
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,983 INFO: Done:	 Result Analysis
-2016-09-06 11:10:35,052 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:35,052 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:35,052 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:10:35,052 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:10:35,052 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:10:35,052 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:10:35,053 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:35,053 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:35,053 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:35,053 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:35,053 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:10:35,053 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:10:35,054 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:10:35,054 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:10:35,132 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:10:35,133 DEBUG: Start:	 Training
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1f6a8621..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 6, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8889d71a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 22
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cb5b0999..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.7
-accuracy_score on test : 0.388888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 4
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.7
-		- Score on test : 0.388888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4a473447..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.880952380952
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 22
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.880952380952
-		- Score on test : 0.377777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3d8ebb56..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0ec8d3c8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 5)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7236
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.471428571429
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111106-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-111106-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index dc730d4e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111106-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,253 +0,0 @@
-2016-09-06 11:11:06,795 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:11:06,795 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00014978125 Gbytes /!\ 
-2016-09-06 11:11:11,810 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 11:11:11,814 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 11:11:11,860 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:11,860 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:11,860 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:11:11,860 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:11:11,860 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:11:11,860 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:11:11,861 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:11,861 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:11,861 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:11,861 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:11,861 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:11:11,861 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:11:11,861 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:11:11,861 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:11:11,894 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:11:11,894 DEBUG: Start:	 Training
-2016-09-06 11:11:11,896 DEBUG: Info:	 Time for Training: 0.036553144455[s]
-2016-09-06 11:11:11,896 DEBUG: Done:	 Training
-2016-09-06 11:11:11,896 DEBUG: Start:	 Predicting
-2016-09-06 11:11:11,898 DEBUG: Done:	 Predicting
-2016-09-06 11:11:11,898 DEBUG: Start:	 Getting Results
-2016-09-06 11:11:11,900 DEBUG: Done:	 Getting Results
-2016-09-06 11:11:11,900 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:11:11,900 INFO: Done:	 Result Analysis
-2016-09-06 11:11:11,909 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:11:11,909 DEBUG: Start:	 Training
-2016-09-06 11:11:11,912 DEBUG: Info:	 Time for Training: 0.0533759593964[s]
-2016-09-06 11:11:11,913 DEBUG: Done:	 Training
-2016-09-06 11:11:11,913 DEBUG: Start:	 Predicting
-2016-09-06 11:11:11,915 DEBUG: Done:	 Predicting
-2016-09-06 11:11:11,916 DEBUG: Start:	 Getting Results
-2016-09-06 11:11:11,917 DEBUG: Done:	 Getting Results
-2016-09-06 11:11:11,917 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:11:11,918 INFO: Done:	 Result Analysis
-2016-09-06 11:11:12,007 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,007 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,007 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:11:12,007 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:11:12,007 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:11:12,007 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:11:12,008 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:12,008 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:12,008 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:12,008 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:12,008 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:11:12,008 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:11:12,008 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:11:12,008 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:11:12,040 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:11:12,041 DEBUG: Start:	 Training
-2016-09-06 11:11:12,041 DEBUG: Info:	 Time for Training: 0.034646987915[s]
-2016-09-06 11:11:12,041 DEBUG: Done:	 Training
-2016-09-06 11:11:12,041 DEBUG: Start:	 Predicting
-2016-09-06 11:11:12,047 DEBUG: Done:	 Predicting
-2016-09-06 11:11:12,047 DEBUG: Start:	 Getting Results
-2016-09-06 11:11:12,049 DEBUG: Done:	 Getting Results
-2016-09-06 11:11:12,049 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:11:12,049 INFO: Done:	 Result Analysis
-2016-09-06 11:11:12,172 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:11:12,172 DEBUG: Start:	 Training
-2016-09-06 11:11:12,193 DEBUG: Info:	 Time for Training: 0.186748981476[s]
-2016-09-06 11:11:12,193 DEBUG: Done:	 Training
-2016-09-06 11:11:12,193 DEBUG: Start:	 Predicting
-2016-09-06 11:11:12,197 DEBUG: Done:	 Predicting
-2016-09-06 11:11:12,198 DEBUG: Start:	 Getting Results
-2016-09-06 11:11:12,199 DEBUG: Done:	 Getting Results
-2016-09-06 11:11:12,199 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:11:12,199 INFO: Done:	 Result Analysis
-2016-09-06 11:11:12,253 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,253 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,253 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:11:12,253 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:11:12,253 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:11:12,253 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:11:12,254 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:12,254 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:12,254 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:12,254 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:12,254 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:11:12,254 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:11:12,254 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:11:12,254 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:11:12,306 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:11:12,306 DEBUG: Start:	 Training
-2016-09-06 11:11:12,317 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:11:12,317 DEBUG: Start:	 Training
-2016-09-06 11:11:12,318 DEBUG: Info:	 Time for Training: 0.0656809806824[s]
-2016-09-06 11:11:12,318 DEBUG: Done:	 Training
-2016-09-06 11:11:12,318 DEBUG: Start:	 Predicting
-2016-09-06 11:11:12,324 DEBUG: Info:	 Time for Training: 0.071897983551[s]
-2016-09-06 11:11:12,324 DEBUG: Done:	 Training
-2016-09-06 11:11:12,324 DEBUG: Start:	 Predicting
-2016-09-06 11:11:12,328 DEBUG: Done:	 Predicting
-2016-09-06 11:11:12,328 DEBUG: Start:	 Getting Results
-2016-09-06 11:11:12,329 DEBUG: Done:	 Getting Results
-2016-09-06 11:11:12,329 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3624
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 11:11:12,330 INFO: Done:	 Result Analysis
-2016-09-06 11:11:12,335 DEBUG: Done:	 Predicting
-2016-09-06 11:11:12,335 DEBUG: Start:	 Getting Results
-2016-09-06 11:11:12,338 DEBUG: Done:	 Getting Results
-2016-09-06 11:11:12,338 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:11:12,338 INFO: Done:	 Result Analysis
-2016-09-06 11:11:12,399 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,399 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:11:12,399 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,399 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:11:12,400 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:11:12,400 DEBUG: Start:	 Determine Train/Test split
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 07969b8c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 560ae93e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ff064c80..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 40
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6f837cad..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 8, max_depth : 27
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 909d20c0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.62380952381
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b2fe01c3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 3624
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111203-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-111203-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 71be916a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111203-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,32 +0,0 @@
-2016-09-06 11:12:03,520 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:12:03,520 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00015446875 Gbytes /!\ 
-2016-09-06 11:12:08,535 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 11:12:08,539 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 11:12:08,590 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:12:08,590 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:12:08,590 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:12:08,591 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:12:08,591 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:12:08,591 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:12:08,592 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 11:12:08,592 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 11:12:08,592 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 11:12:08,592 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 11:12:08,592 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:12:08,592 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:12:08,592 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:12:08,592 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:12:08,648 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:12:08,648 DEBUG: Start:	 Training
-2016-09-06 11:12:08,652 DEBUG: Info:	 Time for Training: 0.0629100799561[s]
-2016-09-06 11:12:08,653 DEBUG: Done:	 Training
-2016-09-06 11:12:08,653 DEBUG: Start:	 Predicting
-2016-09-06 11:12:08,655 DEBUG: Done:	 Predicting
-2016-09-06 11:12:08,655 DEBUG: Start:	 Getting Results
-2016-09-06 11:12:08,656 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:12:08,656 DEBUG: Start:	 Training
-2016-09-06 11:12:08,659 DEBUG: Info:	 Time for Training: 0.0693309307098[s]
-2016-09-06 11:12:08,659 DEBUG: Done:	 Training
-2016-09-06 11:12:08,659 DEBUG: Start:	 Predicting
-2016-09-06 11:12:08,662 DEBUG: Done:	 Predicting
-2016-09-06 11:12:08,662 DEBUG: Start:	 Getting Results
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112150-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-112150-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 5f0724c6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112150-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2150 +0,0 @@
-2016-09-06 11:21:50,451 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:21:50,452 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000124 Gbytes /!\ 
-2016-09-06 11:21:55,466 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 11:21:55,470 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 11:21:55,524 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:55,525 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:21:55,525 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:55,525 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:55,525 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:21:55,525 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:55,526 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:55,526 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:55,526 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:55,526 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:55,526 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:55,526 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:55,526 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:55,526 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:55,561 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:55,561 DEBUG: Start:	 Training
-2016-09-06 11:21:55,563 DEBUG: Info:	 Time for Training: 0.0388250350952[s]
-2016-09-06 11:21:55,563 DEBUG: Done:	 Training
-2016-09-06 11:21:55,563 DEBUG: Start:	 Predicting
-2016-09-06 11:21:55,565 DEBUG: Done:	 Predicting
-2016-09-06 11:21:55,565 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:55,575 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:55,575 DEBUG: Start:	 Training
-2016-09-06 11:21:55,579 DEBUG: Info:	 Time for Training: 0.0551791191101[s]
-2016-09-06 11:21:55,579 DEBUG: Done:	 Training
-2016-09-06 11:21:55,579 DEBUG: Start:	 Predicting
-2016-09-06 11:21:55,582 DEBUG: Done:	 Predicting
-2016-09-06 11:21:55,582 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:55,604 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:55,605 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.413793103448
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.413793103448
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.131912640639
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.391304347826
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.439024390244
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433797909408
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:55,605 INFO: Done:	 Result Analysis
-2016-09-06 11:21:55,619 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:55,619 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.494382022472
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.494382022472
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.5
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.00596274193664
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.458333333333
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.536585365854
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.502986560478
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:21:55,619 INFO: Done:	 Result Analysis
-2016-09-06 11:21:55,774 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:55,774 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:55,774 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:21:55,774 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:21:55,774 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:55,774 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:55,774 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:55,774 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:55,775 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:55,775 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:55,775 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:55,775 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:55,775 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:55,775 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:55,806 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:55,806 DEBUG: Start:	 Training
-2016-09-06 11:21:55,807 DEBUG: Info:	 Time for Training: 0.0337290763855[s]
-2016-09-06 11:21:55,807 DEBUG: Done:	 Training
-2016-09-06 11:21:55,807 DEBUG: Start:	 Predicting
-2016-09-06 11:21:55,814 DEBUG: Done:	 Predicting
-2016-09-06 11:21:55,814 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:55,852 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:55,852 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 47
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.422222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.550724637681
-		- Score on test : 0.315789473684
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.550724637681
-		- Score on test : 0.315789473684
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.442857142857
-		- Score on test : 0.577777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.422222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.11473701202
-		- Score on test : -0.180519041032
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.564356435644
-		- Score on test : 0.342857142857
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.537735849057
-		- Score on test : 0.292682926829
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.55732946299
-		- Score on test : 0.411647585864
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.442857142857
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:21:55,852 INFO: Done:	 Result Analysis
-2016-09-06 11:21:55,945 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:55,946 DEBUG: Start:	 Training
-2016-09-06 11:21:55,964 DEBUG: Info:	 Time for Training: 0.190694093704[s]
-2016-09-06 11:21:55,964 DEBUG: Done:	 Training
-2016-09-06 11:21:55,964 DEBUG: Start:	 Predicting
-2016-09-06 11:21:55,968 DEBUG: Done:	 Predicting
-2016-09-06 11:21:55,968 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:55,996 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:55,996 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.957142857143
-		- Score on test : 0.4
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.956937799043
-		- Score on test : 0.325
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.956937799043
-		- Score on test : 0.325
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0428571428571
-		- Score on test : 0.6
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.957142857143
-		- Score on test : 0.4
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.914674537841
-		- Score on test : -0.214609988978
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.970873786408
-		- Score on test : 0.333333333333
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.943396226415
-		- Score on test : 0.317073170732
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.957275036284
-		- Score on test : 0.393230462917
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0428571428571
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 11:21:55,997 INFO: Done:	 Result Analysis
-2016-09-06 11:21:56,124 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,125 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:21:56,125 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,125 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:56,125 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:21:56,125 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:56,125 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:56,125 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:56,126 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:56,126 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:56,126 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:56,126 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:56,126 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,126 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,173 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:56,173 DEBUG: Start:	 Training
-2016-09-06 11:21:56,174 DEBUG: Info:	 Time for Training: 0.0500919818878[s]
-2016-09-06 11:21:56,174 DEBUG: Done:	 Training
-2016-09-06 11:21:56,174 DEBUG: Start:	 Predicting
-2016-09-06 11:21:56,181 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:56,181 DEBUG: Start:	 Training
-2016-09-06 11:21:56,185 DEBUG: Done:	 Predicting
-2016-09-06 11:21:56,185 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:56,202 DEBUG: Info:	 Time for Training: 0.0777740478516[s]
-2016-09-06 11:21:56,202 DEBUG: Done:	 Training
-2016-09-06 11:21:56,202 DEBUG: Start:	 Predicting
-2016-09-06 11:21:56,206 DEBUG: Done:	 Predicting
-2016-09-06 11:21:56,206 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:56,213 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:56,213 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.433333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.62443438914
-		- Score on test : 0.43956043956
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.62443438914
-		- Score on test : 0.43956043956
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.395238095238
-		- Score on test : 0.566666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.433333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.209578877963
-		- Score on test : -0.124719695673
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.4
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.650943396226
-		- Score on test : 0.487804878049
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.604317851959
-		- Score on test : 0.437779990045
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.395238095238
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,214 INFO: Done:	 Result Analysis
-2016-09-06 11:21:56,250 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:56,251 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.504672897196
-		- Score on test : 0.404761904762
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.504672897196
-		- Score on test : 0.404761904762
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.00980036362201
-		- Score on test : -0.115633266975
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.395348837209
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.509433962264
-		- Score on test : 0.414634146341
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.495101596517
-		- Score on test : 0.442010950722
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,251 INFO: Done:	 Result Analysis
-2016-09-06 11:21:56,375 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,375 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,376 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:21:56,376 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:21:56,376 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:56,376 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:56,377 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:56,377 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:56,377 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:56,377 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:56,377 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:56,377 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:56,377 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,377 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,447 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:56,448 DEBUG: Start:	 Training
-2016-09-06 11:21:56,458 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:56,458 DEBUG: Start:	 Training
-2016-09-06 11:21:56,471 DEBUG: Info:	 Time for Training: 0.0959920883179[s]
-2016-09-06 11:21:56,471 DEBUG: Done:	 Training
-2016-09-06 11:21:56,471 DEBUG: Start:	 Predicting
-2016-09-06 11:21:56,479 DEBUG: Done:	 Predicting
-2016-09-06 11:21:56,479 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:56,482 DEBUG: Info:	 Time for Training: 0.107470989227[s]
-2016-09-06 11:21:56,482 DEBUG: Done:	 Training
-2016-09-06 11:21:56,483 DEBUG: Start:	 Predicting
-2016-09-06 11:21:56,488 DEBUG: Done:	 Predicting
-2016-09-06 11:21:56,488 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:56,517 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:56,517 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.345679012346
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.345679012346
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.588888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.189573937423
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.35
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.341463414634
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.405425584868
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,518 INFO: Done:	 Result Analysis
-2016-09-06 11:21:56,536 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:56,537 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.150943396226
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.150943396226
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.5
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.096260040145
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0975609756098
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.467147834744
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,537 INFO: Done:	 Result Analysis
-2016-09-06 11:21:56,625 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,625 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,625 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:21:56,625 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:21:56,626 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:56,626 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:56,626 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:56,626 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:56,626 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:56,626 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:56,626 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:56,626 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:56,626 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,626 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,664 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:56,664 DEBUG: Start:	 Training
-2016-09-06 11:21:56,666 DEBUG: Info:	 Time for Training: 0.0415709018707[s]
-2016-09-06 11:21:56,666 DEBUG: Done:	 Training
-2016-09-06 11:21:56,666 DEBUG: Start:	 Predicting
-2016-09-06 11:21:56,669 DEBUG: Done:	 Predicting
-2016-09-06 11:21:56,669 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:56,678 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:56,678 DEBUG: Start:	 Training
-2016-09-06 11:21:56,684 DEBUG: Info:	 Time for Training: 0.059385061264[s]
-2016-09-06 11:21:56,684 DEBUG: Done:	 Training
-2016-09-06 11:21:56,684 DEBUG: Start:	 Predicting
-2016-09-06 11:21:56,688 DEBUG: Done:	 Predicting
-2016-09-06 11:21:56,688 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:56,718 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:56,718 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.418604651163
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.418604651163
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.111552687063
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.439024390244
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.44400199104
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,719 INFO: Done:	 Result Analysis
-2016-09-06 11:21:56,731 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:56,731 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.404761904762
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.404761904762
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.115633266975
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.395348837209
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.414634146341
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.442010950722
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,731 INFO: Done:	 Result Analysis
-2016-09-06 11:21:56,878 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,878 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:21:56,878 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:56,879 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:56,879 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:56,879 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:56,879 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,879 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,880 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:21:56,880 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:56,880 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:56,881 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:56,881 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:56,881 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,909 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:56,909 DEBUG: Start:	 Training
-2016-09-06 11:21:56,910 DEBUG: Info:	 Time for Training: 0.032821893692[s]
-2016-09-06 11:21:56,910 DEBUG: Done:	 Training
-2016-09-06 11:21:56,910 DEBUG: Start:	 Predicting
-2016-09-06 11:21:56,917 DEBUG: Done:	 Predicting
-2016-09-06 11:21:56,918 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:56,972 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:56,973 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 47
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.633204633205
-		- Score on test : 0.558558558559
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.633204633205
-		- Score on test : 0.558558558559
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.452380952381
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.102191547553
-		- Score on test : -0.0477019354931
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.535947712418
-		- Score on test : 0.442857142857
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.77358490566
-		- Score on test : 0.756097560976
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.545446298984
-		- Score on test : 0.480089596814
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.452380952381
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,973 INFO: Done:	 Result Analysis
-2016-09-06 11:21:57,059 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:57,059 DEBUG: Start:	 Training
-2016-09-06 11:21:57,082 DEBUG: Info:	 Time for Training: 0.203009128571[s]
-2016-09-06 11:21:57,082 DEBUG: Done:	 Training
-2016-09-06 11:21:57,082 DEBUG: Start:	 Predicting
-2016-09-06 11:21:57,088 DEBUG: Done:	 Predicting
-2016-09-06 11:21:57,088 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:57,136 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:57,136 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.976525821596
-		- Score on test : 0.516853932584
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.976525821596
-		- Score on test : 0.516853932584
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0238095238095
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.952415522541
-		- Score on test : 0.0506833064614
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.971962616822
-		- Score on test : 0.479166666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.981132075472
-		- Score on test : 0.560975609756
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.976142960813
-		- Score on test : 0.525385764062
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0238095238095
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,136 INFO: Done:	 Result Analysis
-2016-09-06 11:21:57,224 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,224 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:21:57,224 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,225 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:57,225 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:21:57,225 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:57,225 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:57,225 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:57,225 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:57,225 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:57,225 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:57,226 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:57,226 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,226 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,271 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:57,271 DEBUG: Start:	 Training
-2016-09-06 11:21:57,272 DEBUG: Info:	 Time for Training: 0.0480880737305[s]
-2016-09-06 11:21:57,272 DEBUG: Done:	 Training
-2016-09-06 11:21:57,272 DEBUG: Start:	 Predicting
-2016-09-06 11:21:57,281 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:57,281 DEBUG: Start:	 Training
-2016-09-06 11:21:57,288 DEBUG: Done:	 Predicting
-2016-09-06 11:21:57,289 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:57,310 DEBUG: Info:	 Time for Training: 0.0865099430084[s]
-2016-09-06 11:21:57,311 DEBUG: Done:	 Training
-2016-09-06 11:21:57,311 DEBUG: Start:	 Predicting
-2016-09-06 11:21:57,315 DEBUG: Done:	 Predicting
-2016-09-06 11:21:57,315 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:57,316 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:57,316 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.564593301435
-		- Score on test : 0.516853932584
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.564593301435
-		- Score on test : 0.516853932584
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.433333333333
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.133545022783
-		- Score on test : 0.0506833064614
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.572815533981
-		- Score on test : 0.479166666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.556603773585
-		- Score on test : 0.560975609756
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.566763425254
-		- Score on test : 0.525385764062
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.433333333333
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,316 INFO: Done:	 Result Analysis
-2016-09-06 11:21:57,383 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:57,383 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.535211267606
-		- Score on test : 0.475
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.535211267606
-		- Score on test : 0.475
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.471428571429
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0569743711331
-		- Score on test : 0.0555284586866
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.532710280374
-		- Score on test : 0.487179487179
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.537735849057
-		- Score on test : 0.463414634146
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.528483309144
-		- Score on test : 0.52762568442
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.471428571429
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,383 INFO: Done:	 Result Analysis
-2016-09-06 11:21:57,473 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,473 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,474 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:21:57,474 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:21:57,474 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:57,474 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:57,475 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:57,475 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:57,475 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:57,475 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:57,475 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:57,475 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:57,475 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,475 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,549 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:57,549 DEBUG: Start:	 Training
-2016-09-06 11:21:57,561 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:57,561 DEBUG: Start:	 Training
-2016-09-06 11:21:57,573 DEBUG: Info:	 Time for Training: 0.100679159164[s]
-2016-09-06 11:21:57,574 DEBUG: Done:	 Training
-2016-09-06 11:21:57,574 DEBUG: Start:	 Predicting
-2016-09-06 11:21:57,582 DEBUG: Done:	 Predicting
-2016-09-06 11:21:57,582 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:57,590 DEBUG: Info:	 Time for Training: 0.117436170578[s]
-2016-09-06 11:21:57,590 DEBUG: Done:	 Training
-2016-09-06 11:21:57,590 DEBUG: Start:	 Predicting
-2016-09-06 11:21:57,596 DEBUG: Done:	 Predicting
-2016-09-06 11:21:57,597 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:57,627 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:57,627 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511627906977
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511627906977
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.066931612238
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.536585365854
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533598805376
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,627 INFO: Done:	 Result Analysis
-2016-09-06 11:21:57,645 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:57,645 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.561403508772
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.561403508772
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0715653145323
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.438356164384
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.780487804878
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4718765555
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,645 INFO: Done:	 Result Analysis
-2016-09-06 11:21:57,719 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,719 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:21:57,719 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,719 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:57,719 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:21:57,719 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:57,720 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:57,720 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:57,720 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:57,720 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:57,720 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:57,720 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:57,720 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,720 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,753 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:57,753 DEBUG: Start:	 Training
-2016-09-06 11:21:57,755 DEBUG: Info:	 Time for Training: 0.0360541343689[s]
-2016-09-06 11:21:57,755 DEBUG: Done:	 Training
-2016-09-06 11:21:57,755 DEBUG: Start:	 Predicting
-2016-09-06 11:21:57,757 DEBUG: Done:	 Predicting
-2016-09-06 11:21:57,757 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:57,767 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:57,767 DEBUG: Start:	 Training
-2016-09-06 11:21:57,771 DEBUG: Info:	 Time for Training: 0.052619934082[s]
-2016-09-06 11:21:57,771 DEBUG: Done:	 Training
-2016-09-06 11:21:57,771 DEBUG: Start:	 Predicting
-2016-09-06 11:21:57,774 DEBUG: Done:	 Predicting
-2016-09-06 11:21:57,774 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:57,802 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:57,802 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.995305164319
-		- Score on test : 0.511627906977
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.995305164319
-		- Score on test : 0.511627906977
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.990519401324
-		- Score on test : 0.066931612238
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.990654205607
-		- Score on test : 0.488888888889
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.536585365854
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.995192307692
-		- Score on test : 0.533598805376
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,803 INFO: Done:	 Result Analysis
-2016-09-06 11:21:57,820 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:57,820 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.444444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.104031856645
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.552015928323
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,820 INFO: Done:	 Result Analysis
-2016-09-06 11:21:57,970 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,970 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,970 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:21:57,970 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:21:57,970 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:57,970 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:57,971 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:57,971 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:57,971 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:57,971 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:57,971 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:57,971 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:57,971 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,971 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,004 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:58,004 DEBUG: Start:	 Training
-2016-09-06 11:21:58,005 DEBUG: Info:	 Time for Training: 0.0356760025024[s]
-2016-09-06 11:21:58,005 DEBUG: Done:	 Training
-2016-09-06 11:21:58,005 DEBUG: Start:	 Predicting
-2016-09-06 11:21:58,013 DEBUG: Done:	 Predicting
-2016-09-06 11:21:58,013 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:58,054 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:58,054 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 47
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.477777777778
-		- Score on test : 0.342105263158
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.477777777778
-		- Score on test : 0.342105263158
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.447619047619
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.112597765671
-		- Score on test : -0.134753650348
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.581081081081
-		- Score on test : 0.371428571429
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.405660377358
-		- Score on test : 0.317073170732
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.553791727141
-		- Score on test : 0.434046789447
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.447619047619
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,054 INFO: Done:	 Result Analysis
-2016-09-06 11:21:58,134 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:58,134 DEBUG: Start:	 Training
-2016-09-06 11:21:58,151 DEBUG: Info:	 Time for Training: 0.181129932404[s]
-2016-09-06 11:21:58,151 DEBUG: Done:	 Training
-2016-09-06 11:21:58,151 DEBUG: Start:	 Predicting
-2016-09-06 11:21:58,154 DEBUG: Done:	 Predicting
-2016-09-06 11:21:58,155 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:58,182 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:58,182 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.588888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.96682464455
-		- Score on test : 0.53164556962
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.96682464455
-		- Score on test : 0.53164556962
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0333333333333
-		- Score on test : 0.411111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.588888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.933375664255
-		- Score on test : 0.166630556676
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.552631578947
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.962264150943
-		- Score on test : 0.512195121951
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.966708998549
-		- Score on test : 0.582628173221
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0333333333333
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,182 INFO: Done:	 Result Analysis
-2016-09-06 11:21:58,321 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,321 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:21:58,321 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,321 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:58,321 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:21:58,322 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:58,322 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:58,322 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:58,322 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:58,322 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:58,323 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:58,323 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:58,323 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,323 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,392 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:58,392 DEBUG: Start:	 Training
-2016-09-06 11:21:58,393 DEBUG: Info:	 Time for Training: 0.0729160308838[s]
-2016-09-06 11:21:58,393 DEBUG: Done:	 Training
-2016-09-06 11:21:58,393 DEBUG: Start:	 Predicting
-2016-09-06 11:21:58,396 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:58,396 DEBUG: Start:	 Training
-2016-09-06 11:21:58,406 DEBUG: Done:	 Predicting
-2016-09-06 11:21:58,406 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:58,415 DEBUG: Info:	 Time for Training: 0.095123052597[s]
-2016-09-06 11:21:58,416 DEBUG: Done:	 Training
-2016-09-06 11:21:58,416 DEBUG: Start:	 Predicting
-2016-09-06 11:21:58,419 DEBUG: Done:	 Predicting
-2016-09-06 11:21:58,419 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:58,431 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:58,431 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.5
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.544554455446
-		- Score on test : 0.470588235294
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.544554455446
-		- Score on test : 0.470588235294
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.438095238095
-		- Score on test : 0.5
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.5
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.125091870983
-		- Score on test : -0.00198364873142
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.572916666667
-		- Score on test : 0.454545454545
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.518867924528
-		- Score on test : 0.487804878049
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.562318577649
-		- Score on test : 0.499004479841
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.438095238095
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,431 INFO: Done:	 Result Analysis
-2016-09-06 11:21:58,448 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:58,448 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.633333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.51376146789
-		- Score on test : 0.637362637363
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.51376146789
-		- Score on test : 0.637362637363
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.366666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.633333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.0101818424163
-		- Score on test : 0.279372118308
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.58
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.528301886792
-		- Score on test : 0.707317073171
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.494920174165
-		- Score on test : 0.6393728223
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.366666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,449 INFO: Done:	 Result Analysis
-2016-09-06 11:21:58,568 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,568 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,568 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:21:58,568 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:21:58,568 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:58,568 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:58,569 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:58,569 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:58,569 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:58,569 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:58,569 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:58,569 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:58,569 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,569 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,614 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:58,614 DEBUG: Start:	 Training
-2016-09-06 11:21:58,619 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:58,619 DEBUG: Start:	 Training
-2016-09-06 11:21:58,632 DEBUG: Info:	 Time for Training: 0.064493894577[s]
-2016-09-06 11:21:58,632 DEBUG: Done:	 Training
-2016-09-06 11:21:58,632 DEBUG: Start:	 Predicting
-2016-09-06 11:21:58,637 DEBUG: Done:	 Predicting
-2016-09-06 11:21:58,637 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:58,638 DEBUG: Info:	 Time for Training: 0.0703361034393[s]
-2016-09-06 11:21:58,638 DEBUG: Done:	 Training
-2016-09-06 11:21:58,638 DEBUG: Start:	 Predicting
-2016-09-06 11:21:58,641 DEBUG: Done:	 Predicting
-2016-09-06 11:21:58,641 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:58,668 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:58,668 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0592334494774
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.428571428571
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.470383275261
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,668 INFO: Done:	 Result Analysis
-2016-09-06 11:21:58,680 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:58,680 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.995260663507
-		- Score on test : 0.479166666667
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.995260663507
-		- Score on test : 0.479166666667
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.990521113087
-		- Score on test : -0.0940733030728
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.418181818182
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.990566037736
-		- Score on test : 0.560975609756
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.995283018868
-		- Score on test : 0.453957192633
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,680 INFO: Done:	 Result Analysis
-2016-09-06 11:21:58,822 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,822 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,822 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:21:58,822 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:21:58,822 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:58,822 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:58,823 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:58,823 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:58,823 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:58,823 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:58,823 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:58,823 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:58,823 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,823 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,874 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:58,874 DEBUG: Start:	 Training
-2016-09-06 11:21:58,875 DEBUG: Info:	 Time for Training: 0.0545539855957[s]
-2016-09-06 11:21:58,876 DEBUG: Done:	 Training
-2016-09-06 11:21:58,876 DEBUG: Start:	 Predicting
-2016-09-06 11:21:58,880 DEBUG: Done:	 Predicting
-2016-09-06 11:21:58,880 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:58,899 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:58,899 DEBUG: Start:	 Training
-2016-09-06 11:21:58,905 DEBUG: Info:	 Time for Training: 0.0836429595947[s]
-2016-09-06 11:21:58,905 DEBUG: Done:	 Training
-2016-09-06 11:21:58,905 DEBUG: Start:	 Predicting
-2016-09-06 11:21:58,908 DEBUG: Done:	 Predicting
-2016-09-06 11:21:58,908 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:58,930 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:58,930 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.428571428571
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.428571428571
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0709680565554
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.418604651163
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.439024390244
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.464410154306
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,931 INFO: Done:	 Result Analysis
-2016-09-06 11:21:58,947 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:58,947 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.404761904762
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.404761904762
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.115633266975
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.395348837209
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.414634146341
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.442010950722
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,947 INFO: Done:	 Result Analysis
-2016-09-06 11:21:59,070 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:59,070 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:59,070 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:21:59,070 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:21:59,070 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:59,070 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:59,071 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:59,071 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:59,071 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:59,071 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:59,071 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:59,071 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:59,071 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:59,071 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:59,101 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:59,102 DEBUG: Start:	 Training
-2016-09-06 11:21:59,102 DEBUG: Info:	 Time for Training: 0.0325701236725[s]
-2016-09-06 11:21:59,102 DEBUG: Done:	 Training
-2016-09-06 11:21:59,102 DEBUG: Start:	 Predicting
-2016-09-06 11:21:59,108 DEBUG: Done:	 Predicting
-2016-09-06 11:21:59,108 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:59,150 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:59,150 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 47
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.411111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.562770562771
-		- Score on test : 0.453608247423
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.562770562771
-		- Score on test : 0.453608247423
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.480952380952
-		- Score on test : 0.588888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.411111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0369594857345
-		- Score on test : -0.161571085301
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.52
-		- Score on test : 0.392857142857
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.61320754717
-		- Score on test : 0.536585365854
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.518142235123
-		- Score on test : 0.421353907417
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.480952380952
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:21:59,150 INFO: Done:	 Result Analysis
-2016-09-06 11:21:59,228 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:59,228 DEBUG: Start:	 Training
-2016-09-06 11:21:59,246 DEBUG: Info:	 Time for Training: 0.176759958267[s]
-2016-09-06 11:21:59,246 DEBUG: Done:	 Training
-2016-09-06 11:21:59,246 DEBUG: Start:	 Predicting
-2016-09-06 11:21:59,250 DEBUG: Done:	 Predicting
-2016-09-06 11:21:59,250 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:59,282 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:59,282 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.488888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.976303317536
-		- Score on test : 0.410256410256
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.976303317536
-		- Score on test : 0.410256410256
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0238095238095
-		- Score on test : 0.511111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.488888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.952424147199
-		- Score on test : -0.0387937676182
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.980952380952
-		- Score on test : 0.432432432432
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.971698113208
-		- Score on test : 0.390243902439
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.976233671988
-		- Score on test : 0.480836236934
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0238095238095
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:21:59,283 INFO: Done:	 Result Analysis
-2016-09-06 11:21:59,419 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:59,419 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:59,419 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:21:59,419 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:21:59,419 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:59,419 DEBUG: Start:	 Determine Train/Test split
-2016-09-06 11:21:59,419 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:59,419 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:59,420 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:59,420 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:59,420 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:59,420 DEBUG: Done:	 Determine Train/Test split
-2016-09-06 11:21:59,420 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:59,420 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-06 11:21:59,464 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:59,464 DEBUG: Start:	 Training
-2016-09-06 11:21:59,465 DEBUG: Info:	 Time for Training: 0.0465881824493[s]
-2016-09-06 11:21:59,465 DEBUG: Done:	 Training
-2016-09-06 11:21:59,465 DEBUG: Start:	 Predicting
-2016-09-06 11:21:59,468 DEBUG: Done:	 RandomSearch best settings
-2016-09-06 11:21:59,468 DEBUG: Start:	 Training
-2016-09-06 11:21:59,481 DEBUG: Done:	 Predicting
-2016-09-06 11:21:59,481 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:59,487 DEBUG: Info:	 Time for Training: 0.0686330795288[s]
-2016-09-06 11:21:59,487 DEBUG: Done:	 Training
-2016-09-06 11:21:59,487 DEBUG: Start:	 Predicting
-2016-09-06 11:21:59,490 DEBUG: Done:	 Predicting
-2016-09-06 11:21:59,490 DEBUG: Start:	 Getting Results
-2016-09-06 11:21:59,505 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:59,505 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.559633027523
-		- Score on test : 0.478260869565
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.559633027523
-		- Score on test : 0.478260869565
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0852729302366
-		- Score on test : -0.0555284586866
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.544642857143
-		- Score on test : 0.43137254902
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.575471698113
-		- Score on test : 0.536585365854
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.542543541364
-		- Score on test : 0.47237431558
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:21:59,505 INFO: Done:	 Result Analysis
-2016-09-06 11:21:59,523 DEBUG: Done:	 Getting Results
-2016-09-06 11:21:59,523 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.566666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.610878661088
-		- Score on test : 0.571428571429
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.610878661088
-		- Score on test : 0.571428571429
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.442857142857
-		- Score on test : 0.433333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.566666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.11594977827
-		- Score on test : 0.144674846981
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.548872180451
-		- Score on test : 0.52
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.688679245283
-		- Score on test : 0.634146341463
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.55587808418
-		- Score on test : 0.572175211548
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.442857142857
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:21:59,523 INFO: Done:	 Result Analysis
-2016-09-06 11:21:59,818 INFO: ### Main Programm for Multiview Classification
-2016-09-06 11:21:59,818 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 11:21:59,819 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 11:21:59,819 INFO: Info:	 Shape of View1 :(300, 18)
-2016-09-06 11:21:59,820 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 11:21:59,820 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 11:21:59,820 INFO: Done:	 Read Database Files
-2016-09-06 11:21:59,820 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 11:21:59,823 INFO: ### Main Programm for Multiview Classification
-2016-09-06 11:21:59,823 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 11:21:59,823 INFO: Info:	 Shape of View0 :(300, 13)
-2016-09-06 11:21:59,824 INFO: Info:	 Shape of View1 :(300, 18)
-2016-09-06 11:21:59,824 INFO: Done:	 Determine validation split
-2016-09-06 11:21:59,824 INFO: Start:	 Determine 5 folds
-2016-09-06 11:21:59,824 INFO: Info:	 Shape of View2 :(300, 8)
-2016-09-06 11:21:59,825 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-06 11:21:59,825 INFO: Done:	 Read Database Files
-2016-09-06 11:21:59,825 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 11:21:59,829 INFO: Done:	 Determine validation split
-2016-09-06 11:21:59,829 INFO: Start:	 Determine 5 folds
-2016-09-06 11:21:59,830 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 11:21:59,831 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 11:21:59,831 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 11:21:59,831 INFO: Done:	 Determine folds
-2016-09-06 11:21:59,831 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-06 11:21:59,831 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 11:21:59,831 DEBUG: 	Start:	 Gridsearch for DecisionTree on View0
-2016-09-06 11:21:59,834 INFO: Info:	 Length of Learning Sets: 170
-2016-09-06 11:21:59,834 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 11:21:59,834 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 11:21:59,834 INFO: Done:	 Determine folds
-2016-09-06 11:21:59,834 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 11:21:59,834 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 11:21:59,834 DEBUG: 	Start:	 Random search for Adaboost with 30 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7310aa13..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.494382022472
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.494382022472
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.5
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.00596274193664
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.458333333333
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.536585365854
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.502986560478
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 60733f7f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.413793103448
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.413793103448
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.131912640639
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.391304347826
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.439024390244
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433797909408
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 98c9ed2a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 47
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.422222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.550724637681
-		- Score on test : 0.315789473684
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.550724637681
-		- Score on test : 0.315789473684
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.442857142857
-		- Score on test : 0.577777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.422222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.11473701202
-		- Score on test : -0.180519041032
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.564356435644
-		- Score on test : 0.342857142857
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.537735849057
-		- Score on test : 0.292682926829
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.55732946299
-		- Score on test : 0.411647585864
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.442857142857
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 11143f3f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.957142857143
-		- Score on test : 0.4
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.956937799043
-		- Score on test : 0.325
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.956937799043
-		- Score on test : 0.325
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0428571428571
-		- Score on test : 0.6
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.957142857143
-		- Score on test : 0.4
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.914674537841
-		- Score on test : -0.214609988978
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.970873786408
-		- Score on test : 0.333333333333
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.943396226415
-		- Score on test : 0.317073170732
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.957275036284
-		- Score on test : 0.393230462917
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0428571428571
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 22da83be..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.404761904762
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.404761904762
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.115633266975
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.395348837209
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.414634146341
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.442010950722
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 97bcfb03..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.418604651163
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.418604651163
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.111552687063
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.439024390244
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.44400199104
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db1e7c90..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 47
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.633204633205
-		- Score on test : 0.558558558559
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.633204633205
-		- Score on test : 0.558558558559
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.452380952381
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.102191547553
-		- Score on test : -0.0477019354931
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.535947712418
-		- Score on test : 0.442857142857
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.77358490566
-		- Score on test : 0.756097560976
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.545446298984
-		- Score on test : 0.480089596814
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.452380952381
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 52272501..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.433333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.62443438914
-		- Score on test : 0.43956043956
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.62443438914
-		- Score on test : 0.43956043956
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.395238095238
-		- Score on test : 0.566666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.604761904762
-		- Score on test : 0.433333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.209578877963
-		- Score on test : -0.124719695673
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.4
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.650943396226
-		- Score on test : 0.487804878049
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.604317851959
-		- Score on test : 0.437779990045
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.395238095238
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 023c955b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.504672897196
-		- Score on test : 0.404761904762
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.504672897196
-		- Score on test : 0.404761904762
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.00980036362201
-		- Score on test : -0.115633266975
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.395348837209
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.509433962264
-		- Score on test : 0.414634146341
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.495101596517
-		- Score on test : 0.442010950722
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4a817800..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.150943396226
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.150943396226
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.5
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.096260040145
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0975609756098
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.467147834744
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9c326ee0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 13)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.345679012346
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.345679012346
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.588888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.411111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.189573937423
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.35
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.341463414634
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.405425584868
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bc43f864..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.444444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.104031856645
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.552015928323
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c42bb400..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.995305164319
-		- Score on test : 0.511627906977
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.995305164319
-		- Score on test : 0.511627906977
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.990519401324
-		- Score on test : 0.066931612238
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.990654205607
-		- Score on test : 0.488888888889
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.536585365854
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.995192307692
-		- Score on test : 0.533598805376
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7bf769f1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.976525821596
-		- Score on test : 0.516853932584
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.976525821596
-		- Score on test : 0.516853932584
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0238095238095
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.952415522541
-		- Score on test : 0.0506833064614
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.971962616822
-		- Score on test : 0.479166666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.981132075472
-		- Score on test : 0.560975609756
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.976142960813
-		- Score on test : 0.525385764062
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0238095238095
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 45ae81a4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.564593301435
-		- Score on test : 0.516853932584
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.564593301435
-		- Score on test : 0.516853932584
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.433333333333
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.133545022783
-		- Score on test : 0.0506833064614
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.572815533981
-		- Score on test : 0.479166666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.556603773585
-		- Score on test : 0.560975609756
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.566763425254
-		- Score on test : 0.525385764062
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.433333333333
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 395792e3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.535211267606
-		- Score on test : 0.475
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.535211267606
-		- Score on test : 0.475
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.471428571429
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.528571428571
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0569743711331
-		- Score on test : 0.0555284586866
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.532710280374
-		- Score on test : 0.487179487179
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.537735849057
-		- Score on test : 0.463414634146
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.528483309144
-		- Score on test : 0.52762568442
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.471428571429
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 96ee48b4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.561403508772
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.561403508772
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0715653145323
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.438356164384
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.780487804878
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4718765555
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75cb5217..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511627906977
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511627906977
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.066931612238
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.536585365854
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533598805376
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 92282ac5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.404761904762
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.404761904762
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.115633266975
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.395348837209
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.414634146341
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.442010950722
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ed81b59a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.428571428571
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.428571428571
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0709680565554
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.418604651163
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.439024390244
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.464410154306
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bddd0960..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 47
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.477777777778
-		- Score on test : 0.342105263158
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.477777777778
-		- Score on test : 0.342105263158
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.447619047619
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.112597765671
-		- Score on test : -0.134753650348
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.581081081081
-		- Score on test : 0.371428571429
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.405660377358
-		- Score on test : 0.317073170732
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.553791727141
-		- Score on test : 0.434046789447
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.447619047619
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9d35d1a6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.588888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.96682464455
-		- Score on test : 0.53164556962
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.96682464455
-		- Score on test : 0.53164556962
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0333333333333
-		- Score on test : 0.411111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.966666666667
-		- Score on test : 0.588888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.933375664255
-		- Score on test : 0.166630556676
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.552631578947
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.962264150943
-		- Score on test : 0.512195121951
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.966708998549
-		- Score on test : 0.582628173221
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0333333333333
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 98fcb2e6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.5
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.544554455446
-		- Score on test : 0.470588235294
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.544554455446
-		- Score on test : 0.470588235294
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.438095238095
-		- Score on test : 0.5
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.5
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.125091870983
-		- Score on test : -0.00198364873142
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.572916666667
-		- Score on test : 0.454545454545
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.518867924528
-		- Score on test : 0.487804878049
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.562318577649
-		- Score on test : 0.499004479841
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.438095238095
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4471f09e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.633333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.633333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.51376146789
-		- Score on test : 0.637362637363
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.51376146789
-		- Score on test : 0.637362637363
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.366666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.633333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.0101818424163
-		- Score on test : 0.279372118308
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.58
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.528301886792
-		- Score on test : 0.707317073171
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.494920174165
-		- Score on test : 0.6393728223
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.366666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 25f841d0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.995260663507
-		- Score on test : 0.479166666667
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.995260663507
-		- Score on test : 0.479166666667
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.990521113087
-		- Score on test : -0.0940733030728
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.418181818182
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.990566037736
-		- Score on test : 0.560975609756
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.995283018868
-		- Score on test : 0.453957192633
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 70e52533..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0592334494774
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.428571428571
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.470383275261
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e57793d4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 47
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.411111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.562770562771
-		- Score on test : 0.453608247423
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.562770562771
-		- Score on test : 0.453608247423
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.480952380952
-		- Score on test : 0.588888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.519047619048
-		- Score on test : 0.411111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0369594857345
-		- Score on test : -0.161571085301
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.52
-		- Score on test : 0.392857142857
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.61320754717
-		- Score on test : 0.536585365854
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.518142235123
-		- Score on test : 0.421353907417
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.480952380952
-		- Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e0114964..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 7, max_depth : 15
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.488888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.976303317536
-		- Score on test : 0.410256410256
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.976303317536
-		- Score on test : 0.410256410256
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0238095238095
-		- Score on test : 0.511111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.97619047619
-		- Score on test : 0.488888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.952424147199
-		- Score on test : -0.0387937676182
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.980952380952
-		- Score on test : 0.432432432432
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.971698113208
-		- Score on test : 0.390243902439
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.976233671988
-		- Score on test : 0.480836236934
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0238095238095
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 08b2b5e9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.559633027523
-		- Score on test : 0.478260869565
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.559633027523
-		- Score on test : 0.478260869565
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0852729302366
-		- Score on test : -0.0555284586866
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.544642857143
-		- Score on test : 0.43137254902
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.575471698113
-		- Score on test : 0.536585365854
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.542543541364
-		- Score on test : 0.47237431558
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75c3ebcf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 2991
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.566666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.610878661088
-		- Score on test : 0.571428571429
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.610878661088
-		- Score on test : 0.571428571429
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.442857142857
-		- Score on test : 0.433333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.566666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.11594977827
-		- Score on test : 0.144674846981
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.548872180451
-		- Score on test : 0.52
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.688679245283
-		- Score on test : 0.634146341463
-	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.55587808418
-		- Score on test : 0.572175211548
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.442857142857
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161431-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-161431-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 5d2fd1b2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161431-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,50 +0,0 @@
-2016-09-06 16:14:31,063 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 16:14:31,063 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00012634375 Gbytes /!\ 
-2016-09-06 16:14:36,078 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 16:14:36,080 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 16:14:36,142 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:14:36,142 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:14:36,143 INFO: Info:	 Shape of View0 :(300, 9)
-2016-09-06 16:14:36,143 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:14:36,143 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:14:36,143 INFO: Info:	 Shape of View1 :(300, 13)
-2016-09-06 16:14:36,144 INFO: Info:	 Shape of View0 :(300, 9)
-2016-09-06 16:14:36,144 INFO: Info:	 Shape of View2 :(300, 12)
-2016-09-06 16:14:36,144 INFO: Info:	 Shape of View1 :(300, 13)
-2016-09-06 16:14:36,144 INFO: Info:	 Shape of View3 :(300, 12)
-2016-09-06 16:14:36,145 INFO: Done:	 Read Database Files
-2016-09-06 16:14:36,145 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 16:14:36,145 INFO: Info:	 Shape of View2 :(300, 12)
-2016-09-06 16:14:36,145 INFO: Info:	 Shape of View3 :(300, 12)
-2016-09-06 16:14:36,145 INFO: Done:	 Read Database Files
-2016-09-06 16:14:36,145 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 16:14:36,150 INFO: Done:	 Determine validation split
-2016-09-06 16:14:36,150 INFO: Start:	 Determine 5 folds
-2016-09-06 16:14:36,151 INFO: Done:	 Determine validation split
-2016-09-06 16:14:36,151 INFO: Start:	 Determine 5 folds
-2016-09-06 16:14:36,159 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 16:14:36,159 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 16:14:36,159 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 16:14:36,159 INFO: Done:	 Determine folds
-2016-09-06 16:14:36,159 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 16:14:36,159 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:14:36,160 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:36,162 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 16:14:36,162 INFO: Info:	 Length of Testing Sets: 42
-2016-09-06 16:14:36,162 INFO: Info:	 Length of Validation Set: 89
-2016-09-06 16:14:36,162 INFO: Done:	 Determine folds
-2016-09-06 16:14:36,162 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 16:14:36,162 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:14:36,162 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:36,589 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:14:36,589 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:36,594 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:14:36,594 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:37,006 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:14:37,006 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:37,012 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:14:37,012 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:37,423 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:14:37,423 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:37,429 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:14:37,429 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161457-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-161457-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index cd3713da..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161457-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,352 +0,0 @@
-2016-09-06 16:14:57,297 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-06 16:14:57,298 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00015446875 Gbytes /!\ 
-2016-09-06 16:15:02,311 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-06 16:15:02,313 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-06 16:15:02,362 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:02,362 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:02,362 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:02,363 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:02,363 INFO: Info:	 Shape of View0 :(300, 15)
-2016-09-06 16:15:02,363 INFO: Info:	 Shape of View0 :(300, 15)
-2016-09-06 16:15:02,363 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 16:15:02,364 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 16:15:02,364 INFO: Info:	 Shape of View2 :(300, 14)
-2016-09-06 16:15:02,364 INFO: Info:	 Shape of View2 :(300, 14)
-2016-09-06 16:15:02,364 INFO: Info:	 Shape of View3 :(300, 13)
-2016-09-06 16:15:02,364 INFO: Done:	 Read Database Files
-2016-09-06 16:15:02,365 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 16:15:02,365 INFO: Info:	 Shape of View3 :(300, 13)
-2016-09-06 16:15:02,365 INFO: Done:	 Read Database Files
-2016-09-06 16:15:02,365 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 16:15:02,369 INFO: Done:	 Determine validation split
-2016-09-06 16:15:02,369 INFO: Start:	 Determine 5 folds
-2016-09-06 16:15:02,369 INFO: Done:	 Determine validation split
-2016-09-06 16:15:02,369 INFO: Start:	 Determine 5 folds
-2016-09-06 16:15:02,375 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 16:15:02,375 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 16:15:02,375 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 16:15:02,375 INFO: Done:	 Determine folds
-2016-09-06 16:15:02,375 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 16:15:02,375 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:02,375 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:02,376 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 16:15:02,376 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 16:15:02,376 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 16:15:02,376 INFO: Done:	 Determine folds
-2016-09-06 16:15:02,376 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 16:15:02,376 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:02,377 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:02,805 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:02,805 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:02,809 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:02,809 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:03,243 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:03,243 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:03,311 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:03,311 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:03,666 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:03,666 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:03,737 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:03,737 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:04,086 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:04,161 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:05,703 INFO: Done:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:05,703 INFO: Start:	 Classification
-2016-09-06 16:15:05,703 INFO: 	Start:	 Fold number 1
-2016-09-06 16:15:05,733 INFO: 	Start: 	 Classification
-2016-09-06 16:15:05,759 INFO: 	Done: 	 Fold number 1
-2016-09-06 16:15:05,759 INFO: 	Start:	 Fold number 2
-2016-09-06 16:15:05,790 INFO: 	Start: 	 Classification
-2016-09-06 16:15:05,817 INFO: 	Done: 	 Fold number 2
-2016-09-06 16:15:05,817 INFO: 	Start:	 Fold number 3
-2016-09-06 16:15:05,848 INFO: 	Start: 	 Classification
-2016-09-06 16:15:05,875 INFO: 	Done: 	 Fold number 3
-2016-09-06 16:15:05,876 INFO: 	Start:	 Fold number 4
-2016-09-06 16:15:05,906 INFO: 	Start: 	 Classification
-2016-09-06 16:15:05,933 INFO: 	Done: 	 Fold number 4
-2016-09-06 16:15:05,933 INFO: 	Start:	 Fold number 5
-2016-09-06 16:15:05,964 INFO: 	Start: 	 Classification
-2016-09-06 16:15:05,991 INFO: 	Done: 	 Fold number 5
-2016-09-06 16:15:05,991 INFO: Done:	 Classification
-2016-09-06 16:15:05,991 INFO: Info:	 Time for Classification: 3[s]
-2016-09-06 16:15:05,992 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 16:15:06,172 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 48.2926829268
-	-On Validation : 49.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.4761253742, 0.00270164468431, 0.0406662390422, 0.480506742074
-	-With monoview classifiers : 
-		- Decision Tree with max_depth : 7
-		- Decision Tree with max_depth : 3
-		- Decision Tree with max_depth : 24
-		- Decision Tree with max_depth : 4
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:03        0:00:00
-	         Fold 2        0:00:03        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:03        0:00:00
-	         Fold 5        0:00:03        0:00:00
-	          Total        0:00:17        0:00:00
-	So a total classification time of 0:00:03.
-
-
-2016-09-06 16:15:06,172 INFO: Done:	 Result Analysis
-2016-09-06 16:15:06,570 INFO: Done:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:06,570 INFO: Start:	 Classification
-2016-09-06 16:15:06,570 INFO: 	Start:	 Fold number 1
-2016-09-06 16:15:06,599 INFO: 	Start: 	 Classification
-2016-09-06 16:15:06,667 INFO: 	Done: 	 Fold number 1
-2016-09-06 16:15:06,668 INFO: 	Start:	 Fold number 2
-2016-09-06 16:15:06,697 INFO: 	Start: 	 Classification
-2016-09-06 16:15:06,765 INFO: 	Done: 	 Fold number 2
-2016-09-06 16:15:06,765 INFO: 	Start:	 Fold number 3
-2016-09-06 16:15:06,794 INFO: 	Start: 	 Classification
-2016-09-06 16:15:06,862 INFO: 	Done: 	 Fold number 3
-2016-09-06 16:15:06,862 INFO: 	Start:	 Fold number 4
-2016-09-06 16:15:06,896 INFO: 	Start: 	 Classification
-2016-09-06 16:15:06,964 INFO: 	Done: 	 Fold number 4
-2016-09-06 16:15:06,964 INFO: 	Start:	 Fold number 5
-2016-09-06 16:15:06,993 INFO: 	Start: 	 Classification
-2016-09-06 16:15:07,061 INFO: 	Done: 	 Fold number 5
-2016-09-06 16:15:07,061 INFO: Done:	 Classification
-2016-09-06 16:15:07,061 INFO: Info:	 Time for Classification: 4[s]
-2016-09-06 16:15:07,061 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 16:15:07,194 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 46.8292682927
-	-On Validation : 44.0
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- Decision Tree with max_depth : 7
-		- Decision Tree with max_depth : 3
-		- Decision Tree with max_depth : 24
-		- Decision Tree with max_depth : 4
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:04        0:00:00
-	         Fold 2        0:00:04        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:04        0:00:00
-	         Fold 5        0:00:04        0:00:00
-	          Total        0:00:22        0:00:00
-	So a total classification time of 0:00:04.
-
-
-2016-09-06 16:15:07,195 INFO: Done:	 Result Analysis
-2016-09-06 16:15:07,318 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:07,318 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:07,319 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:07,319 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:07,319 INFO: Info:	 Shape of View0 :(300, 15)
-2016-09-06 16:15:07,319 INFO: Info:	 Shape of View0 :(300, 15)
-2016-09-06 16:15:07,320 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 16:15:07,320 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 16:15:07,321 INFO: Info:	 Shape of View2 :(300, 14)
-2016-09-06 16:15:07,321 INFO: Info:	 Shape of View2 :(300, 14)
-2016-09-06 16:15:07,321 INFO: Info:	 Shape of View3 :(300, 13)
-2016-09-06 16:15:07,321 INFO: Info:	 Shape of View3 :(300, 13)
-2016-09-06 16:15:07,321 INFO: Done:	 Read Database Files
-2016-09-06 16:15:07,321 INFO: Done:	 Read Database Files
-2016-09-06 16:15:07,322 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 16:15:07,322 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 16:15:07,327 INFO: Done:	 Determine validation split
-2016-09-06 16:15:07,328 INFO: Done:	 Determine validation split
-2016-09-06 16:15:07,328 INFO: Start:	 Determine 5 folds
-2016-09-06 16:15:07,328 INFO: Start:	 Determine 5 folds
-2016-09-06 16:15:07,339 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 16:15:07,339 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 16:15:07,339 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 16:15:07,339 INFO: Done:	 Determine folds
-2016-09-06 16:15:07,339 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 16:15:07,339 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:07,339 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:07,344 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 16:15:07,345 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 16:15:07,345 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 16:15:07,345 INFO: Done:	 Determine folds
-2016-09-06 16:15:07,345 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 16:15:07,345 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:07,345 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:08,051 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:08,051 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:08,056 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:08,057 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:08,762 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:08,762 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:08,764 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:08,765 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:09,471 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:09,471 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:09,474 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:09,474 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:10,189 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:10,190 INFO: Done:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:10,190 INFO: Start:	 Classification
-2016-09-06 16:15:10,190 INFO: 	Start:	 Fold number 1
-2016-09-06 16:15:10,192 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:10,252 INFO: 	Start: 	 Classification
-2016-09-06 16:15:10,280 INFO: 	Done: 	 Fold number 1
-2016-09-06 16:15:10,280 INFO: 	Start:	 Fold number 2
-2016-09-06 16:15:10,327 INFO: 	Start: 	 Classification
-2016-09-06 16:15:10,355 INFO: 	Done: 	 Fold number 2
-2016-09-06 16:15:10,355 INFO: 	Start:	 Fold number 3
-2016-09-06 16:15:10,402 INFO: 	Start: 	 Classification
-2016-09-06 16:15:10,430 INFO: 	Done: 	 Fold number 3
-2016-09-06 16:15:10,430 INFO: 	Start:	 Fold number 4
-2016-09-06 16:15:10,477 INFO: 	Start: 	 Classification
-2016-09-06 16:15:10,505 INFO: 	Done: 	 Fold number 4
-2016-09-06 16:15:10,505 INFO: 	Start:	 Fold number 5
-2016-09-06 16:15:10,553 INFO: 	Start: 	 Classification
-2016-09-06 16:15:10,581 INFO: 	Done: 	 Fold number 5
-2016-09-06 16:15:10,581 INFO: Done:	 Classification
-2016-09-06 16:15:10,581 INFO: Info:	 Time for Classification: 3[s]
-2016-09-06 16:15:10,581 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 16:15:10,751 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 44.8780487805
-	-On Validation : 50.6666666667
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- Decision Tree with max_depth : 7
-		- Decision Tree with max_depth : 3
-		- Decision Tree with max_depth : 24
-		- Decision Tree with max_depth : 4
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:02        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:03        0:00:00
-	         Fold 5        0:00:03        0:00:00
-	          Total        0:00:15        0:00:00
-	So a total classification time of 0:00:03.
-
-
-2016-09-06 16:15:10,765 INFO: Done:	 Result Analysis
-2016-09-06 16:15:11,829 INFO: Done:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:11,830 INFO: Start:	 Classification
-2016-09-06 16:15:11,830 INFO: 	Start:	 Fold number 1
-2016-09-06 16:15:11,858 INFO: 	Start: 	 Classification
-2016-09-06 16:15:11,888 INFO: 	Done: 	 Fold number 1
-2016-09-06 16:15:11,888 INFO: 	Start:	 Fold number 2
-2016-09-06 16:15:11,917 INFO: 	Start: 	 Classification
-2016-09-06 16:15:11,947 INFO: 	Done: 	 Fold number 2
-2016-09-06 16:15:11,947 INFO: 	Start:	 Fold number 3
-2016-09-06 16:15:11,975 INFO: 	Start: 	 Classification
-2016-09-06 16:15:12,005 INFO: 	Done: 	 Fold number 3
-2016-09-06 16:15:12,005 INFO: 	Start:	 Fold number 4
-2016-09-06 16:15:12,033 INFO: 	Start: 	 Classification
-2016-09-06 16:15:12,063 INFO: 	Done: 	 Fold number 4
-2016-09-06 16:15:12,064 INFO: 	Start:	 Fold number 5
-2016-09-06 16:15:12,092 INFO: 	Start: 	 Classification
-2016-09-06 16:15:12,122 INFO: 	Done: 	 Fold number 5
-2016-09-06 16:15:12,122 INFO: Done:	 Classification
-2016-09-06 16:15:12,122 INFO: Info:	 Time for Classification: 4[s]
-2016-09-06 16:15:12,122 INFO: Start:	 Result Analysis for Fusion
-2016-09-06 16:15:12,264 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 53.2544378698
-	-On Test : 53.6585365854
-	-On Validation : 53.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 0.111861495999, 0.371763764635, 0.0125470094031
-	-With monoview classifiers : 
-		- Decision Tree with max_depth : 7
-		- Decision Tree with max_depth : 3
-		- Decision Tree with max_depth : 24
-		- Decision Tree with max_depth : 4
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:04        0:00:00
-	         Fold 2        0:00:04        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:04        0:00:00
-	         Fold 5        0:00:04        0:00:00
-	          Total        0:00:23        0:00:00
-	So a total classification time of 0:00:04.
-
-
-2016-09-06 16:15:12,264 INFO: Done:	 Result Analysis
-2016-09-06 16:15:12,376 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:12,376 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:12,377 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:12,377 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:12,377 INFO: Info:	 Shape of View0 :(300, 15)
-2016-09-06 16:15:12,378 INFO: Info:	 Shape of View0 :(300, 15)
-2016-09-06 16:15:12,378 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 16:15:12,379 INFO: Info:	 Shape of View1 :(300, 16)
-2016-09-06 16:15:12,379 INFO: Info:	 Shape of View2 :(300, 14)
-2016-09-06 16:15:12,380 INFO: Info:	 Shape of View2 :(300, 14)
-2016-09-06 16:15:12,380 INFO: Info:	 Shape of View3 :(300, 13)
-2016-09-06 16:15:12,380 INFO: Done:	 Read Database Files
-2016-09-06 16:15:12,380 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 16:15:12,381 INFO: Info:	 Shape of View3 :(300, 13)
-2016-09-06 16:15:12,381 INFO: Done:	 Read Database Files
-2016-09-06 16:15:12,381 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-06 16:15:12,385 INFO: Done:	 Determine validation split
-2016-09-06 16:15:12,385 INFO: Start:	 Determine 5 folds
-2016-09-06 16:15:12,385 INFO: Done:	 Determine validation split
-2016-09-06 16:15:12,385 INFO: Start:	 Determine 5 folds
-2016-09-06 16:15:12,392 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 16:15:12,392 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 16:15:12,392 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 16:15:12,392 INFO: Done:	 Determine folds
-2016-09-06 16:15:12,392 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 16:15:12,393 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:12,393 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:12,393 INFO: Info:	 Length of Learning Sets: 169
-2016-09-06 16:15:12,393 INFO: Info:	 Length of Testing Sets: 41
-2016-09-06 16:15:12,393 INFO: Info:	 Length of Validation Set: 90
-2016-09-06 16:15:12,393 INFO: Done:	 Determine folds
-2016-09-06 16:15:12,393 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-06 16:15:12,393 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:12,393 DEBUG: 	Start:	 Random search for Adaboost with 30 iterations
-2016-09-06 16:15:12,822 DEBUG: 	Done:	 Random search for DecisionTree
-2016-09-06 16:15:15,451 DEBUG: 	Done:	 Random search for Adaboost
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161506Results-Fusion-LateFusion-BayesianInference-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161506Results-Fusion-LateFusion-BayesianInference-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 05f6d240..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161506Results-Fusion-LateFusion-BayesianInference-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 48.2926829268
-	-On Validation : 49.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.4761253742, 0.00270164468431, 0.0406662390422, 0.480506742074
-	-With monoview classifiers : 
-		- Decision Tree with max_depth : 7
-		- Decision Tree with max_depth : 3
-		- Decision Tree with max_depth : 24
-		- Decision Tree with max_depth : 4
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:03        0:00:00
-	         Fold 2        0:00:03        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:03        0:00:00
-	         Fold 5        0:00:03        0:00:00
-	          Total        0:00:17        0:00:00
-	So a total classification time of 0:00:03.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161507Results-Fusion-LateFusion-MajorityVoting-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161507Results-Fusion-LateFusion-MajorityVoting-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 83b87a92..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161507Results-Fusion-LateFusion-MajorityVoting-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 46.8292682927
-	-On Validation : 44.0
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- Decision Tree with max_depth : 7
-		- Decision Tree with max_depth : 3
-		- Decision Tree with max_depth : 24
-		- Decision Tree with max_depth : 4
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:04        0:00:00
-	         Fold 2        0:00:04        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:04        0:00:00
-	         Fold 5        0:00:04        0:00:00
-	          Total        0:00:22        0:00:00
-	So a total classification time of 0:00:04.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161510Results-Fusion-LateFusion-SVMForLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161510Results-Fusion-LateFusion-SVMForLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index c190e8d1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161510Results-Fusion-LateFusion-SVMForLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 44.8780487805
-	-On Validation : 50.6666666667
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- Decision Tree with max_depth : 7
-		- Decision Tree with max_depth : 3
-		- Decision Tree with max_depth : 24
-		- Decision Tree with max_depth : 4
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:02        0:00:00
-	         Fold 2        0:00:02        0:00:00
-	         Fold 3        0:00:03        0:00:00
-	         Fold 4        0:00:03        0:00:00
-	         Fold 5        0:00:03        0:00:00
-	          Total        0:00:15        0:00:00
-	So a total classification time of 0:00:03.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161512Results-Fusion-LateFusion-WeightedLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161512Results-Fusion-LateFusion-WeightedLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5e8e31ae..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161512Results-Fusion-LateFusion-WeightedLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 53.2544378698
-	-On Test : 53.6585365854
-	-On Validation : 53.3333333333
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 0.111861495999, 0.371763764635, 0.0125470094031
-	-With monoview classifiers : 
-		- Decision Tree with max_depth : 7
-		- Decision Tree with max_depth : 3
-		- Decision Tree with max_depth : 24
-		- Decision Tree with max_depth : 4
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:04        0:00:00
-	         Fold 2        0:00:04        0:00:00
-	         Fold 3        0:00:04        0:00:00
-	         Fold 4        0:00:04        0:00:00
-	         Fold 5        0:00:04        0:00:00
-	          Total        0:00:23        0:00:00
-	So a total classification time of 0:00:04.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095527-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160908-095527-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 52d4e73f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095527-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2211 +0,0 @@
-2016-09-08 09:55:28,079 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-08 09:55:28,079 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00010290625 Gbytes /!\ 
-2016-09-08 09:55:33,093 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-08 09:55:33,096 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-08 09:55:33,343 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:33,343 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:33,343 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:55:33,343 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:55:33,343 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:33,343 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:33,383 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:33,383 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:33,383 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:33,384 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:33,384 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:33,384 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:33,384 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:33,384 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:33,415 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:33,415 DEBUG: Start:	 Training
-2016-09-08 09:55:33,417 DEBUG: Info:	 Time for Training: 0.0742340087891[s]
-2016-09-08 09:55:33,417 DEBUG: Done:	 Training
-2016-09-08 09:55:33,417 DEBUG: Start:	 Predicting
-2016-09-08 09:55:33,439 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:33,440 DEBUG: Start:	 Training
-2016-09-08 09:55:33,445 DEBUG: Info:	 Time for Training: 0.102918863297[s]
-2016-09-08 09:55:33,446 DEBUG: Done:	 Training
-2016-09-08 09:55:33,446 DEBUG: Start:	 Predicting
-2016-09-08 09:55:33,583 DEBUG: Done:	 Predicting
-2016-09-08 09:55:33,583 DEBUG: Done:	 Predicting
-2016-09-08 09:55:33,584 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:33,584 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:34,228 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:34,228 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.505263157895
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.505263157895
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.522222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0418655345164
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.470588235294
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.545454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.479249011858
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,228 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:34,228 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.484210526316
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.484210526316
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0867214643554
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.450980392157
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522727272727
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.457015810277
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,228 INFO: Done:	 Result Analysis
-2016-09-08 09:55:34,228 INFO: Done:	 Result Analysis
-2016-09-08 09:55:34,286 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,286 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,287 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:55:34,287 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:55:34,287 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:34,287 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:34,287 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,287 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,288 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,288 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,288 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:34,288 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:34,288 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,288 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,320 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:34,320 DEBUG: Start:	 Training
-2016-09-08 09:55:34,321 DEBUG: Info:	 Time for Training: 0.0349078178406[s]
-2016-09-08 09:55:34,321 DEBUG: Done:	 Training
-2016-09-08 09:55:34,321 DEBUG: Start:	 Predicting
-2016-09-08 09:55:34,326 DEBUG: Done:	 Predicting
-2016-09-08 09:55:34,326 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:34,371 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:34,371 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.661904761905
-		- Score on test : 0.477777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.727969348659
-		- Score on test : 0.534653465347
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.727969348659
-		- Score on test : 0.534653465347
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.338095238095
-		- Score on test : 0.522222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.661904761905
-		- Score on test : 0.477777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.290672377783
-		- Score on test : -0.0399755963154
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.68345323741
-		- Score on test : 0.473684210526
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.77868852459
-		- Score on test : 0.613636363636
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.639344262295
-		- Score on test : 0.480731225296
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.338095238095
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,371 INFO: Done:	 Result Analysis
-2016-09-08 09:55:34,386 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:34,387 DEBUG: Start:	 Training
-2016-09-08 09:55:34,397 DEBUG: Info:	 Time for Training: 0.11102604866[s]
-2016-09-08 09:55:34,397 DEBUG: Done:	 Training
-2016-09-08 09:55:34,397 DEBUG: Start:	 Predicting
-2016-09-08 09:55:34,400 DEBUG: Done:	 Predicting
-2016-09-08 09:55:34,401 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:34,429 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:34,429 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.895238095238
-		- Score on test : 0.477777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.905982905983
-		- Score on test : 0.515463917526
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.905982905983
-		- Score on test : 0.515463917526
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.104761904762
-		- Score on test : 0.522222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.895238095238
-		- Score on test : 0.477777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.791868570857
-		- Score on test : -0.0411594726194
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.946428571429
-		- Score on test : 0.471698113208
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.868852459016
-		- Score on test : 0.568181818182
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.900335320417
-		- Score on test : 0.479743083004
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.104761904762
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,430 INFO: Done:	 Result Analysis
-2016-09-08 09:55:34,537 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,537 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:55:34,537 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:34,537 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,538 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:55:34,538 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:34,538 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,538 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,538 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:34,538 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,538 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,538 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,538 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:34,539 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,668 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:34,668 DEBUG: Start:	 Training
-2016-09-08 09:55:34,686 DEBUG: Info:	 Time for Training: 0.148998022079[s]
-2016-09-08 09:55:34,686 DEBUG: Done:	 Training
-2016-09-08 09:55:34,686 DEBUG: Start:	 Predicting
-2016-09-08 09:55:34,689 DEBUG: Done:	 Predicting
-2016-09-08 09:55:34,689 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:34,699 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:34,699 DEBUG: Start:	 Training
-2016-09-08 09:55:34,700 DEBUG: Info:	 Time for Training: 0.164316892624[s]
-2016-09-08 09:55:34,700 DEBUG: Done:	 Training
-2016-09-08 09:55:34,700 DEBUG: Start:	 Predicting
-2016-09-08 09:55:34,711 DEBUG: Done:	 Predicting
-2016-09-08 09:55:34,711 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:34,727 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:34,727 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.377777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.563265306122
-		- Score on test : 0.416666666667
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.563265306122
-		- Score on test : 0.416666666667
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.622222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.377777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.0481411286791
-		- Score on test : -0.244017569898
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.560975609756
-		- Score on test : 0.384615384615
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.565573770492
-		- Score on test : 0.454545454545
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.475968703428
-		- Score on test : 0.379446640316
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,727 INFO: Done:	 Result Analysis
-2016-09-08 09:55:34,737 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:34,737 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.749226006192
-		- Score on test : 0.630769230769
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.749226006192
-		- Score on test : 0.630769230769
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.385714285714
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.201498784613
-		- Score on test : -0.112653159931
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.601990049751
-		- Score on test : 0.476744186047
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.991803278689
-		- Score on test : 0.931818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.541356184799
-		- Score on test : 0.476778656126
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.385714285714
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,738 INFO: Done:	 Result Analysis
-2016-09-08 09:55:34,880 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,881 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,881 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:55:34,881 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:55:34,881 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:34,881 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:34,881 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,881 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,882 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,882 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,882 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:34,882 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:34,882 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,882 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,928 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:34,928 DEBUG: Start:	 Training
-2016-09-08 09:55:34,928 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:34,929 DEBUG: Start:	 Training
-2016-09-08 09:55:34,944 DEBUG: Info:	 Time for Training: 0.0635361671448[s]
-2016-09-08 09:55:34,944 DEBUG: Done:	 Training
-2016-09-08 09:55:34,944 DEBUG: Start:	 Predicting
-2016-09-08 09:55:34,945 DEBUG: Info:	 Time for Training: 0.0647149085999[s]
-2016-09-08 09:55:34,945 DEBUG: Done:	 Training
-2016-09-08 09:55:34,945 DEBUG: Start:	 Predicting
-2016-09-08 09:55:34,947 DEBUG: Done:	 Predicting
-2016-09-08 09:55:34,947 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:34,950 DEBUG: Done:	 Predicting
-2016-09-08 09:55:34,950 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:34,979 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:34,979 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.468085106383
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.468085106383
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.109345881217
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.44
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.445652173913
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,980 INFO: Done:	 Result Analysis
-2016-09-08 09:55:34,995 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:34,995 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.616822429907
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.616822429907
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.106710653456
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.52380952381
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.75
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.548913043478
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,995 INFO: Done:	 Result Analysis
-2016-09-08 09:55:35,123 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,123 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,123 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:55:35,123 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:55:35,123 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:35,123 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:35,124 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,125 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,125 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,125 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:35,125 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,125 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,125 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:35,125 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,163 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:35,163 DEBUG: Start:	 Training
-2016-09-08 09:55:35,165 DEBUG: Info:	 Time for Training: 0.0432438850403[s]
-2016-09-08 09:55:35,165 DEBUG: Done:	 Training
-2016-09-08 09:55:35,165 DEBUG: Start:	 Predicting
-2016-09-08 09:55:35,168 DEBUG: Done:	 Predicting
-2016-09-08 09:55:35,168 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:35,177 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:35,177 DEBUG: Start:	 Training
-2016-09-08 09:55:35,181 DEBUG: Info:	 Time for Training: 0.0592088699341[s]
-2016-09-08 09:55:35,181 DEBUG: Done:	 Training
-2016-09-08 09:55:35,181 DEBUG: Start:	 Predicting
-2016-09-08 09:55:35,184 DEBUG: Done:	 Predicting
-2016-09-08 09:55:35,184 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:35,215 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:35,215 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.48
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.48
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.154858431981
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.428571428571
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.545454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.424901185771
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,216 INFO: Done:	 Result Analysis
-2016-09-08 09:55:35,220 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:35,220 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.48
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.48
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.154858431981
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.428571428571
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.545454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.424901185771
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,220 INFO: Done:	 Result Analysis
-2016-09-08 09:55:35,370 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,370 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:55:35,370 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,370 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:35,370 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:55:35,370 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:35,371 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,371 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,371 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,371 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:35,371 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,371 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,371 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:35,371 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,402 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:35,403 DEBUG: Start:	 Training
-2016-09-08 09:55:35,403 DEBUG: Info:	 Time for Training: 0.0339629650116[s]
-2016-09-08 09:55:35,403 DEBUG: Done:	 Training
-2016-09-08 09:55:35,403 DEBUG: Start:	 Predicting
-2016-09-08 09:55:35,409 DEBUG: Done:	 Predicting
-2016-09-08 09:55:35,409 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:35,450 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:35,450 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.6484375
-		- Score on test : 0.541666666667
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.6484375
-		- Score on test : 0.541666666667
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.428571428571
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.103477711187
-		- Score on test : 0.0260018722022
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.619402985075
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.680327868852
-		- Score on test : 0.590909090909
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.550391207154
-		- Score on test : 0.512845849802
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.428571428571
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,450 INFO: Done:	 Result Analysis
-2016-09-08 09:55:35,480 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:35,481 DEBUG: Start:	 Training
-2016-09-08 09:55:35,491 DEBUG: Info:	 Time for Training: 0.121489048004[s]
-2016-09-08 09:55:35,491 DEBUG: Done:	 Training
-2016-09-08 09:55:35,491 DEBUG: Start:	 Predicting
-2016-09-08 09:55:35,495 DEBUG: Done:	 Predicting
-2016-09-08 09:55:35,495 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:35,527 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:35,527 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.9
-		- Score on test : 0.477777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.909090909091
-		- Score on test : 0.356164383562
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.909090909091
-		- Score on test : 0.356164383562
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.1
-		- Score on test : 0.522222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.9
-		- Score on test : 0.477777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.805030105216
-		- Score on test : -0.0560191732057
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.963302752294
-		- Score on test : 0.448275862069
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.860655737705
-		- Score on test : 0.295454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.907600596125
-		- Score on test : 0.473814229249
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.1
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,528 INFO: Done:	 Result Analysis
-2016-09-08 09:55:35,610 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,610 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,610 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:55:35,610 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:55:35,610 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:35,610 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:35,611 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,611 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,611 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,611 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,611 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:35,611 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:35,611 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,611 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,656 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:35,656 DEBUG: Start:	 Training
-2016-09-08 09:55:35,657 DEBUG: Info:	 Time for Training: 0.0472548007965[s]
-2016-09-08 09:55:35,657 DEBUG: Done:	 Training
-2016-09-08 09:55:35,657 DEBUG: Start:	 Predicting
-2016-09-08 09:55:35,660 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:35,661 DEBUG: Start:	 Training
-2016-09-08 09:55:35,680 DEBUG: Done:	 Predicting
-2016-09-08 09:55:35,680 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:35,681 DEBUG: Info:	 Time for Training: 0.0712029933929[s]
-2016-09-08 09:55:35,681 DEBUG: Done:	 Training
-2016-09-08 09:55:35,681 DEBUG: Start:	 Predicting
-2016-09-08 09:55:35,684 DEBUG: Done:	 Predicting
-2016-09-08 09:55:35,684 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:35,704 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:35,704 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.745341614907
-		- Score on test : 0.661417322835
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.745341614907
-		- Score on test : 0.661417322835
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.390476190476
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.172644893682
-		- Score on test : 0.118036588599
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.506024096386
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.983606557377
-		- Score on test : 0.954545454545
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.537257824143
-		- Score on test : 0.53162055336
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.390476190476
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,704 INFO: Done:	 Result Analysis
-2016-09-08 09:55:35,714 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:35,715 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.606557377049
-		- Score on test : 0.584905660377
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.606557377049
-		- Score on test : 0.584905660377
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0611028315946
-		- Score on test : 0.0330758927464
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.606557377049
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.606557377049
-		- Score on test : 0.704545454545
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.530551415797
-		- Score on test : 0.515316205534
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,715 INFO: Done:	 Result Analysis
-2016-09-08 09:55:35,860 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,860 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,860 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:55:35,860 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:55:35,860 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:35,860 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:35,861 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,861 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,861 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,861 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,861 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:35,861 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:35,861 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,861 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,908 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:35,908 DEBUG: Start:	 Training
-2016-09-08 09:55:35,910 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:35,910 DEBUG: Start:	 Training
-2016-09-08 09:55:35,925 DEBUG: Info:	 Time for Training: 0.0658419132233[s]
-2016-09-08 09:55:35,925 DEBUG: Done:	 Training
-2016-09-08 09:55:35,925 DEBUG: Start:	 Predicting
-2016-09-08 09:55:35,926 DEBUG: Info:	 Time for Training: 0.06693816185[s]
-2016-09-08 09:55:35,926 DEBUG: Done:	 Training
-2016-09-08 09:55:35,926 DEBUG: Start:	 Predicting
-2016-09-08 09:55:35,929 DEBUG: Done:	 Predicting
-2016-09-08 09:55:35,929 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:35,932 DEBUG: Done:	 Predicting
-2016-09-08 09:55:35,932 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:35,962 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:35,962 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.358208955224
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.358208955224
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0385036888617
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.521739130435
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.272727272727
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.516798418972
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,962 INFO: Done:	 Result Analysis
-2016-09-08 09:55:35,967 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:35,967 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.645161290323
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.645161290323
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0628694613462
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.909090909091
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.51976284585
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,967 INFO: Done:	 Result Analysis
-2016-09-08 09:55:36,109 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,109 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:55:36,110 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:36,110 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,110 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:55:36,111 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:36,111 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,111 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,111 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:36,111 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,112 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,112 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,112 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:36,112 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,145 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:36,145 DEBUG: Start:	 Training
-2016-09-08 09:55:36,146 DEBUG: Info:	 Time for Training: 0.0371689796448[s]
-2016-09-08 09:55:36,146 DEBUG: Done:	 Training
-2016-09-08 09:55:36,146 DEBUG: Start:	 Predicting
-2016-09-08 09:55:36,149 DEBUG: Done:	 Predicting
-2016-09-08 09:55:36,149 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:36,159 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:36,159 DEBUG: Start:	 Training
-2016-09-08 09:55:36,163 DEBUG: Info:	 Time for Training: 0.0549581050873[s]
-2016-09-08 09:55:36,163 DEBUG: Done:	 Training
-2016-09-08 09:55:36,163 DEBUG: Start:	 Predicting
-2016-09-08 09:55:36,166 DEBUG: Done:	 Predicting
-2016-09-08 09:55:36,166 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:36,197 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:36,197 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.441860465116
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.441860465116
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.068316965625
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.452380952381
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.431818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.465909090909
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,197 INFO: Done:	 Result Analysis
-2016-09-08 09:55:36,208 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:36,208 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.436781609195
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.436781609195
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0899876638096
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.441860465116
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.431818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455039525692
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,209 INFO: Done:	 Result Analysis
-2016-09-08 09:55:36,356 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,356 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:55:36,356 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:36,356 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,357 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:55:36,357 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:36,357 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,357 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,357 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,358 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:36,358 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,358 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,358 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:36,358 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,387 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:36,388 DEBUG: Start:	 Training
-2016-09-08 09:55:36,388 DEBUG: Info:	 Time for Training: 0.0327939987183[s]
-2016-09-08 09:55:36,388 DEBUG: Done:	 Training
-2016-09-08 09:55:36,388 DEBUG: Start:	 Predicting
-2016-09-08 09:55:36,394 DEBUG: Done:	 Predicting
-2016-09-08 09:55:36,394 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:36,437 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:36,437 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.705882352941
-		- Score on test : 0.619469026549
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.705882352941
-		- Score on test : 0.619469026549
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.380952380952
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.189221481343
-		- Score on test : 0.0665679839847
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.64
-		- Score on test : 0.507246376812
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.786885245902
-		- Score on test : 0.795454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.586624441133
-		- Score on test : 0.528162055336
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.380952380952
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,437 INFO: Done:	 Result Analysis
-2016-09-08 09:55:36,463 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:36,463 DEBUG: Start:	 Training
-2016-09-08 09:55:36,473 DEBUG: Info:	 Time for Training: 0.11700296402[s]
-2016-09-08 09:55:36,473 DEBUG: Done:	 Training
-2016-09-08 09:55:36,473 DEBUG: Start:	 Predicting
-2016-09-08 09:55:36,477 DEBUG: Done:	 Predicting
-2016-09-08 09:55:36,477 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:36,509 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:36,509 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.4
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.904564315353
-		- Score on test : 0.357142857143
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.904564315353
-		- Score on test : 0.357142857143
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.109523809524
-		- Score on test : 0.6
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.4
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.77645053118
-		- Score on test : -0.20378096045
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.915966386555
-		- Score on test : 0.375
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.893442622951
-		- Score on test : 0.340909090909
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.889903129657
-		- Score on test : 0.39871541502
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.109523809524
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,509 INFO: Done:	 Result Analysis
-2016-09-08 09:55:36,597 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,597 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:55:36,597 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,597 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:36,598 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:55:36,598 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:36,598 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,598 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,598 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:36,598 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,598 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,598 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,599 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:36,599 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,645 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:36,645 DEBUG: Start:	 Training
-2016-09-08 09:55:36,646 DEBUG: Info:	 Time for Training: 0.0489931106567[s]
-2016-09-08 09:55:36,646 DEBUG: Done:	 Training
-2016-09-08 09:55:36,646 DEBUG: Start:	 Predicting
-2016-09-08 09:55:36,651 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:36,651 DEBUG: Start:	 Training
-2016-09-08 09:55:36,658 DEBUG: Done:	 Predicting
-2016-09-08 09:55:36,658 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:36,670 DEBUG: Info:	 Time for Training: 0.0731010437012[s]
-2016-09-08 09:55:36,670 DEBUG: Done:	 Training
-2016-09-08 09:55:36,670 DEBUG: Start:	 Predicting
-2016-09-08 09:55:36,674 DEBUG: Done:	 Predicting
-2016-09-08 09:55:36,674 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:36,682 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:36,683 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.715231788079
-		- Score on test : 0.676923076923
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.715231788079
-		- Score on test : 0.676923076923
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0945615027077
-		- Score on test : 0.210925065403
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.511627906977
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.885245901639
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.533532041729
-		- Score on test : 0.54347826087
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,683 INFO: Done:	 Result Analysis
-2016-09-08 09:55:36,702 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:36,703 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.433333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.604838709677
-		- Score on test : 0.484848484848
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.604838709677
-		- Score on test : 0.484848484848
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.566666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.433333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0354605635154
-		- Score on test : -0.131720304791
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.436363636364
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.614754098361
-		- Score on test : 0.545454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.517604321908
-		- Score on test : 0.435770750988
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,703 INFO: Done:	 Result Analysis
-2016-09-08 09:55:36,843 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,843 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:55:36,843 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,843 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:36,843 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:55:36,843 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:36,843 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,844 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,844 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,844 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,844 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:36,844 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:36,844 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,844 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,891 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:36,891 DEBUG: Start:	 Training
-2016-09-08 09:55:36,892 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:36,892 DEBUG: Start:	 Training
-2016-09-08 09:55:36,907 DEBUG: Info:	 Time for Training: 0.0647799968719[s]
-2016-09-08 09:55:36,907 DEBUG: Done:	 Training
-2016-09-08 09:55:36,907 DEBUG: Start:	 Predicting
-2016-09-08 09:55:36,908 DEBUG: Info:	 Time for Training: 0.0657360553741[s]
-2016-09-08 09:55:36,908 DEBUG: Done:	 Training
-2016-09-08 09:55:36,908 DEBUG: Start:	 Predicting
-2016-09-08 09:55:36,910 DEBUG: Done:	 Predicting
-2016-09-08 09:55:36,910 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:36,913 DEBUG: Done:	 Predicting
-2016-09-08 09:55:36,913 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:36,946 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:36,946 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.357142857143
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.357142857143
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.6
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.20378096045
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.375
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.340909090909
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.39871541502
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,946 INFO: Done:	 Result Analysis
-2016-09-08 09:55:36,952 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:36,952 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.538461538462
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.538461538462
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0628694613462
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.636363636364
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.470355731225
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,952 INFO: Done:	 Result Analysis
-2016-09-08 09:55:37,093 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,093 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,093 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:55:37,093 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:37,093 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:55:37,093 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:37,093 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,093 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,094 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,094 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,094 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:37,094 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:37,094 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,094 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,124 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:37,124 DEBUG: Start:	 Training
-2016-09-08 09:55:37,125 DEBUG: Info:	 Time for Training: 0.0331890583038[s]
-2016-09-08 09:55:37,126 DEBUG: Done:	 Training
-2016-09-08 09:55:37,126 DEBUG: Start:	 Predicting
-2016-09-08 09:55:37,128 DEBUG: Done:	 Predicting
-2016-09-08 09:55:37,128 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:37,139 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:37,139 DEBUG: Start:	 Training
-2016-09-08 09:55:37,142 DEBUG: Info:	 Time for Training: 0.0498540401459[s]
-2016-09-08 09:55:37,142 DEBUG: Done:	 Training
-2016-09-08 09:55:37,142 DEBUG: Start:	 Predicting
-2016-09-08 09:55:37,145 DEBUG: Done:	 Predicting
-2016-09-08 09:55:37,145 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:37,172 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:37,172 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588235294118
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588235294118
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0763602735229
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.51724137931
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.681818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.536561264822
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,173 INFO: Done:	 Result Analysis
-2016-09-08 09:55:37,183 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:37,184 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.576923076923
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.576923076923
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0314347306731
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.681818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.514822134387
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,184 INFO: Done:	 Result Analysis
-2016-09-08 09:55:37,335 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,335 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,335 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:55:37,335 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:55:37,335 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:37,335 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:37,336 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,336 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,336 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,336 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,336 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:37,336 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:37,336 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,336 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,365 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:37,365 DEBUG: Start:	 Training
-2016-09-08 09:55:37,365 DEBUG: Info:	 Time for Training: 0.0312879085541[s]
-2016-09-08 09:55:37,365 DEBUG: Done:	 Training
-2016-09-08 09:55:37,366 DEBUG: Start:	 Predicting
-2016-09-08 09:55:37,370 DEBUG: Done:	 Predicting
-2016-09-08 09:55:37,371 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:37,421 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:37,422 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.681481481481
-		- Score on test : 0.601941747573
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.681481481481
-		- Score on test : 0.601941747573
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.127350050081
-		- Score on test : 0.100829966549
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.621621621622
-		- Score on test : 0.525423728814
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.754098360656
-		- Score on test : 0.704545454545
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.558867362146
-		- Score on test : 0.547924901186
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,422 INFO: Done:	 Result Analysis
-2016-09-08 09:55:37,439 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:37,439 DEBUG: Start:	 Training
-2016-09-08 09:55:37,449 DEBUG: Info:	 Time for Training: 0.114704847336[s]
-2016-09-08 09:55:37,449 DEBUG: Done:	 Training
-2016-09-08 09:55:37,449 DEBUG: Start:	 Predicting
-2016-09-08 09:55:37,452 DEBUG: Done:	 Predicting
-2016-09-08 09:55:37,453 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:37,485 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:37,485 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.9
-		- Score on test : 0.577777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.910638297872
-		- Score on test : 0.586956521739
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.910638297872
-		- Score on test : 0.586956521739
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.1
-		- Score on test : 0.422222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.9
-		- Score on test : 0.577777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.800522373751
-		- Score on test : 0.157426051223
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.946902654867
-		- Score on test : 0.5625
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.877049180328
-		- Score on test : 0.613636363636
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.904433681073
-		- Score on test : 0.578557312253
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.1
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,486 INFO: Done:	 Result Analysis
-2016-09-08 09:55:37,579 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,579 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:55:37,579 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,579 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:37,579 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:55:37,579 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:55:37,579 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,580 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,580 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,580 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:37,580 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,580 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:55:37,580 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,580 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,625 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:37,625 DEBUG: Start:	 Training
-2016-09-08 09:55:37,626 DEBUG: Info:	 Time for Training: 0.0474660396576[s]
-2016-09-08 09:55:37,626 DEBUG: Done:	 Training
-2016-09-08 09:55:37,626 DEBUG: Start:	 Predicting
-2016-09-08 09:55:37,627 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:55:37,627 DEBUG: Start:	 Training
-2016-09-08 09:55:37,643 DEBUG: Info:	 Time for Training: 0.0650768280029[s]
-2016-09-08 09:55:37,644 DEBUG: Done:	 Training
-2016-09-08 09:55:37,644 DEBUG: Start:	 Predicting
-2016-09-08 09:55:37,647 DEBUG: Done:	 Predicting
-2016-09-08 09:55:37,647 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:37,652 DEBUG: Done:	 Predicting
-2016-09-08 09:55:37,652 DEBUG: Start:	 Getting Results
-2016-09-08 09:55:37,675 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:37,675 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.488888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.739393939394
-		- Score on test : 0.65671641791
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.739393939394
-		- Score on test : 0.65671641791
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.511111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.488888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.115457436228
-		- Score on test : 0.0
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.586538461538
-		- Score on test : 0.488888888889
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.511363636364
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,675 INFO: Done:	 Result Analysis
-2016-09-08 09:55:37,686 DEBUG: Done:	 Getting Results
-2016-09-08 09:55:37,686 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.595419847328
-		- Score on test : 0.550458715596
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.595419847328
-		- Score on test : 0.550458715596
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.0682438863041
-		- Score on test : -0.0882242643891
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.461538461538
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.639344262295
-		- Score on test : 0.681818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.467399403875
-		- Score on test : 0.4604743083
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,686 INFO: Done:	 Result Analysis
-2016-09-08 09:55:37,978 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:55:37,979 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-08 09:55:37,980 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-08 09:55:37,981 INFO: Info:	 Shape of View1 :(300, 15)
-2016-09-08 09:55:37,982 INFO: Info:	 Shape of View2 :(300, 7)
-2016-09-08 09:55:37,983 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-08 09:55:37,983 INFO: Done:	 Read Database Files
-2016-09-08 09:55:37,983 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-08 09:55:37,987 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:55:37,987 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-08 09:55:37,987 INFO: Done:	 Determine validation split
-2016-09-08 09:55:37,987 INFO: Start:	 Determine 5 folds
-2016-09-08 09:55:37,988 INFO: Info:	 Shape of View0 :(300, 8)
-2016-09-08 09:55:37,988 INFO: Info:	 Shape of View1 :(300, 15)
-2016-09-08 09:55:37,989 INFO: Info:	 Shape of View2 :(300, 7)
-2016-09-08 09:55:37,989 INFO: Info:	 Shape of View3 :(300, 6)
-2016-09-08 09:55:37,989 INFO: Done:	 Read Database Files
-2016-09-08 09:55:37,989 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-08 09:55:37,993 INFO: Done:	 Determine validation split
-2016-09-08 09:55:37,993 INFO: Start:	 Determine 5 folds
-2016-09-08 09:55:37,995 INFO: Info:	 Length of Learning Sets: 170
-2016-09-08 09:55:37,995 INFO: Info:	 Length of Testing Sets: 41
-2016-09-08 09:55:37,995 INFO: Info:	 Length of Validation Set: 89
-2016-09-08 09:55:37,995 INFO: Done:	 Determine folds
-2016-09-08 09:55:37,995 INFO: Start:	 Learning with Mumbo and 5 folds
-2016-09-08 09:55:37,996 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-08 09:55:37,996 DEBUG: 	Start:	 Random search for DecisionTree on View0
-2016-09-08 09:55:37,999 INFO: Info:	 Length of Learning Sets: 170
-2016-09-08 09:55:38,000 INFO: Info:	 Length of Testing Sets: 41
-2016-09-08 09:55:38,000 INFO: Info:	 Length of Validation Set: 89
-2016-09-08 09:55:38,000 INFO: Done:	 Determine folds
-2016-09-08 09:55:38,000 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-08 09:55:38,000 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-08 09:55:38,000 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:55:38,054 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:55:38,054 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:55:38,107 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:55:38,107 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:55:38,157 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:55:38,157 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:55:38,212 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:55:38,284 INFO: Done:	 Randomsearching best settings for monoview classifiers
-2016-09-08 09:55:38,284 INFO: Start:	 Classification
-2016-09-08 09:55:38,284 INFO: 	Start:	 Fold number 1
-2016-09-08 09:55:38,311 INFO: 	Start: 	 Classification
-2016-09-08 09:55:38,336 INFO: 	Done: 	 Fold number 1
-2016-09-08 09:55:38,337 INFO: 	Start:	 Fold number 2
-2016-09-08 09:55:38,363 INFO: 	Start: 	 Classification
-2016-09-08 09:55:38,389 INFO: 	Done: 	 Fold number 2
-2016-09-08 09:55:38,389 INFO: 	Start:	 Fold number 3
-2016-09-08 09:55:38,416 INFO: 	Start: 	 Classification
-2016-09-08 09:55:38,443 INFO: 	Done: 	 Fold number 3
-2016-09-08 09:55:38,443 INFO: 	Start:	 Fold number 4
-2016-09-08 09:55:38,470 INFO: 	Start: 	 Classification
-2016-09-08 09:55:38,496 INFO: 	Done: 	 Fold number 4
-2016-09-08 09:55:38,496 INFO: 	Start:	 Fold number 5
-2016-09-08 09:55:38,523 INFO: 	Start: 	 Classification
-2016-09-08 09:55:38,549 INFO: 	Done: 	 Fold number 5
-2016-09-08 09:55:38,549 INFO: Done:	 Classification
-2016-09-08 09:55:38,549 INFO: Info:	 Time for Classification: 0[s]
-2016-09-08 09:55:38,550 INFO: Start:	 Result Analysis for Fusion
-2016-09-08 09:55:38,681 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 57.1764705882
-	-On Test : 52.6829268293
-	-On Validation : 52.1348314607
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.322967175445, 0.0184701333132, 0.322597810111, 0.335964881131
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-08 09:55:38,682 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0d0cf3ff..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.505263157895
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.505263157895
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.522222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.477777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0418655345164
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.470588235294
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.545454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.479249011858
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9f126a9e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.484210526316
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.484210526316
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0867214643554
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.450980392157
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522727272727
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.457015810277
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 79be41be..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.661904761905
-		- Score on test : 0.477777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.727969348659
-		- Score on test : 0.534653465347
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.727969348659
-		- Score on test : 0.534653465347
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.338095238095
-		- Score on test : 0.522222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.661904761905
-		- Score on test : 0.477777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.290672377783
-		- Score on test : -0.0399755963154
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.68345323741
-		- Score on test : 0.473684210526
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.77868852459
-		- Score on test : 0.613636363636
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.639344262295
-		- Score on test : 0.480731225296
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.338095238095
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bbd62487..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.895238095238
-		- Score on test : 0.477777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.905982905983
-		- Score on test : 0.515463917526
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.905982905983
-		- Score on test : 0.515463917526
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.104761904762
-		- Score on test : 0.522222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.895238095238
-		- Score on test : 0.477777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.791868570857
-		- Score on test : -0.0411594726194
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.946428571429
-		- Score on test : 0.471698113208
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.868852459016
-		- Score on test : 0.568181818182
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.900335320417
-		- Score on test : 0.479743083004
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.104761904762
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fd413a5d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.749226006192
-		- Score on test : 0.630769230769
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.749226006192
-		- Score on test : 0.630769230769
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.385714285714
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.614285714286
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.201498784613
-		- Score on test : -0.112653159931
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.601990049751
-		- Score on test : 0.476744186047
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.991803278689
-		- Score on test : 0.931818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.541356184799
-		- Score on test : 0.476778656126
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.385714285714
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7b2f009c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.377777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.563265306122
-		- Score on test : 0.416666666667
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.563265306122
-		- Score on test : 0.416666666667
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.622222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.377777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.0481411286791
-		- Score on test : -0.244017569898
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.560975609756
-		- Score on test : 0.384615384615
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.565573770492
-		- Score on test : 0.454545454545
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.475968703428
-		- Score on test : 0.379446640316
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db536ff6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.468085106383
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.468085106383
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.109345881217
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.44
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.445652173913
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4251899b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 8)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.616822429907
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.616822429907
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.106710653456
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.52380952381
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.75
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.548913043478
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a32b018a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.48
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.48
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.154858431981
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.428571428571
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.545454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.424901185771
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b096d574..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.48
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.48
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.154858431981
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.428571428571
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.545454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.424901185771
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 376343e6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.6484375
-		- Score on test : 0.541666666667
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.6484375
-		- Score on test : 0.541666666667
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.428571428571
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.103477711187
-		- Score on test : 0.0260018722022
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.619402985075
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.680327868852
-		- Score on test : 0.590909090909
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.550391207154
-		- Score on test : 0.512845849802
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.428571428571
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c3e2e299..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.9
-		- Score on test : 0.477777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.909090909091
-		- Score on test : 0.356164383562
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.909090909091
-		- Score on test : 0.356164383562
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.1
-		- Score on test : 0.522222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.9
-		- Score on test : 0.477777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.805030105216
-		- Score on test : -0.0560191732057
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.963302752294
-		- Score on test : 0.448275862069
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.860655737705
-		- Score on test : 0.295454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.907600596125
-		- Score on test : 0.473814229249
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.1
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1bd711b4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.745341614907
-		- Score on test : 0.661417322835
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.745341614907
-		- Score on test : 0.661417322835
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.390476190476
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.172644893682
-		- Score on test : 0.118036588599
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.506024096386
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.983606557377
-		- Score on test : 0.954545454545
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.537257824143
-		- Score on test : 0.53162055336
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.390476190476
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6b6c9c97..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.606557377049
-		- Score on test : 0.584905660377
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.606557377049
-		- Score on test : 0.584905660377
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0611028315946
-		- Score on test : 0.0330758927464
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.606557377049
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.606557377049
-		- Score on test : 0.704545454545
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.530551415797
-		- Score on test : 0.515316205534
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5bc5811d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.358208955224
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.358208955224
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0385036888617
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.521739130435
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.272727272727
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.516798418972
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ccd0f1ad..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.645161290323
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.645161290323
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0628694613462
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.909090909091
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.51976284585
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9deb12f3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.436781609195
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.436781609195
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0899876638096
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.441860465116
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.431818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455039525692
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 183d0919..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.441860465116
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.441860465116
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.068316965625
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.452380952381
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.431818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.465909090909
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8cf553cc..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.705882352941
-		- Score on test : 0.619469026549
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.705882352941
-		- Score on test : 0.619469026549
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.380952380952
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.189221481343
-		- Score on test : 0.0665679839847
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.64
-		- Score on test : 0.507246376812
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.786885245902
-		- Score on test : 0.795454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.586624441133
-		- Score on test : 0.528162055336
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.380952380952
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f74150b1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.4
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.904564315353
-		- Score on test : 0.357142857143
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.904564315353
-		- Score on test : 0.357142857143
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.109523809524
-		- Score on test : 0.6
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.890476190476
-		- Score on test : 0.4
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.77645053118
-		- Score on test : -0.20378096045
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.915966386555
-		- Score on test : 0.375
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.893442622951
-		- Score on test : 0.340909090909
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.889903129657
-		- Score on test : 0.39871541502
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.109523809524
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 608729cd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.715231788079
-		- Score on test : 0.676923076923
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.715231788079
-		- Score on test : 0.676923076923
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0945615027077
-		- Score on test : 0.210925065403
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.511627906977
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.885245901639
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.533532041729
-		- Score on test : 0.54347826087
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 59671aee..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.433333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.604838709677
-		- Score on test : 0.484848484848
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.604838709677
-		- Score on test : 0.484848484848
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.566666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.433333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0354605635154
-		- Score on test : -0.131720304791
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.595238095238
-		- Score on test : 0.436363636364
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.614754098361
-		- Score on test : 0.545454545455
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.517604321908
-		- Score on test : 0.435770750988
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a085f760..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.357142857143
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.357142857143
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.6
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.20378096045
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.375
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.340909090909
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.39871541502
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cf24b718..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 7)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.538461538462
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.538461538462
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0628694613462
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.636363636364
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.470355731225
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b93b2e14..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.576923076923
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.576923076923
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0314347306731
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.681818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.514822134387
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ed86c3be..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588235294118
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588235294118
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0763602735229
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.51724137931
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.681818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.536561264822
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 81a8c71b..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.681481481481
-		- Score on test : 0.601941747573
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.681481481481
-		- Score on test : 0.601941747573
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.127350050081
-		- Score on test : 0.100829966549
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.621621621622
-		- Score on test : 0.525423728814
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.754098360656
-		- Score on test : 0.704545454545
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.558867362146
-		- Score on test : 0.547924901186
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5a7ef634..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 4, max_depth : 20
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.9
-		- Score on test : 0.577777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.910638297872
-		- Score on test : 0.586956521739
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.910638297872
-		- Score on test : 0.586956521739
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.1
-		- Score on test : 0.422222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.9
-		- Score on test : 0.577777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.800522373751
-		- Score on test : 0.157426051223
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.946902654867
-		- Score on test : 0.5625
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.877049180328
-		- Score on test : 0.613636363636
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.904433681073
-		- Score on test : 0.578557312253
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.1
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 616ff06f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : elasticnet
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.488888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.739393939394
-		- Score on test : 0.65671641791
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.739393939394
-		- Score on test : 0.65671641791
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.511111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.590476190476
-		- Score on test : 0.488888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.115457436228
-		- Score on test : 0.0
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.586538461538
-		- Score on test : 0.488888888889
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.511363636364
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.409523809524
-		- Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 226afbe8..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 6)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 6107
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.595419847328
-		- Score on test : 0.550458715596
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.595419847328
-		- Score on test : 0.550458715596
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.495238095238
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.0682438863041
-		- Score on test : -0.0882242643891
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.557142857143
-		- Score on test : 0.461538461538
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.639344262295
-		- Score on test : 0.681818181818
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.467399403875
-		- Score on test : 0.4604743083
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.504761904762
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095538Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095538Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index d2af511d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095538Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 57.1764705882
-	-On Test : 52.6829268293
-	-On Validation : 52.1348314607
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.322967175445, 0.0184701333132, 0.322597810111, 0.335964881131
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160908-095622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index e8dd4634..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2394 +0,0 @@
-2016-09-08 09:56:22,282 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-08 09:56:22,282 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.0001661875 Gbytes /!\ 
-2016-09-08 09:56:27,294 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-08 09:56:27,298 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-08 09:56:27,351 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:27,351 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:56:27,351 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:27,352 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:27,352 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:56:27,352 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:27,352 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:27,352 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:27,352 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:27,353 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:27,353 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:27,353 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:27,353 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:27,354 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:27,389 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:27,389 DEBUG: Start:	 Training
-2016-09-08 09:56:27,391 DEBUG: Info:	 Time for Training: 0.0395510196686[s]
-2016-09-08 09:56:27,391 DEBUG: Done:	 Training
-2016-09-08 09:56:27,391 DEBUG: Start:	 Predicting
-2016-09-08 09:56:27,393 DEBUG: Done:	 Predicting
-2016-09-08 09:56:27,393 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:27,403 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:27,403 DEBUG: Start:	 Training
-2016-09-08 09:56:27,407 DEBUG: Info:	 Time for Training: 0.0563409328461[s]
-2016-09-08 09:56:27,407 DEBUG: Done:	 Training
-2016-09-08 09:56:27,407 DEBUG: Start:	 Predicting
-2016-09-08 09:56:27,410 DEBUG: Done:	 Predicting
-2016-09-08 09:56:27,410 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:27,440 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:27,440 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.377777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.348837209302
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.348837209302
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.622222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.377777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.245415911539
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.365853658537
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.376804380289
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:56:27,441 INFO: Done:	 Result Analysis
-2016-09-08 09:56:27,452 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:27,452 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.377777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.622222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.377777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.249628898234
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.325581395349
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.341463414634
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.37481333997
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:56:27,452 INFO: Done:	 Result Analysis
-2016-09-08 09:56:27,595 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:27,595 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:27,595 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:56:27,595 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:56:27,595 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:27,595 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:27,596 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:27,596 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:27,596 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:27,596 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:27,596 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:27,596 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:27,596 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:27,596 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:27,627 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:27,627 DEBUG: Start:	 Training
-2016-09-08 09:56:27,628 DEBUG: Info:	 Time for Training: 0.0337619781494[s]
-2016-09-08 09:56:27,628 DEBUG: Done:	 Training
-2016-09-08 09:56:27,628 DEBUG: Start:	 Predicting
-2016-09-08 09:56:27,635 DEBUG: Done:	 Predicting
-2016-09-08 09:56:27,635 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:27,676 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:27,676 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.422222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.467532467532
-		- Score on test : 0.212121212121
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.467532467532
-		- Score on test : 0.212121212121
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.390476190476
-		- Score on test : 0.577777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.422222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.237135686833
-		- Score on test : -0.218615245335
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.692307692308
-		- Score on test : 0.28
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.352941176471
-		- Score on test : 0.170731707317
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.602396514161
-		- Score on test : 0.401692384271
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.390476190476
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:56:27,676 INFO: Done:	 Result Analysis
-2016-09-08 09:56:27,921 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:27,921 DEBUG: Start:	 Training
-2016-09-08 09:56:27,970 DEBUG: Info:	 Time for Training: 0.375941991806[s]
-2016-09-08 09:56:27,970 DEBUG: Done:	 Training
-2016-09-08 09:56:27,970 DEBUG: Start:	 Predicting
-2016-09-08 09:56:27,976 DEBUG: Done:	 Predicting
-2016-09-08 09:56:27,977 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:28,010 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:28,011 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 19, max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.423529411765
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.423529411765
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0912478416452
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.409090909091
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.439024390244
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.454206072673
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,011 INFO: Done:	 Result Analysis
-2016-09-08 09:56:28,143 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,143 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:56:28,143 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:28,143 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,143 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:56:28,144 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:28,144 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:28,144 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:28,144 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:28,144 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:28,144 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,144 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:28,144 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:28,145 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,189 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:28,189 DEBUG: Start:	 Training
-2016-09-08 09:56:28,190 DEBUG: Info:	 Time for Training: 0.0481970310211[s]
-2016-09-08 09:56:28,190 DEBUG: Done:	 Training
-2016-09-08 09:56:28,190 DEBUG: Start:	 Predicting
-2016-09-08 09:56:28,195 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:28,195 DEBUG: Start:	 Training
-2016-09-08 09:56:28,214 DEBUG: Info:	 Time for Training: 0.0711450576782[s]
-2016-09-08 09:56:28,214 DEBUG: Done:	 Training
-2016-09-08 09:56:28,214 DEBUG: Start:	 Predicting
-2016-09-08 09:56:28,216 DEBUG: Done:	 Predicting
-2016-09-08 09:56:28,216 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:28,217 DEBUG: Done:	 Predicting
-2016-09-08 09:56:28,217 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:28,239 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:28,239 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.0
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,239 INFO: Done:	 Result Analysis
-2016-09-08 09:56:28,250 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:28,250 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.477777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.532710280374
-		- Score on test : 0.459770114943
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.532710280374
-		- Score on test : 0.459770114943
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.522222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.477777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0496545019224
-		- Score on test : -0.0426484477255
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.508928571429
-		- Score on test : 0.434782608696
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.558823529412
-		- Score on test : 0.487804878049
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.524782135076
-		- Score on test : 0.478596316575
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,250 INFO: Done:	 Result Analysis
-2016-09-08 09:56:28,387 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,387 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,387 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:56:28,387 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:56:28,387 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:28,387 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:28,388 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:28,388 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:28,388 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:28,388 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:28,388 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:28,388 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:28,388 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,388 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,433 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:28,433 DEBUG: Start:	 Training
-2016-09-08 09:56:28,438 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:28,438 DEBUG: Start:	 Training
-2016-09-08 09:56:28,450 DEBUG: Info:	 Time for Training: 0.0634729862213[s]
-2016-09-08 09:56:28,450 DEBUG: Done:	 Training
-2016-09-08 09:56:28,450 DEBUG: Start:	 Predicting
-2016-09-08 09:56:28,455 DEBUG: Info:	 Time for Training: 0.0687439441681[s]
-2016-09-08 09:56:28,455 DEBUG: Done:	 Training
-2016-09-08 09:56:28,455 DEBUG: Start:	 Predicting
-2016-09-08 09:56:28,455 DEBUG: Done:	 Predicting
-2016-09-08 09:56:28,455 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:28,459 DEBUG: Done:	 Predicting
-2016-09-08 09:56:28,459 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:28,491 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:28,491 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.409090909091
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.409090909091
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.152357995542
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.382978723404
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.439024390244
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.423593827775
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,492 INFO: Done:	 Result Analysis
-2016-09-08 09:56:28,505 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:28,505 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.475
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.475
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0555284586866
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.487179487179
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.463414634146
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.52762568442
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,505 INFO: Done:	 Result Analysis
-2016-09-08 09:56:28,637 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,638 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:56:28,638 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:28,638 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,638 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:56:28,638 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:28,638 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:28,638 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:28,638 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:28,639 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:28,639 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:28,639 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:28,639 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,639 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,677 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:28,677 DEBUG: Start:	 Training
-2016-09-08 09:56:28,680 DEBUG: Info:	 Time for Training: 0.0427668094635[s]
-2016-09-08 09:56:28,680 DEBUG: Done:	 Training
-2016-09-08 09:56:28,680 DEBUG: Start:	 Predicting
-2016-09-08 09:56:28,683 DEBUG: Done:	 Predicting
-2016-09-08 09:56:28,683 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:28,691 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:28,691 DEBUG: Start:	 Training
-2016-09-08 09:56:28,696 DEBUG: Info:	 Time for Training: 0.0597720146179[s]
-2016-09-08 09:56:28,696 DEBUG: Done:	 Training
-2016-09-08 09:56:28,696 DEBUG: Start:	 Predicting
-2016-09-08 09:56:28,699 DEBUG: Done:	 Predicting
-2016-09-08 09:56:28,699 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:28,734 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:28,734 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.957142857143
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.957746478873
-		- Score on test : 0.444444444444
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.957746478873
-		- Score on test : 0.444444444444
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0428571428571
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.957142857143
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.917792101918
-		- Score on test : -0.104031856645
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.918918918919
-		- Score on test : 0.408163265306
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.487804878049
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.958333333333
-		- Score on test : 0.447984071677
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0428571428571
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,734 INFO: Done:	 Result Analysis
-2016-09-08 09:56:28,741 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:28,741 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.390243902439
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.390243902439
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.119960179194
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.390243902439
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.390243902439
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.440019910403
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,741 INFO: Done:	 Result Analysis
-2016-09-08 09:56:28,882 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,883 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:56:28,883 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:28,883 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,883 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:56:28,884 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:28,884 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:28,884 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:28,884 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:28,884 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:28,885 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:28,885 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:28,885 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,885 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,919 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:28,919 DEBUG: Start:	 Training
-2016-09-08 09:56:28,920 DEBUG: Info:	 Time for Training: 0.0378148555756[s]
-2016-09-08 09:56:28,920 DEBUG: Done:	 Training
-2016-09-08 09:56:28,920 DEBUG: Start:	 Predicting
-2016-09-08 09:56:28,927 DEBUG: Done:	 Predicting
-2016-09-08 09:56:28,927 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:28,967 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:28,967 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.559139784946
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.559139784946
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.438095238095
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.133359904768
-		- Score on test : 0.104395047556
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.5390625
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.676470588235
-		- Score on test : 0.634146341463
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.565087145969
-		- Score on test : 0.551767048283
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.438095238095
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,967 INFO: Done:	 Result Analysis
-2016-09-08 09:56:29,211 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:29,212 DEBUG: Start:	 Training
-2016-09-08 09:56:29,260 DEBUG: Info:	 Time for Training: 0.377947092056[s]
-2016-09-08 09:56:29,261 DEBUG: Done:	 Training
-2016-09-08 09:56:29,261 DEBUG: Start:	 Predicting
-2016-09-08 09:56:29,267 DEBUG: Done:	 Predicting
-2016-09-08 09:56:29,267 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:29,300 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:29,300 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 19, max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.995073891626
-		- Score on test : 0.516853932584
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.995073891626
-		- Score on test : 0.516853932584
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.990510833227
-		- Score on test : 0.0506833064614
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.479166666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.990196078431
-		- Score on test : 0.560975609756
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.995098039216
-		- Score on test : 0.525385764062
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:56:29,300 INFO: Done:	 Result Analysis
-2016-09-08 09:56:29,434 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,435 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:56:29,435 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:29,435 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,435 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:56:29,435 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:29,436 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:29,436 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:29,436 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:29,436 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:29,436 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,436 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:29,437 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:29,437 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,483 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:29,483 DEBUG: Start:	 Training
-2016-09-08 09:56:29,484 DEBUG: Info:	 Time for Training: 0.0506761074066[s]
-2016-09-08 09:56:29,484 DEBUG: Done:	 Training
-2016-09-08 09:56:29,484 DEBUG: Start:	 Predicting
-2016-09-08 09:56:29,491 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:29,491 DEBUG: Start:	 Training
-2016-09-08 09:56:29,511 DEBUG: Done:	 Predicting
-2016-09-08 09:56:29,511 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:29,520 DEBUG: Info:	 Time for Training: 0.0860919952393[s]
-2016-09-08 09:56:29,520 DEBUG: Done:	 Training
-2016-09-08 09:56:29,521 DEBUG: Start:	 Predicting
-2016-09-08 09:56:29,526 DEBUG: Done:	 Predicting
-2016-09-08 09:56:29,526 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:29,539 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:29,539 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.0
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:29,540 INFO: Done:	 Result Analysis
-2016-09-08 09:56:29,553 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:29,554 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.577777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.547619047619
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.547619047619
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.422222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.577777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0294117647059
-		- Score on test : 0.152357995542
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.53488372093
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.529411764706
-		- Score on test : 0.560975609756
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.514705882353
-		- Score on test : 0.576406172225
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:56:29,554 INFO: Done:	 Result Analysis
-2016-09-08 09:56:29,683 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,684 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,684 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:56:29,684 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:56:29,684 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:29,684 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:29,685 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:29,685 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:29,685 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:29,685 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:29,686 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:29,686 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:29,686 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,686 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,756 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:29,756 DEBUG: Start:	 Training
-2016-09-08 09:56:29,762 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:29,762 DEBUG: Start:	 Training
-2016-09-08 09:56:29,781 DEBUG: Info:	 Time for Training: 0.0979940891266[s]
-2016-09-08 09:56:29,781 DEBUG: Done:	 Training
-2016-09-08 09:56:29,781 DEBUG: Start:	 Predicting
-2016-09-08 09:56:29,788 DEBUG: Info:	 Time for Training: 0.105060100555[s]
-2016-09-08 09:56:29,788 DEBUG: Done:	 Training
-2016-09-08 09:56:29,788 DEBUG: Start:	 Predicting
-2016-09-08 09:56:29,790 DEBUG: Done:	 Predicting
-2016-09-08 09:56:29,790 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:29,793 DEBUG: Done:	 Predicting
-2016-09-08 09:56:29,794 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:29,827 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:29,827 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:29,827 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.394366197183
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.394366197183
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0157759322964
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.341463414634
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.507466401195
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:56:29,827 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0193709711057
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.44
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.268292682927
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.491289198606
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:56:29,827 INFO: Done:	 Result Analysis
-2016-09-08 09:56:29,828 INFO: Done:	 Result Analysis
-2016-09-08 09:56:29,934 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,934 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:56:29,934 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:29,934 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,935 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:56:29,935 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:29,935 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:29,935 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:29,936 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:29,936 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:29,936 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:29,936 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,936 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:29,936 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,974 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:29,974 DEBUG: Start:	 Training
-2016-09-08 09:56:29,976 DEBUG: Info:	 Time for Training: 0.0422959327698[s]
-2016-09-08 09:56:29,976 DEBUG: Done:	 Training
-2016-09-08 09:56:29,976 DEBUG: Start:	 Predicting
-2016-09-08 09:56:29,979 DEBUG: Done:	 Predicting
-2016-09-08 09:56:29,979 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:29,988 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:29,988 DEBUG: Start:	 Training
-2016-09-08 09:56:29,994 DEBUG: Info:	 Time for Training: 0.0606279373169[s]
-2016-09-08 09:56:29,994 DEBUG: Done:	 Training
-2016-09-08 09:56:29,994 DEBUG: Start:	 Predicting
-2016-09-08 09:56:29,997 DEBUG: Done:	 Predicting
-2016-09-08 09:56:29,997 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:30,029 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:30,029 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.385542168675
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.385542168675
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.140124435511
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.380952380952
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.390243902439
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.429815828771
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,029 INFO: Done:	 Result Analysis
-2016-09-08 09:56:30,043 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:30,043 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.375
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.375
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.124563839757
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.384615384615
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.365853658537
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.438028870085
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,044 INFO: Done:	 Result Analysis
-2016-09-08 09:56:30,185 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,186 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:56:30,186 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,186 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:30,186 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:56:30,186 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:30,187 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,187 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,187 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,187 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,187 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:30,187 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:30,187 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:30,188 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:30,236 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:30,237 DEBUG: Start:	 Training
-2016-09-08 09:56:30,238 DEBUG: Info:	 Time for Training: 0.0538048744202[s]
-2016-09-08 09:56:30,238 DEBUG: Done:	 Training
-2016-09-08 09:56:30,238 DEBUG: Start:	 Predicting
-2016-09-08 09:56:30,249 DEBUG: Done:	 Predicting
-2016-09-08 09:56:30,249 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:30,288 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:30,288 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.588888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.323943661972
-		- Score on test : 0.327272727273
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.323943661972
-		- Score on test : 0.327272727273
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.411111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.588888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0866552427925
-		- Score on test : 0.161417724438
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.575
-		- Score on test : 0.642857142857
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.225490196078
-		- Score on test : 0.219512195122
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.534041394336
-		- Score on test : 0.558735689398
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,289 INFO: Done:	 Result Analysis
-2016-09-08 09:56:30,545 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:30,545 DEBUG: Start:	 Training
-2016-09-08 09:56:30,594 DEBUG: Info:	 Time for Training: 0.40927195549[s]
-2016-09-08 09:56:30,594 DEBUG: Done:	 Training
-2016-09-08 09:56:30,594 DEBUG: Start:	 Predicting
-2016-09-08 09:56:30,600 DEBUG: Done:	 Predicting
-2016-09-08 09:56:30,600 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:30,633 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:30,633 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 19, max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.55421686747
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.55421686747
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.411111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.172919516163
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.547619047619
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.560975609756
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.586610253858
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,633 INFO: Done:	 Result Analysis
-2016-09-08 09:56:30,727 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,727 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:56:30,727 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:30,727 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,728 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:56:30,728 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:30,728 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,728 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,728 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:30,728 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:30,728 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,728 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,729 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:30,729 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:30,773 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:30,773 DEBUG: Start:	 Training
-2016-09-08 09:56:30,774 DEBUG: Info:	 Time for Training: 0.0476858615875[s]
-2016-09-08 09:56:30,774 DEBUG: Done:	 Training
-2016-09-08 09:56:30,774 DEBUG: Start:	 Predicting
-2016-09-08 09:56:30,779 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:30,779 DEBUG: Start:	 Training
-2016-09-08 09:56:30,796 DEBUG: Done:	 Predicting
-2016-09-08 09:56:30,796 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:30,800 DEBUG: Info:	 Time for Training: 0.0737199783325[s]
-2016-09-08 09:56:30,800 DEBUG: Done:	 Training
-2016-09-08 09:56:30,801 DEBUG: Start:	 Predicting
-2016-09-08 09:56:30,804 DEBUG: Done:	 Predicting
-2016-09-08 09:56:30,804 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:30,819 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:30,819 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.0
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,819 INFO: Done:	 Result Analysis
-2016-09-08 09:56:30,833 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:30,833 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.588888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.514851485149
-		- Score on test : 0.543209876543
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.514851485149
-		- Score on test : 0.543209876543
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.411111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.588888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0654069940168
-		- Score on test : 0.169618786115
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.52
-		- Score on test : 0.55
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.509803921569
-		- Score on test : 0.536585365854
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.532679738562
-		- Score on test : 0.584619213539
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,833 INFO: Done:	 Result Analysis
-2016-09-08 09:56:30,973 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,973 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,974 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:56:30,974 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:56:30,974 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:30,974 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:30,974 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,974 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,974 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,974 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,975 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:30,975 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:30,975 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:30,975 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:31,020 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:31,020 DEBUG: Start:	 Training
-2016-09-08 09:56:31,024 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:31,024 DEBUG: Start:	 Training
-2016-09-08 09:56:31,039 DEBUG: Info:	 Time for Training: 0.0657398700714[s]
-2016-09-08 09:56:31,039 DEBUG: Done:	 Training
-2016-09-08 09:56:31,039 DEBUG: Start:	 Predicting
-2016-09-08 09:56:31,043 DEBUG: Info:	 Time for Training: 0.0697932243347[s]
-2016-09-08 09:56:31,043 DEBUG: Done:	 Training
-2016-09-08 09:56:31,043 DEBUG: Start:	 Predicting
-2016-09-08 09:56:31,045 DEBUG: Done:	 Predicting
-2016-09-08 09:56:31,045 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:31,047 DEBUG: Done:	 Predicting
-2016-09-08 09:56:31,047 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:31,077 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:31,077 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588235294118
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588235294118
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0350036509618
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.448717948718
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.853658536585
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488053758089
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,077 INFO: Done:	 Result Analysis
-2016-09-08 09:56:31,077 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:31,077 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.453333333333
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.453333333333
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0695369227879
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.414634146341
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533847685416
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,078 INFO: Done:	 Result Analysis
-2016-09-08 09:56:31,220 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:31,220 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:31,220 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:56:31,220 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:56:31,221 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:31,221 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:31,221 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:31,221 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:31,221 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:31,221 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:31,221 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:31,222 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:31,222 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:31,222 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:31,257 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:31,257 DEBUG: Start:	 Training
-2016-09-08 09:56:31,259 DEBUG: Info:	 Time for Training: 0.0391139984131[s]
-2016-09-08 09:56:31,259 DEBUG: Done:	 Training
-2016-09-08 09:56:31,259 DEBUG: Start:	 Predicting
-2016-09-08 09:56:31,262 DEBUG: Done:	 Predicting
-2016-09-08 09:56:31,262 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:31,270 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:31,270 DEBUG: Start:	 Training
-2016-09-08 09:56:31,274 DEBUG: Info:	 Time for Training: 0.0547120571136[s]
-2016-09-08 09:56:31,275 DEBUG: Done:	 Training
-2016-09-08 09:56:31,275 DEBUG: Start:	 Predicting
-2016-09-08 09:56:31,277 DEBUG: Done:	 Predicting
-2016-09-08 09:56:31,277 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:31,302 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:31,302 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.985507246377
-		- Score on test : 0.516853932584
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.985507246377
-		- Score on test : 0.516853932584
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0142857142857
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.971825315808
-		- Score on test : 0.0506833064614
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.479166666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.560975609756
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.986111111111
-		- Score on test : 0.525385764062
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0142857142857
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,303 INFO: Done:	 Result Analysis
-2016-09-08 09:56:31,313 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:31,314 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511627906977
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511627906977
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.066931612238
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.536585365854
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533598805376
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,314 INFO: Done:	 Result Analysis
-2016-09-08 09:56:31,468 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:31,468 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:56:31,468 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:31,468 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:31,469 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:56:31,469 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:31,469 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:31,469 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:31,469 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:31,469 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:31,469 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:31,469 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:31,470 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:31,470 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:31,503 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:31,503 DEBUG: Start:	 Training
-2016-09-08 09:56:31,504 DEBUG: Info:	 Time for Training: 0.0364670753479[s]
-2016-09-08 09:56:31,504 DEBUG: Done:	 Training
-2016-09-08 09:56:31,504 DEBUG: Start:	 Predicting
-2016-09-08 09:56:31,511 DEBUG: Done:	 Predicting
-2016-09-08 09:56:31,511 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:31,565 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:31,565 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.588888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.505050505051
-		- Score on test : 0.543209876543
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.505050505051
-		- Score on test : 0.543209876543
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.411111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.588888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0644812208979
-		- Score on test : 0.169618786115
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.520833333333
-		- Score on test : 0.55
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.490196078431
-		- Score on test : 0.536585365854
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.532135076253
-		- Score on test : 0.584619213539
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,565 INFO: Done:	 Result Analysis
-2016-09-08 09:56:31,800 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:31,800 DEBUG: Start:	 Training
-2016-09-08 09:56:31,848 DEBUG: Info:	 Time for Training: 0.380412101746[s]
-2016-09-08 09:56:31,848 DEBUG: Done:	 Training
-2016-09-08 09:56:31,848 DEBUG: Start:	 Predicting
-2016-09-08 09:56:31,855 DEBUG: Done:	 Predicting
-2016-09-08 09:56:31,855 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:31,882 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:31,882 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 19, max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.99512195122
-		- Score on test : 0.488372093023
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.99512195122
-		- Score on test : 0.488372093023
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.990515975943
-		- Score on test : 0.0223105374127
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.990291262136
-		- Score on test : 0.466666666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.99537037037
-		- Score on test : 0.511199601792
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,882 INFO: Done:	 Result Analysis
-2016-09-08 09:56:32,016 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:32,016 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:32,016 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:56:32,016 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:56:32,016 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:32,016 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:56:32,017 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:32,017 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:32,017 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:32,017 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:32,017 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:32,017 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:56:32,017 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:32,017 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:56:32,062 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:32,062 DEBUG: Start:	 Training
-2016-09-08 09:56:32,062 DEBUG: Info:	 Time for Training: 0.04727602005[s]
-2016-09-08 09:56:32,063 DEBUG: Done:	 Training
-2016-09-08 09:56:32,063 DEBUG: Start:	 Predicting
-2016-09-08 09:56:32,066 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:56:32,067 DEBUG: Start:	 Training
-2016-09-08 09:56:32,075 DEBUG: Done:	 Predicting
-2016-09-08 09:56:32,076 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:32,089 DEBUG: Info:	 Time for Training: 0.0742139816284[s]
-2016-09-08 09:56:32,090 DEBUG: Done:	 Training
-2016-09-08 09:56:32,090 DEBUG: Start:	 Predicting
-2016-09-08 09:56:32,093 DEBUG: Done:	 Predicting
-2016-09-08 09:56:32,093 DEBUG: Start:	 Getting Results
-2016-09-08 09:56:32,100 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:32,100 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.0
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:32,100 INFO: Done:	 Result Analysis
-2016-09-08 09:56:32,122 DEBUG: Done:	 Getting Results
-2016-09-08 09:56:32,122 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.566666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.520408163265
-		- Score on test : 0.506329113924
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.520408163265
-		- Score on test : 0.506329113924
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.447619047619
-		- Score on test : 0.433333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.566666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.10237359911
-		- Score on test : 0.121459622637
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.542553191489
-		- Score on test : 0.526315789474
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.487804878049
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.550925925926
-		- Score on test : 0.560228969637
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.447619047619
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-08 09:56:32,122 INFO: Done:	 Result Analysis
-2016-09-08 09:56:32,424 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:56:32,425 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-08 09:56:32,426 INFO: Info:	 Shape of View0 :(300, 12)
-2016-09-08 09:56:32,426 INFO: Info:	 Shape of View1 :(300, 19)
-2016-09-08 09:56:32,427 INFO: Info:	 Shape of View2 :(300, 20)
-2016-09-08 09:56:32,427 INFO: Info:	 Shape of View3 :(300, 12)
-2016-09-08 09:56:32,428 INFO: Done:	 Read Database Files
-2016-09-08 09:56:32,428 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-08 09:56:32,431 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:56:32,432 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-08 09:56:32,432 INFO: Info:	 Shape of View0 :(300, 12)
-2016-09-08 09:56:32,433 INFO: Done:	 Determine validation split
-2016-09-08 09:56:32,433 INFO: Start:	 Determine 5 folds
-2016-09-08 09:56:32,433 INFO: Info:	 Shape of View1 :(300, 19)
-2016-09-08 09:56:32,434 INFO: Info:	 Shape of View2 :(300, 20)
-2016-09-08 09:56:32,434 INFO: Info:	 Shape of View3 :(300, 12)
-2016-09-08 09:56:32,434 INFO: Done:	 Read Database Files
-2016-09-08 09:56:32,434 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-08 09:56:32,439 INFO: Done:	 Determine validation split
-2016-09-08 09:56:32,439 INFO: Start:	 Determine 5 folds
-2016-09-08 09:56:32,445 INFO: Info:	 Length of Learning Sets: 169
-2016-09-08 09:56:32,445 INFO: Info:	 Length of Testing Sets: 42
-2016-09-08 09:56:32,445 INFO: Info:	 Length of Validation Set: 89
-2016-09-08 09:56:32,445 INFO: Done:	 Determine folds
-2016-09-08 09:56:32,445 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-08 09:56:32,445 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:32,445 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:32,448 INFO: Info:	 Length of Learning Sets: 169
-2016-09-08 09:56:32,448 INFO: Info:	 Length of Testing Sets: 42
-2016-09-08 09:56:32,449 INFO: Info:	 Length of Validation Set: 89
-2016-09-08 09:56:32,449 INFO: Done:	 Determine folds
-2016-09-08 09:56:32,449 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-08 09:56:32,449 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:32,449 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:32,500 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:32,501 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:32,504 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:32,504 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:32,551 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:32,551 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:32,556 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:32,556 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:32,600 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:32,600 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:32,607 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:32,607 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:32,651 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:32,660 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:32,717 INFO: Done:	 Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:32,717 INFO: Start:	 Classification
-2016-09-08 09:56:32,717 INFO: 	Start:	 Fold number 1
-2016-09-08 09:56:32,745 INFO: 	Start: 	 Classification
-2016-09-08 09:56:32,751 INFO: Done:	 Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:32,751 INFO: Start:	 Classification
-2016-09-08 09:56:32,751 INFO: 	Start:	 Fold number 1
-2016-09-08 09:56:32,771 INFO: 	Done: 	 Fold number 1
-2016-09-08 09:56:32,772 INFO: 	Start:	 Fold number 2
-2016-09-08 09:56:32,778 INFO: 	Start: 	 Classification
-2016-09-08 09:56:32,798 INFO: 	Start: 	 Classification
-2016-09-08 09:56:32,824 INFO: 	Done: 	 Fold number 2
-2016-09-08 09:56:32,824 INFO: 	Start:	 Fold number 3
-2016-09-08 09:56:32,847 INFO: 	Done: 	 Fold number 1
-2016-09-08 09:56:32,848 INFO: 	Start:	 Fold number 2
-2016-09-08 09:56:32,850 INFO: 	Start: 	 Classification
-2016-09-08 09:56:32,874 INFO: 	Start: 	 Classification
-2016-09-08 09:56:32,876 INFO: 	Done: 	 Fold number 3
-2016-09-08 09:56:32,877 INFO: 	Start:	 Fold number 4
-2016-09-08 09:56:32,903 INFO: 	Start: 	 Classification
-2016-09-08 09:56:32,928 INFO: 	Done: 	 Fold number 4
-2016-09-08 09:56:32,928 INFO: 	Start:	 Fold number 5
-2016-09-08 09:56:32,944 INFO: 	Done: 	 Fold number 2
-2016-09-08 09:56:32,944 INFO: 	Start:	 Fold number 3
-2016-09-08 09:56:32,955 INFO: 	Start: 	 Classification
-2016-09-08 09:56:32,971 INFO: 	Start: 	 Classification
-2016-09-08 09:56:32,981 INFO: 	Done: 	 Fold number 5
-2016-09-08 09:56:32,981 INFO: Done:	 Classification
-2016-09-08 09:56:32,981 INFO: Info:	 Time for Classification: 0[s]
-2016-09-08 09:56:32,981 INFO: Start:	 Result Analysis for Fusion
-2016-09-08 09:56:33,041 INFO: 	Done: 	 Fold number 3
-2016-09-08 09:56:33,041 INFO: 	Start:	 Fold number 4
-2016-09-08 09:56:33,068 INFO: 	Start: 	 Classification
-2016-09-08 09:56:33,109 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 64.9704142012
-	-On Test : 42.380952381
-	-On Validation : 48.7640449438
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.241839908393, 0.362121620258, 0.0533308084229, 0.342707662926
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : log, penalty : l2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-		- SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-08 09:56:33,109 INFO: Done:	 Result Analysis
-2016-09-08 09:56:33,137 INFO: 	Done: 	 Fold number 4
-2016-09-08 09:56:33,137 INFO: 	Start:	 Fold number 5
-2016-09-08 09:56:33,163 INFO: 	Start: 	 Classification
-2016-09-08 09:56:33,229 INFO: 	Done: 	 Fold number 5
-2016-09-08 09:56:33,229 INFO: Done:	 Classification
-2016-09-08 09:56:33,229 INFO: Info:	 Time for Classification: 0[s]
-2016-09-08 09:56:33,229 INFO: Start:	 Result Analysis for Fusion
-2016-09-08 09:56:33,351 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 62.2485207101
-	-On Test : 47.619047619
-	-On Validation : 52.1348314607
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : log, penalty : l2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-		- SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-08 09:56:33,351 INFO: Done:	 Result Analysis
-2016-09-08 09:56:33,471 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:56:33,471 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-08 09:56:33,472 INFO: Info:	 Shape of View0 :(300, 12)
-2016-09-08 09:56:33,472 INFO: Info:	 Shape of View1 :(300, 19)
-2016-09-08 09:56:33,473 INFO: Info:	 Shape of View2 :(300, 20)
-2016-09-08 09:56:33,473 INFO: Info:	 Shape of View3 :(300, 12)
-2016-09-08 09:56:33,473 INFO: Done:	 Read Database Files
-2016-09-08 09:56:33,474 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-08 09:56:33,478 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:56:33,478 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-08 09:56:33,479 INFO: Info:	 Shape of View0 :(300, 12)
-2016-09-08 09:56:33,480 INFO: Info:	 Shape of View1 :(300, 19)
-2016-09-08 09:56:33,481 INFO: Done:	 Determine validation split
-2016-09-08 09:56:33,481 INFO: Start:	 Determine 5 folds
-2016-09-08 09:56:33,481 INFO: Info:	 Shape of View2 :(300, 20)
-2016-09-08 09:56:33,482 INFO: Info:	 Shape of View3 :(300, 12)
-2016-09-08 09:56:33,482 INFO: Done:	 Read Database Files
-2016-09-08 09:56:33,482 INFO: Start:	 Determine validation split for ratio 0.7
-2016-09-08 09:56:33,485 INFO: Done:	 Determine validation split
-2016-09-08 09:56:33,485 INFO: Start:	 Determine 5 folds
-2016-09-08 09:56:33,488 INFO: Info:	 Length of Learning Sets: 169
-2016-09-08 09:56:33,489 INFO: Info:	 Length of Testing Sets: 42
-2016-09-08 09:56:33,489 INFO: Info:	 Length of Validation Set: 89
-2016-09-08 09:56:33,489 INFO: Done:	 Determine folds
-2016-09-08 09:56:33,489 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-08 09:56:33,489 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:33,489 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:33,495 INFO: Info:	 Length of Learning Sets: 169
-2016-09-08 09:56:33,495 INFO: Info:	 Length of Testing Sets: 42
-2016-09-08 09:56:33,495 INFO: Info:	 Length of Validation Set: 89
-2016-09-08 09:56:33,495 INFO: Done:	 Determine folds
-2016-09-08 09:56:33,496 INFO: Start:	 Learning with Fusion and 5 folds
-2016-09-08 09:56:33,496 INFO: Start:	 Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:33,496 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:33,544 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:33,545 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:33,550 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:33,550 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:33,596 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:33,596 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:33,600 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:33,601 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:33,646 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:33,646 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:33,650 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:33,650 DEBUG: 	Start:	 Random search for SGD with 1 iterations
-2016-09-08 09:56:33,698 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:33,698 INFO: Done:	 Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:33,698 INFO: Start:	 Classification
-2016-09-08 09:56:33,699 INFO: 	Start:	 Fold number 1
-2016-09-08 09:56:33,702 DEBUG: 	Done:	 Random search for SGD
-2016-09-08 09:56:33,760 INFO: 	Start: 	 Classification
-2016-09-08 09:56:33,767 INFO: Done:	 Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:33,767 INFO: Start:	 Classification
-2016-09-08 09:56:33,767 INFO: 	Start:	 Fold number 1
-2016-09-08 09:56:33,790 INFO: 	Done: 	 Fold number 1
-2016-09-08 09:56:33,790 INFO: 	Start:	 Fold number 2
-2016-09-08 09:56:33,794 INFO: 	Start: 	 Classification
-2016-09-08 09:56:33,825 INFO: 	Done: 	 Fold number 1
-2016-09-08 09:56:33,825 INFO: 	Start:	 Fold number 2
-2016-09-08 09:56:33,835 INFO: 	Start: 	 Classification
-2016-09-08 09:56:33,851 INFO: 	Start: 	 Classification
-2016-09-08 09:56:33,864 INFO: 	Done: 	 Fold number 2
-2016-09-08 09:56:33,865 INFO: 	Start:	 Fold number 3
-2016-09-08 09:56:33,882 INFO: 	Done: 	 Fold number 2
-2016-09-08 09:56:33,882 INFO: 	Start:	 Fold number 3
-2016-09-08 09:56:33,908 INFO: 	Start: 	 Classification
-2016-09-08 09:56:33,909 INFO: 	Start: 	 Classification
-2016-09-08 09:56:33,938 INFO: 	Done: 	 Fold number 3
-2016-09-08 09:56:33,938 INFO: 	Start:	 Fold number 4
-2016-09-08 09:56:33,938 INFO: 	Done: 	 Fold number 3
-2016-09-08 09:56:33,939 INFO: 	Start:	 Fold number 4
-2016-09-08 09:56:33,965 INFO: 	Start: 	 Classification
-2016-09-08 09:56:33,984 INFO: 	Start: 	 Classification
-2016-09-08 09:56:33,997 INFO: 	Done: 	 Fold number 4
-2016-09-08 09:56:33,997 INFO: 	Start:	 Fold number 5
-2016-09-08 09:56:34,014 INFO: 	Done: 	 Fold number 4
-2016-09-08 09:56:34,014 INFO: 	Start:	 Fold number 5
-2016-09-08 09:56:34,024 INFO: 	Start: 	 Classification
-2016-09-08 09:56:34,054 INFO: 	Done: 	 Fold number 5
-2016-09-08 09:56:34,054 INFO: Done:	 Classification
-2016-09-08 09:56:34,054 INFO: Info:	 Time for Classification: 0[s]
-2016-09-08 09:56:34,055 INFO: Start:	 Result Analysis for Fusion
-2016-09-08 09:56:34,059 INFO: 	Start: 	 Classification
-2016-09-08 09:56:34,089 INFO: 	Done: 	 Fold number 5
-2016-09-08 09:56:34,089 INFO: Done:	 Classification
-2016-09-08 09:56:34,089 INFO: Info:	 Time for Classification: 0[s]
-2016-09-08 09:56:34,089 INFO: Start:	 Result Analysis for Fusion
-2016-09-08 09:56:34,221 INFO: 		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 63.4319526627
-	-On Test : 47.619047619
-	-On Validation : 44.2696629213
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : log, penalty : l2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-		- SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
-
-2016-09-08 09:56:34,222 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 03f454c1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.377777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.622222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.377777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.249628898234
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.325581395349
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.341463414634
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.37481333997
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 11212171..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.377777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.377777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.348837209302
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.348837209302
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.622222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.377777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.245415911539
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.365853658537
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.376804380289
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 717561a9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.422222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.467532467532
-		- Score on test : 0.212121212121
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.467532467532
-		- Score on test : 0.212121212121
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.390476190476
-		- Score on test : 0.577777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.609523809524
-		- Score on test : 0.422222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.237135686833
-		- Score on test : -0.218615245335
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.692307692308
-		- Score on test : 0.28
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.352941176471
-		- Score on test : 0.170731707317
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.602396514161
-		- Score on test : 0.401692384271
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.390476190476
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db639d87..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.390243902439
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.390243902439
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.119960179194
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.390243902439
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.390243902439
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.440019910403
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 63f60667..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.957142857143
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.957746478873
-		- Score on test : 0.444444444444
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.957746478873
-		- Score on test : 0.444444444444
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0428571428571
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.957142857143
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.917792101918
-		- Score on test : -0.104031856645
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.918918918919
-		- Score on test : 0.408163265306
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.487804878049
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.958333333333
-		- Score on test : 0.447984071677
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0428571428571
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 450f0ea1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.559139784946
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.559139784946
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.438095238095
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.561904761905
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.133359904768
-		- Score on test : 0.104395047556
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.5390625
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.676470588235
-		- Score on test : 0.634146341463
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.565087145969
-		- Score on test : 0.551767048283
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.438095238095
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3d2d8158..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 19, max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.423529411765
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.423529411765
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0912478416452
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.409090909091
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.439024390244
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.454206072673
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 511f2fee..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.0
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2bda3cf3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.477777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.532710280374
-		- Score on test : 0.459770114943
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.532710280374
-		- Score on test : 0.459770114943
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.522222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.477777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0496545019224
-		- Score on test : -0.0426484477255
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.508928571429
-		- Score on test : 0.434782608696
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.558823529412
-		- Score on test : 0.487804878049
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.524782135076
-		- Score on test : 0.478596316575
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 59cdb6c6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.475
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.475
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0555284586866
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.487179487179
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.463414634146
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.52762568442
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a96b05b6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.409090909091
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.409090909091
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.152357995542
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.382978723404
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.439024390244
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.423593827775
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 46f9acd1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 19, max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.995073891626
-		- Score on test : 0.516853932584
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.995073891626
-		- Score on test : 0.516853932584
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.990510833227
-		- Score on test : 0.0506833064614
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.479166666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.990196078431
-		- Score on test : 0.560975609756
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.995098039216
-		- Score on test : 0.525385764062
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c2daea4e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.0
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 65a1d806..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.577777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.547619047619
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.547619047619
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.422222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.577777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0294117647059
-		- Score on test : 0.152357995542
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.53488372093
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.529411764706
-		- Score on test : 0.560975609756
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.514705882353
-		- Score on test : 0.576406172225
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ffa02970..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.394366197183
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.394366197183
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0157759322964
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.466666666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.341463414634
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.507466401195
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 664dd67d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 19)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0193709711057
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.44
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.268292682927
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.491289198606
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1026306a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.375
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.375
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.124563839757
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.384615384615
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.365853658537
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.438028870085
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e47d02bf..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.385542168675
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.385542168675
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.140124435511
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.380952380952
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.390243902439
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.429815828771
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 15b07a9c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.588888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.323943661972
-		- Score on test : 0.327272727273
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.323943661972
-		- Score on test : 0.327272727273
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.411111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.542857142857
-		- Score on test : 0.588888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0866552427925
-		- Score on test : 0.161417724438
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.575
-		- Score on test : 0.642857142857
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.225490196078
-		- Score on test : 0.219512195122
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.534041394336
-		- Score on test : 0.558735689398
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.457142857143
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 70517c8c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 19, max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.55421686747
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.55421686747
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.411111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.172919516163
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.547619047619
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.560975609756
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.586610253858
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fd86ceca..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.0
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ea0b1413..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.588888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.514851485149
-		- Score on test : 0.543209876543
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.514851485149
-		- Score on test : 0.543209876543
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.411111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.588888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0654069940168
-		- Score on test : 0.169618786115
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.52
-		- Score on test : 0.55
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.509803921569
-		- Score on test : 0.536585365854
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.532679738562
-		- Score on test : 0.584619213539
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7ea3a02f..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511627906977
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511627906977
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.066931612238
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488888888889
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.536585365854
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533598805376
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a3cc2947..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.522222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.985507246377
-		- Score on test : 0.516853932584
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.985507246377
-		- Score on test : 0.516853932584
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0142857142857
-		- Score on test : 0.477777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.985714285714
-		- Score on test : 0.522222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.971825315808
-		- Score on test : 0.0506833064614
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.971428571429
-		- Score on test : 0.479166666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.560975609756
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.986111111111
-		- Score on test : 0.525385764062
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0142857142857
-		- Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 889e2b6c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 42
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.588888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.505050505051
-		- Score on test : 0.543209876543
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.505050505051
-		- Score on test : 0.543209876543
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.411111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.588888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0644812208979
-		- Score on test : 0.169618786115
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.520833333333
-		- Score on test : 0.55
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.490196078431
-		- Score on test : 0.536585365854
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.532135076253
-		- Score on test : 0.584619213539
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.466666666667
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index df0c84da..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 19, max_depth : 10
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.99512195122
-		- Score on test : 0.488372093023
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.99512195122
-		- Score on test : 0.488372093023
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.990515975943
-		- Score on test : 0.0223105374127
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.990291262136
-		- Score on test : 0.466666666667
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.512195121951
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.99537037037
-		- Score on test : 0.511199601792
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 58bf957c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588235294118
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588235294118
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0350036509618
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.448717948718
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.853658536585
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.488053758089
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b1712044..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 20)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.453333333333
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.453333333333
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0695369227879
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.414634146341
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533847685416
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ac66b07e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : log, penalty : l1
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.653846153846
-		- Score on test : 0.625954198473
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.0
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.455555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 1.0
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c4a246c9..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 8627
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.566666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.520408163265
-		- Score on test : 0.506329113924
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.520408163265
-		- Score on test : 0.506329113924
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.447619047619
-		- Score on test : 0.433333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.552380952381
-		- Score on test : 0.566666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.10237359911
-		- Score on test : 0.121459622637
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.542553191489
-		- Score on test : 0.526315789474
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.5
-		- Score on test : 0.487804878049
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.550925925926
-		- Score on test : 0.560228969637
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.447619047619
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ec16784..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 64.9704142012
-	-On Test : 42.380952381
-	-On Validation : 48.7640449438
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.241839908393, 0.362121620258, 0.0533308084229, 0.342707662926
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : log, penalty : l2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-		- SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index f9c3ded1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 62.2485207101
-	-On Test : 47.619047619
-	-On Validation : 52.1348314607
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : log, penalty : l2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-		- SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095634Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095634Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 457213b0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095634Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 63.4319526627
-	-On Test : 47.619047619
-	-On Validation : 44.2696629213
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : elasticnet
-		- SGDClassifier with loss : log, penalty : l2
-		- SGDClassifier with loss : modified_huber, penalty : l2
-		- SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095845-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160908-095845-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index d434781c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095845-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,32 +0,0 @@
-2016-09-08 09:58:45,489 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
-2016-09-08 09:58:45,489 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00010759375 Gbytes /!\ 
-2016-09-08 09:58:50,503 DEBUG: Start:	 Creating datasets for multiprocessing
-2016-09-08 09:58:50,507 INFO: Start:	 Finding all available mono- & multiview algorithms
-2016-09-08 09:58:50,558 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:58:50,558 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:58:50,559 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:58:50,559 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:58:50,559 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:58:50,559 DEBUG: Start:	 Determine Train/Test split
-2016-09-08 09:58:50,559 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:58:50,559 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:58:50,559 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:58:50,559 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:58:50,560 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:58:50,560 DEBUG: Done:	 Determine Train/Test split
-2016-09-08 09:58:50,560 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:58:50,560 DEBUG: Start:	 RandomSearch best settings with 1 iterations
-2016-09-08 09:58:50,594 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:58:50,595 DEBUG: Start:	 Training
-2016-09-08 09:58:50,596 DEBUG: Info:	 Time for Training: 0.0384030342102[s]
-2016-09-08 09:58:50,596 DEBUG: Done:	 Training
-2016-09-08 09:58:50,596 DEBUG: Start:	 Predicting
-2016-09-08 09:58:50,599 DEBUG: Done:	 Predicting
-2016-09-08 09:58:50,599 DEBUG: Start:	 Getting Results
-2016-09-08 09:58:50,609 DEBUG: Done:	 RandomSearch best settings
-2016-09-08 09:58:50,609 DEBUG: Start:	 Training
-2016-09-08 09:58:50,613 DEBUG: Info:	 Time for Training: 0.0557579994202[s]
-2016-09-08 09:58:50,614 DEBUG: Done:	 Training
-2016-09-08 09:58:50,614 DEBUG: Start:	 Predicting
-2016-09-08 09:58:50,616 DEBUG: Done:	 Predicting
-2016-09-08 09:58:50,617 DEBUG: Start:	 Getting Results
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c10750d4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.576923076923
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.576923076923
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -2.5337258102e-17
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.6
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ba0382db..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.56862745098
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.56862745098
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.00503027272866
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.557692307692
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.58
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5025
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fc54b504..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 24
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.6
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.661538461538
-		- Score on test : 0.678571428571
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.661538461538
-		- Score on test : 0.678571428571
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.419047619048
-		- Score on test : 0.4
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.580952380952
-		- Score on test : 0.6
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.130162282504
-		- Score on test : 0.171735516296
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.601398601399
-		- Score on test : 0.612903225806
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.735042735043
-		- Score on test : 0.76
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.561069754618
-		- Score on test : 0.58
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.419047619048
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b24a64e6..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 24, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.52427184466
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.52427184466
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.111088444626
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.509433962264
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.54
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.445
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9d76693a..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.655172413793
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.655172413793
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.444444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0674199862463
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.575757575758
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.76
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.53
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 867521e7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.637168141593
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.637168141593
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0487950036474
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.571428571429
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.72
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5225
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 589edf90..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 24
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.659176029963
-		- Score on test : 0.542056074766
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.659176029963
-		- Score on test : 0.542056074766
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.433333333333
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0939782359481
-		- Score on test : -0.123737644978
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.586666666667
-		- Score on test : 0.508771929825
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.752136752137
-		- Score on test : 0.58
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.542735042735
-		- Score on test : 0.44
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.433333333333
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1881529c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.644444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.644444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.661596958175
-		- Score on test : 0.724137931034
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.661596958175
-		- Score on test : 0.724137931034
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.42380952381
-		- Score on test : 0.355555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.57619047619
-		- Score on test : 0.644444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.117819078215
-		- Score on test : 0.269679944985
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.595890410959
-		- Score on test : 0.636363636364
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.74358974359
-		- Score on test : 0.84
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.55459057072
-		- Score on test : 0.62
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.42380952381
-		- Score on test : 0.355555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cbbd1a0d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7704
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.609523809524
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.609523809524
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.485714285714
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.0643090141189
-		- Score on test : 0.0662541348869
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.533333333333
-		- Score on test : 0.581818181818
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.615384615385
-		- Score on test : 0.64
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.468982630273
-		- Score on test : 0.5325
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.514285714286
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 23cf02f7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7704
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0973655073258
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.617647058824
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.42
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5475
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d276635c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View0	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7704
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.6
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.714285714286
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.714285714286
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.4
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.6
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.171377655346
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.592105263158
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.9
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5625
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index caa7c968..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.43956043956
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.43956043956
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.124719695673
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.487804878049
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4375
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 836977b7..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.43956043956
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.43956043956
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.433333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.124719695673
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.487804878049
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4375
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4dbb4c25..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 24, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.621359223301
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.621359223301
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.433333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.566666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.116137919381
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.603773584906
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.64
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5575
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d66ec8a4..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.555555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.640316205534
-		- Score on test : 0.666666666667
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.640316205534
-		- Score on test : 0.666666666667
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.433333333333
-		- Score on test : 0.444444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.566666666667
-		- Score on test : 0.555555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.104925880155
-		- Score on test : 0.0597614304667
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.595588235294
-		- Score on test : 0.571428571429
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.692307692308
-		- Score on test : 0.8
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.550454921423
-		- Score on test : 0.525
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.433333333333
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3b75a933..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7704
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.566801619433
-		- Score on test : 0.576923076923
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.566801619433
-		- Score on test : 0.576923076923
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.490476190476
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.0479423260647
-		- Score on test : -3.54721613428e-17
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.538461538462
-		- Score on test : 0.555555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.598290598291
-		- Score on test : 0.6
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.476564653984
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.509523809524
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d128db52..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7704
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0714285714286
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.0714285714286
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.422222222222
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.119522860933
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.333333333333
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.04
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.47
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d2a21462..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View1	 View shape : (300, 15)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7704
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.64
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.64
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.5
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.1
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.8
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4625
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d9a3f5a3..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.466666666667
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 24
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.466666666667
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.649446494465
-		- Score on test : 0.586206896552
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.649446494465
-		- Score on test : 0.586206896552
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.452380952381
-		- Score on test : 0.533333333333
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.547619047619
-		- Score on test : 0.466666666667
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.0476928413215
-		- Score on test : -0.134839972493
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.571428571429
-		- Score on test : 0.515151515152
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.752136752137
-		- Score on test : 0.68
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.521229666391
-		- Score on test : 0.44
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.452380952381
-		- Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8e7a8c2d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 24, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.995708154506
-		- Score on test : 0.468085106383
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.995708154506
-		- Score on test : 0.468085106383
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.995238095238
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.990406794809
-		- Score on test : -0.109345881217
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.991452991453
-		- Score on test : 0.44
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.995726495726
-		- Score on test : 0.445
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0047619047619
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9ee2ef1d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.701492537313
-		- Score on test : 0.592592592593
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.701492537313
-		- Score on test : 0.592592592593
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.380952380952
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.619047619048
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.210547659218
-		- Score on test : -0.0103806849817
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.622516556291
-		- Score on test : 0.551724137931
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.803418803419
-		- Score on test : 0.64
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.595257788806
-		- Score on test : 0.495
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.380952380952
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d16950a5..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.555555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7704
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.555555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.521739130435
-		- Score on test : 0.565217391304
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.521739130435
-		- Score on test : 0.565217391304
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.444444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.555555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.0568633060564
-		- Score on test : 0.119522860933
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.530973451327
-		- Score on test : 0.619047619048
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.512820512821
-		- Score on test : 0.52
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.471464019851
-		- Score on test : 0.56
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1fadae0e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.62
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.62
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.422222222222
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.577777777778
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.145
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.62
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.62
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5725
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ade21df1..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Decision Tree with max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.626262626263
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.626262626263
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.411111111111
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.588888888889
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.169618786115
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.632653061224
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.62
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.585
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ce39d233..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- K nearest Neighbors with  n_neighbors: 24
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.688888888889
-		- Score on test : 0.637168141593
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.688888888889
-		- Score on test : 0.637168141593
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.4
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.6
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.167225897665
-		- Score on test : 0.0487950036474
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.607843137255
-		- Score on test : 0.571428571429
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.794871794872
-		- Score on test : 0.72
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.574855252275
-		- Score on test : 0.5225
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.4
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a6d23dae..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- Random Forest with num_esimators : 24, max_depth : 25
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.533333333333
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.455555555556
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.117218854031
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.509090909091
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.56
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4425
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 53f3a855..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7704
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.107142857143
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.107142857143
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.444444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : -0.0298807152334
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.06
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.4925
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bf98d86d..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View2	 View shape : (300, 18)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7704
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.687022900763
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.687022900763
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.544444444444
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 4.13755692208e-17
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.555555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.9
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 1.0
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.0
-		- Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 04d9aee0..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SGDClassifier with loss : modified_huber, penalty : l2
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.642857142857
-		- Score on test : 0.533333333333
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.701195219124
-		- Score on test : 0.596153846154
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.701195219124
-		- Score on test : 0.596153846154
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.357142857143
-		- Score on test : 0.466666666667
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.642857142857
-		- Score on test : 0.533333333333
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : 0.266179454365
-		- Score on test : 0.0456435464588
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.65671641791
-		- Score on test : 0.574074074074
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.752136752137
-		- Score on test : 0.62
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.628756548111
-		- Score on test : 0.5225
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.357142857143
-		- Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d52376ec..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.511111111111
-
-Database configuration : 
-	- Database name : Fake
-	- View name : View3	 View shape : (300, 12)
-	- Learning Rate : 0.7
-	- Labels used : Non, Oui
-	- Number of cross validation folds : 5
-
-Classifier configuration : 
-	- SVM Linear with C : 7704
-	- Executed on 1 core(s) 
-	- Got configuration using randomized search with 1 iterations 
-
-
-	For Accuracy score using None as sample_weights (higher is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.511111111111
-	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
-		- Score on train : 0.541666666667
-		- Score on test : 0.576923076923
-	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
-		- Score on train : 0.541666666667
-		- Score on test : 0.576923076923
-	For Hamming loss using None as classes (lower is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.488888888889
-	For Jaccard similarity score using None as sample_weights (higher is better) : 
-		- Score on train : 0.47619047619
-		- Score on test : 0.511111111111
-	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
-		- Score on train : nan
-		- Score on test : nan
-	For Matthews correlation coefficient (higher is better) : 
-		- Score on train : -0.068670723144
-		- Score on test : 0.0
-	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.528455284553
-		- Score on test : 0.555555555556
-	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
-		- Score on train : 0.555555555556
-		- Score on test : 0.6
-	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
-		- Score on train : 0.465949820789
-		- Score on test : 0.5
-	For Zero one loss using None as sample_weights (lower is better) : 
-		- Score on train : 0.52380952381
-		- Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 31c59525..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 59.6470588235
-	-On Test : 50.7317073171
-	-On Validation : 47.191011236
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.395042964582, 0.135468886361, 0.187401197987, 0.282086951071
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- SGDClassifier with loss : log, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 4d7b96cd..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 55.2941176471
-	-On Test : 56.0975609756
-	-On Validation : 56.1797752809
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Majority Voting 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- SGDClassifier with loss : log, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:03        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 92e2e7eb..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 60.4705882353
-	-On Test : 47.8048780488
-	-On Validation : 53.2584269663
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with SVM for linear 
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- SGDClassifier with loss : log, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 90357518..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-		Result for Multiview classification with LateFusion
-
-Average accuracy :
-	-On Train : 25.6470588235
-	-On Test : 23.4146341463
-	-On Validation : 27.4157303371
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 0.342921905986, 0.474381813597, 0.714066510131
-	-With monoview classifiers : 
-		- SGDClassifier with loss : modified_huber, penalty : l1
-		- SGDClassifier with loss : log, penalty : l2
-		- SGDClassifier with loss : log, penalty : elasticnet
-		- SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3ebeedf2..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 100.0
-	-On Test : 54.6341463415
-	-On Validation : 49.2134831461
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.444263234099, 1.0, 0.292116326168, 0.822047817174 with monoview classifier : 
-		- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
-            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
-            min_samples_split=2, min_weight_fraction_leaf=0.0,
-            presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:02        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 007add07..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 97.4117647059
-	-On Test : 47.3170731707
-	-On Validation : 50.7865168539
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.564923899429, 0.171414234739, 1.0, 0.282773686486 with monoview classifier : 
-		- Decision Tree with max_depth : 8
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index d10e5809..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 60.4705882353
-	-On Test : 54.6341463415
-	-On Validation : 51.9101123596
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 0.567673336435, 0.401953729602, 0.0761117950819 with monoview classifier : 
-		- K nearest Neighbors with  n_neighbors: 40
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3109c44c..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 81.0588235294
-	-On Test : 43.4146341463
-	-On Validation : 48.9887640449
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.073909136797, 0.326197494021, 1.0, 0.0290483308675 with monoview classifier : 
-		- Random Forest with num_esimators : 1, max_depth : 8
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index e63a7f81..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 55.2941176471
-	-On Test : 56.0975609756
-	-On Validation : 56.1797752809
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 0.728775264645, 0.482876097673, 0.365130635662 with monoview classifier : 
-		- SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 6c39250e..00000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-		Result for Multiview classification with EarlyFusion
-
-Average accuracy :
-	-On Train : 57.7647058824
-	-On Test : 52.1951219512
-	-On Validation : 49.2134831461
-
-Dataset info :
-	-Database name : Fake
-	-Labels : Methyl, MiRNA_, RNASeq, Clinic
-	-Views : Methyl, MiRNA_, RNASeq, Clinic
-	-5 folds
-
-Classification configuration : 
-	-Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.073909136797, 0.326197494021, 1.0, 0.0290483308675 with monoview classifier : 
-		- SVM Linear with C : 3073
-
-Computation time on 1 cores : 
-	Database extraction time : 0:00:00
-	                         Learn     Prediction
-	         Fold 1        0:00:00        0:00:00
-	         Fold 2        0:00:00        0:00:00
-	         Fold 3        0:00:00        0:00:00
-	         Fold 4        0:00:00        0:00:00
-	         Fold 5        0:00:00        0:00:00
-	          Total        0:00:01        0:00:00
-	So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/utils/Transformations.py b/Code/MonoMutliViewClassifiers/utils/Transformations.py
new file mode 100644
index 00000000..a28cccd4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/utils/Transformations.py
@@ -0,0 +1,7 @@
+import numpy as np
+
+def signLabels(labels):
+    if set(labels) == (0,1):
+        return np.array([label if label != 0 else -1 for label in labels])
+    else:
+        return labels
-- 
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