diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index 34ab48ffc5028b79c395c61e861dbcda9730f4b4..b58cb12905a4f70beb85977f3136632e37bfacaa 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -311,26 +311,29 @@ else:
         bestClassifiers.append(classifiersNames[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
         bestClassifiersConfigs.append(classifiersConfigs[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
 monoviewTime = time.time()-dataBaseTime
+print resultsMonoview
 try:
     if benchmark["Multiview"]:
-        try:
-            if benchmark["Multiview"]["Mumbo"]:
-                for combination in itertools.combinations_with_replacement(range(len(benchmark["Multiview"]["Mumbo"])), NB_VIEW):
-                    classifiersNames = [benchmark["Multiview"]["Mumbo"][index] for index in combination]
-                    arguments = {"CL_type": "Mumbo",
-                                 "views": args.views.split(":"),
-                                 "NB_VIEW": len(args.views.split(":")),
-                                 "NB_CLASS": len(args.CL_classes.split(":")),
-                                 "LABELS_NAMES": args.CL_classes.split(":"),
-                                 "MumboKWARGS": {"classifiersNames": classifiersNames,
-                                                 "maxIter":int(args.MU_iter[0]), "minIter":int(args.MU_iter[1]),
-                                                 "threshold":args.MU_iter[2],
-                                                 "classifiersConfigs": [argument.split(":") for argument in args.MU_config]}}
-                    argumentDictionaries["Multiview"].append(arguments)
-        except:
-            pass
-#         bestClassifiers = ["DecisionTree", "DecisionTree", "DecisionTree", "DecisionTree"]
-
+        # try:
+        #     if benchmark["Multiview"]["Mumbo"]:
+        #         for combination in itertools.combinations_with_replacement(range(len(benchmark["Multiview"]["Mumbo"])), NB_VIEW):
+        #             classifiersNames = [benchmark["Multiview"]["Mumbo"][index] for index in combination]
+        #             arguments = {"CL_type": "Mumbo",
+        #                          "views": args.views.split(":"),
+        #                          "NB_VIEW": len(args.views.split(":")),
+        #                          "NB_CLASS": len(args.CL_classes.split(":")),
+        #                          "LABELS_NAMES": args.CL_classes.split(":"),
+        #                          "MumboKWARGS": {"classifiersNames": classifiersNames,
+        #                                          "maxIter":int(args.MU_iter[0]), "minIter":int(args.MU_iter[1]),
+        #                                          "threshold":args.MU_iter[2],
+        #                                          "classifiersConfigs": [argument.split(":") for argument in args.MU_config]}}
+        #             argumentDictionaries["Multiview"].append(arguments)
+        # except:
+        #     pass
+        # bestClassifiers = ["DecisionTree", "DecisionTree", "DecisionTree", "DecisionTree"]
+        # monoviewTime = 0
+        # resultsMonoview = []
+        # bestClassifiersConfigs = []
         try:
             if benchmark["Multiview"]["Fusion"]:
                 try:
@@ -368,6 +371,7 @@ try:
             pass
 except:
     pass
+# resultsMultiview = []
 if nbCores>1:
     resultsMultiview = []
     nbExperiments = len(argumentDictionaries["Multiview"])
@@ -375,12 +379,12 @@ if nbCores>1:
         resultsMultiview += Parallel(n_jobs=nbCores)(
             delayed(ExecMultiview_multicore)(coreIndex, args.name, args.CL_split, args.CL_nbFolds, args.type, args.pathF,
                                    LABELS_DICTIONARY, gridSearch=gridSearch,
-                                   metrics=metrics, **argumentDictionaries["Multiview"][stepIndex*nbCores+coreIndex])
+                                   metrics=metrics, nIter=args.CL_GS_iter, **argumentDictionaries["Multiview"][stepIndex*nbCores+coreIndex])
             for coreIndex in range(min(nbCores, nbExperiments - (stepIndex + 1) * nbCores)))
 else:
     resultsMultiview = [ExecMultiview(DATASET, args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
                                LABELS_DICTIONARY, gridSearch=gridSearch,
-                               metrics=metrics, **arguments) for arguments in argumentDictionaries["Multiview"]]
+                               metrics=metrics, nIter=args.CL_GS_iter, **arguments) for arguments in argumentDictionaries["Multiview"]]
 multiviewTime = time.time()-monoviewTime
 if nbCores>1:
     logging.debug("Start:\t Deleting "+str(nbCores)+" temporary datasets for multiprocessing")
@@ -390,6 +394,7 @@ if nbCores>1:
 times = [dataBaseTime, monoviewTime, multiviewTime]
 # times=[]
 results = (resultsMonoview, resultsMultiview)
+logging.debug("Start:\t Analyze Results")
 resultAnalysis(benchmark, results, args.name, times, metrics)
-
+logging.debug("Done:\t Analyze Results")
 
diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index 577a4999a8607e7198573f2751156a70b8477699..154a52c621ccb09c203e7c8057725adca999d03f 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -107,6 +107,7 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
                                          clKWARGS, classLabelsNames, X.shape,
                                          y_train, y_train_pred, y_test, y_test_pred, t_end)
     cl_desc = [value for key, value in sorted(clKWARGS.iteritems())]
+    print cl_desc
     logging.debug("Done:\t Getting Results")
     logging.info(stringAnalysis)
     labelsString = "-".join(classLabelsNames)
@@ -132,7 +133,7 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
 
     logging.info("Done:\t Result Analysis")
     viewIndex = args["viewIndex"]
-    return viewIndex, [CL_type, cl_desc.append(feat), metricsScores]
+    return viewIndex, [CL_type, cl_desc+[feat], metricsScores]
     # # 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/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
index 7af341b5e9d233473d1829e8635214cb84b7fb62..61707487d14c014bfb06bc63e7312499dcae4844 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
@@ -34,12 +34,11 @@ class WeightedLinear(EarlyFusionClassifier):
         self.weights = self.weights/float(max(self.weights))
         self.makeMonoviewData_hdf5(DATASET, weights=self.weights, usedIndices=trainIndices)
         monoviewClassifierModule = getattr(MonoviewClassifiers, self.monoviewClassifierName)
-        print self.monoviewClassifiersConfig
         self.monoviewClassifier = monoviewClassifierModule.fit(self.monoviewData, DATASET.get("labels")[trainIndices],
-                                                                     NB_CORES=self.nbCores, **self.monoviewClassifiersConfig)
-                                                                     #**dict((str(configIndex), config) for configIndex, config in
-                                                                      #      enumerate(self.monoviewClassifiersConfig
-                                                                       #               )))
+                                                                     NB_CORES=self.nbCores, #**self.monoviewClassifiersConfig)
+                                                                     **dict((str(configIndex), config) for configIndex, config in
+                                                                           enumerate(self.monoviewClassifiersConfig
+                                                                                     )))
 
     def predict_hdf5(self, DATASET, usedIndices=None):
         self.weights = self.weights/float(max(self.weights))
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/WeightedLinear.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/WeightedLinear.py
index d3e05e42eb6f7d0dbb531878fa8b5bc817871d1d..9bb535f09ba3e60c0a9a96841b57f4bc112c98ff 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/WeightedLinear.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/WeightedLinear.py
@@ -26,7 +26,10 @@ class WeightedLinear(LateFusionClassifier):
     def __init__(self, NB_CORES=1, **kwargs):
         LateFusionClassifier.__init__(self, kwargs['classifiersNames'], kwargs['classifiersConfigs'],
                                       NB_CORES=NB_CORES)
-        self.weights = np.array(map(float, kwargs['fusionMethodConfig'][0]))
+        if kwargs['fusionMethodConfig'][0]==None:
+            self.weights = np.ones(len(kwargs["classifiersNames"]), dtype=float)
+        else:
+            self.weights = np.array(map(float, kwargs['fusionMethodConfig'][0]))
 
     def predict_hdf5(self, DATASET, usedIndices=None):
         self.weights = self.weights/float(max(self.weights))
diff --git a/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
index b944ffa99b61febd59bce637c34cd459f7d6602f..8a60070a1a6adce323c106ef1f047ea4bfe2bb4f 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
@@ -1,4 +1,5 @@
 import numpy as np
+import math
 from string import digits
 import os
 import random
@@ -259,6 +260,54 @@ def getMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
     return datasetFile, labelDictionary
 
 
+def findClosestPowerOfTwo(k):
+    power=1
+    while k-power>0:
+        power = 2*power
+    if abs(k-power)<abs(k-power/2):
+        return power
+    else:
+        return power/2
+
+
+def getVector(matrix):
+    argmax = [0,0]
+    n = len(matrix)
+    maxi = 0
+    for i in range(n):
+        for j in range(n):
+            if j==i+1:
+                value = (i+1)*(n-j)
+                if value>maxi:
+                    maxi= value
+                    argmax = [i,j]
+    i,j = argmax
+    vector = np.zeros(n, dtype=bool)
+    vector[:i+1]=np.ones(i+1, dtype=bool)
+    matrixSup = [i+1, j+1]
+    matrixInf = [i+1, j+1]
+    return vector, matrixSup, matrixInf
+
+
+def easyFactorize(targetMatrix, k, t=0):
+    n = len(targetMatrix)
+    if math.log(k+1, 2)%1==0.0:
+        pass
+    else:
+        k = findClosestPowerOfTwo(k)-1
+    if k==1:
+        t=1
+        return t, getVector(targetMatrix)[0]
+    vector, matrixSup, matrixInf = getVector(targetMatrix)
+    t, vectorSup = easyFactorize(targetMatrix[:matrixSup[0], :matrixSup[1]], (k-1)/2, t)
+    t, vectorInf = easyFactorize(targetMatrix[matrixInf[0]:, matrixInf[0]:], (k-1)/2, t)
+    factor = np.zeros((n,2*t+1), dtype=bool)
+    factor[:matrixSup[0], :t] = vectorSup.reshape(factor[:matrixSup[0], :t].shape)
+    factor[matrixInf[0]:, t:2*t] = vectorInf.reshape(factor[matrixInf[0]:, t:2*t].shape)
+    factor[:, 2*t] = vector
+    return 2*t+1, factor
+
+
 def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
 
     datasetFile = h5py.File(path+"ModifiedMultiOmic.hdf5", "w")
@@ -307,6 +356,16 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
     mrnaseqDset.attrs["name"] = "SRNASeq"
     logging.debug("Done:\t Getting Sorted RNASeq Data")
 
+    logging.debug("Start:\t Getting Binarized RNASeq Data")
+    factorizedBaseMatrix = np.genfromtxt(path+"factorMatrix.csv", delimiter=',')
+    brnaseqDset = datasetFile.create_dataset("View5", len(modifiedRNASeq), len(factorizedBaseMatrix.flatten()))
+    for patientIndex, patientSortedArray in enumerate(modifiedRNASeq):
+        patientMatrix = np.zeros(factorizedBaseMatrix.shape, dtype=bool)
+        for lineIndex, geneIndex in enumerate(patientSortedArray):
+            patientMatrix[geneIndex]=factorizedBaseMatrix[lineIndex]
+        brnaseqDset[patientIndex] = patientMatrix.flatten()
+    logging.debug("Done:\t Getting Binarized RNASeq Data")
+
     # logging.debug("Start:\t Getting Binned RNASeq Data")
     # SRNASeq = datasetFile["View4"][...]
     # nbBins = 372
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py
index 9a894de3e51f4d6526876c8452926b39b0596d88..7214759ae323465701a72467a60a361d66bb56b7 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py
@@ -50,7 +50,7 @@ def gridSearch_hdf5(DATASET, classificationKWARGS, learningIndices, metric=None,
     classifiersNames = classificationKWARGS["classifiersNames"]
     bestSettings = []
     for classifierIndex, classifierName in enumerate(classifiersNames):
-        logging.debug("\tStart:\t Gridsearch for "+classifierName+" on "+DATASET.get("View"+str(classifierIndex)).attrs["name"])
+        logging.debug("\tStart:\t Random search for "+classifierName+" on "+DATASET.get("View"+str(classifierIndex)).attrs["name"])
         classifierModule = globals()[classifierName]  # Permet d'appeler une fonction avec une string
         classifierMethod = getattr(classifierModule, "gridSearch")
         bestSettings.append(classifierMethod(DATASET.get("View"+str(classifierIndex))[learningIndices],
diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index ff02817fe307d69623975821d7f52d1e4398a75b..9782f7d57cc3301c59f80c5e1991bc85517eb808 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -20,7 +20,9 @@ def resultAnalysis(benchmark, results, name, times, metrics):
     for metric in metrics:
         mono, multi = results
         names = [res[1][0]+"-"+res[1][1][-1] for res in mono]
-        names+=[type_ if type_ != "Fusion" else a["fusionType"]+"-"+a["fusionMethod"] for type_, a, b in multi]
+        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"]
+        names+=[ "Early-"+a["fusionMethod"]+"-"+a["classifiersNames"][0]  for type_, a, b in multi if type_ == "Fusion" and a["fusionType"] != "LateFusion"]
         nbResults = len(mono)+len(multi)
         validationScores = [float(res[1][2][metric[0]][2]) for res in mono]
         validationScores += [float(scores[metric[0]][2]) for a, b, scores in multi]
diff --git a/Code/MonoMutliViewClassifiers/Results/20160907-162651-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160907-162651-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..7790e9fe4b578b6955d56e73f1cb1383ef61f7ab
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160907-162651-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,224 @@
+2016-09-07 16:26:51,762 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-07 16:26:51,762 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.273145851562 Gbytes /!\ 
+2016-09-07 16:27:02,716 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-07 16:27:02,972 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-07 16:27:04,713 DEBUG: ### Main Programm for Classification MonoView
+2016-09-07 16:27:04,714 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-07 16:27:04,714 DEBUG: Start:	 Determine Train/Test split
+2016-09-07 16:27:04,911 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-07 16:27:04,911 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-07 16:27:04,911 DEBUG: Done:	 Determine Train/Test split
+2016-09-07 16:27:04,911 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-07 16:27:06,463 DEBUG: ### Main Programm for Classification MonoView
+2016-09-07 16:27:06,464 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-07 16:27:06,464 DEBUG: Start:	 Determine Train/Test split
+2016-09-07 16:27:06,499 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-07 16:27:06,499 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-07 16:27:06,499 DEBUG: Done:	 Determine Train/Test split
+2016-09-07 16:27:06,499 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-07 16:27:14,206 DEBUG: Done:	 RandomSearch best settings
+2016-09-07 16:27:14,206 DEBUG: Start:	 Training
+2016-09-07 16:27:15,153 DEBUG: Done:	 RandomSearch best settings
+2016-09-07 16:27:15,153 DEBUG: Start:	 Training
+2016-09-07 16:27:16,232 DEBUG: Info:	 Time for Training: 13.1729319096[s]
+2016-09-07 16:27:16,232 DEBUG: Done:	 Training
+2016-09-07 16:27:16,233 DEBUG: Start:	 Predicting
+2016-09-07 16:27:16,348 DEBUG: Done:	 Predicting
+2016-09-07 16:27:16,348 DEBUG: Start:	 Getting Results
+2016-09-07 16:27:16,926 DEBUG: Done:	 Getting Results
+2016-09-07 16:27:16,926 INFO: Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- 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 : 1.0
+		- Score on test : 0.771428571429
+	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 : 0.586206896552
+	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 : 1.0
+		- Score on test : 0.586206896552
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.228571428571
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.771428571429
+	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 : 0.440385506051
+	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 : 0.68
+	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 : 0.515151515152
+	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.70202020202
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.228571428571
+
+
+ Classification took 0:00:13
+2016-09-07 16:27:16,964 INFO: Done:	 Result Analysis
+2016-09-07 16:27:17,029 DEBUG: Info:	 Time for Training: 13.9593689442[s]
+2016-09-07 16:27:17,029 DEBUG: Done:	 Training
+2016-09-07 16:27:17,029 DEBUG: Start:	 Predicting
+2016-09-07 16:27:17,043 DEBUG: Done:	 Predicting
+2016-09-07 16:27:17,043 DEBUG: Start:	 Getting Results
+2016-09-07 16:27:17,076 DEBUG: Done:	 Getting Results
+2016-09-07 16:27:17,076 INFO: Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- 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 : 0.790476190476
+	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 : 0.592592592593
+	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 : 1.0
+		- Score on test : 0.592592592593
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.209523809524
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.790476190476
+	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 : 0.482108339669
+	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 : 0.761904761905
+	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 : 0.484848484848
+	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.707702020202
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.209523809524
+
+
+ Classification took 0:00:13
+2016-09-07 16:27:17,076 INFO: Done:	 Result Analysis
+2016-09-07 16:27:18,338 DEBUG: ### Main Programm for Classification MonoView
+2016-09-07 16:27:18,339 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-07 16:27:18,339 DEBUG: Start:	 Determine Train/Test split
+2016-09-07 16:27:18,379 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-07 16:27:18,379 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-07 16:27:18,379 DEBUG: Done:	 Determine Train/Test split
+2016-09-07 16:27:18,379 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-07 16:27:18,521 DEBUG: ### Main Programm for Classification MonoView
+2016-09-07 16:27:18,522 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-07 16:27:18,522 DEBUG: Start:	 Determine Train/Test split
+2016-09-07 16:27:18,570 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-07 16:27:18,571 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-07 16:27:18,571 DEBUG: Done:	 Determine Train/Test split
+2016-09-07 16:27:18,571 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-07 16:27:18,830 DEBUG: Done:	 RandomSearch best settings
+2016-09-07 16:27:18,830 DEBUG: Start:	 Training
+2016-09-07 16:27:18,913 DEBUG: Info:	 Time for Training: 1.72966194153[s]
+2016-09-07 16:27:18,914 DEBUG: Done:	 Training
+2016-09-07 16:27:18,914 DEBUG: Start:	 Predicting
+2016-09-07 16:27:18,924 DEBUG: Done:	 Predicting
+2016-09-07 16:27:18,924 DEBUG: Start:	 Getting Results
+2016-09-07 16:27:19,006 DEBUG: Done:	 Getting Results
+2016-09-07 16:27:19,006 INFO: Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 0.97520661157
+accuracy_score on test : 0.761904761905
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 6, 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 : 0.97520661157
+		- Score on test : 0.761904761905
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.949152542373
+		- 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 : 0.949152542373
+		- Score on test : 0.468085106383
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0247933884298
+		- Score on test : 0.238095238095
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97520661157
+		- Score on test : 0.761904761905
+	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 : 0.932999668908
+		- Score on test : 0.398313753408
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.965517241379
+		- Score on test : 0.785714285714
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.933333333333
+		- Score on test : 0.333333333333
+	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.961172161172
+		- Score on test : 0.645833333333
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0247933884298
+		- Score on test : 0.238095238095
+
+
+ Classification took 0:00:01
+2016-09-07 16:27:19,007 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160907-162716Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160907-162716Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e9719acf0c5fcc94a1c5ad47a4f0609ba08055af
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160907-162716Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,54 @@
+Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- 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 : 1.0
+		- Score on test : 0.771428571429
+	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 : 0.586206896552
+	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 : 1.0
+		- Score on test : 0.586206896552
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.228571428571
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.771428571429
+	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 : 0.440385506051
+	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 : 0.68
+	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 : 0.515151515152
+	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.70202020202
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.228571428571
+
+
+ Classification took 0:00:13
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160907-162717Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160907-162717Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..32fa654fd647c09fec791c94b3f3b876d76473b9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160907-162717Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,57 @@
+Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- 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 : 0.790476190476
+	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 : 0.592592592593
+	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 : 1.0
+		- Score on test : 0.592592592593
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.209523809524
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.790476190476
+	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 : 0.482108339669
+	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 : 0.761904761905
+	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 : 0.484848484848
+	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.707702020202
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.209523809524
+
+
+ Classification took 0:00:13
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160907-162719Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160907-162719Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..97c69cd4414a34cedd3472bb2f1a24d98437e90b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160907-162719Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,54 @@
+Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 0.97520661157
+accuracy_score on test : 0.761904761905
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 6, 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 : 0.97520661157
+		- Score on test : 0.761904761905
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.949152542373
+		- 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 : 0.949152542373
+		- Score on test : 0.468085106383
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0247933884298
+		- Score on test : 0.238095238095
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97520661157
+		- Score on test : 0.761904761905
+	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 : 0.932999668908
+		- Score on test : 0.398313753408
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.965517241379
+		- Score on test : 0.785714285714
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.933333333333
+		- Score on test : 0.333333333333
+	For ROC AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.961172161172
+		- Score on test : 0.645833333333
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0247933884298
+		- Score on test : 0.238095238095
+
+
+ Classification took 0:00:01
\ No newline at end of file
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
new file mode 100644
index 0000000000000000000000000000000000000000..52d4e73fa353f4c5e4dd770a4fbf8026a0dfdd7b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095527-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,2211 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..0d0cf3ff0c83bca73e37fc715e290390079bbb69
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..9f126a9ea93eea83d8098254d24706eaafb2b497
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..79be41beb0284d7bf6eb3848621838655d406c4c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..bbd624877d4bfc10a8b89482d2ec1a7829577c75
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..fd413a5d803bf18dd794b651f84d3eeb305d6c94
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..7b2f009cb9959d5bd993cf1b27c37272d3ffaf39
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..db536ff68f9249c6cab5a9be1ebf6c21dbb9de58
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..4251899bb7a8bf556dee03c2c785c4ac0e48d076
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..a32b018a2505e28f037bf72ff427fc03005ce552
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..b096d574bd224677355e3b214b9cfe60865aabed
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..376343e68cd0ba94ded748ff13a5313cc2f34cb9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..c3e2e2995350f4e7c2570de375e0fe6d36fb5526
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..1bd711b474351f5d09ddf4a343c4f7ee989d2036
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..6b6c9c97508ff479293091869f19742e26369f0a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..5bc5811d3a8eecb74b0763c70685c8d806b52e8b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..ccd0f1ad914c25ad47b37f5f969f4202f2a8de66
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..9deb12f3b14d4f8565c6168fa4a139bddf91fb16
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..183d09191497b8ab3804d324b510249818998073
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..8cf553cc1128b3958539e214301a382219781461
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..f74150b10001487507af804c177f45b9af53b193
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..608729cd17cff989b5994f699bcc2c9f7d249468
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..59671aeef690b38b65227b5dd243edfa5b9b1e5b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..a085f7609062a0cf55e082cfc7420a115dad6758
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..cf24b718b7d42ee523ad9050390a3c2e19696090
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..b93b2e14fa71d3dfb0ae9bead5ea8c59f93ea5b9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..ed86c3be7a62566d0a9aeb7b157f834ad4429c2d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..81a8c71b0206a996bae52dbc3db9c74f8f8ceb77
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..5a7ef634e77ff082d71a4580ab038c25426e46c2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..616ff06f5c3552206bb0e28675b677ea6d137aac
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..226afbe88c8d26e49858a82280f274251102f2e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..d2af511daa99659d08543c83537f0fa618a2ebef
--- /dev/null
+++ 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
@@ -0,0 +1,32 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..e8dd4634725d66617aab8ac7917d7f46df47a448
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,2394 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..03f454c1907151b2cd4700f0b9fa21f634a895a3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..1121217156862cbd341038acd8d0bb7420aea4f5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..717561a91111c362546c229613b377405c534f58
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..db639d874780820a793d5b425131f599b619d1a1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..63f60667fbbb850f1c17b27d456a7ad94bf2a1cd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..450f0ea162ee5681ba44e7e36c2da99ff7c208db
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..3d2d8158208053f14d8d289f1b53da5a0ff267a7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..511f2feec38b0ab80eca16401cbe7d50a5a52472
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..2bda3cf3e9865595e5900f4a617e2f7210d1b111
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..59cdb6c697d146d7d3ed98f76ae3654d60df8f00
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..a96b05b6ca83ab7a200d3d56ec086a92a28bbee4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..46f9acd1a6f415e8ac5c14de01639553c10c8914
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..c2daea4e1eb4967649e0a7bd2c099f23f75f51e8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..65a1d806d6c532ff7273d35788ad31ff125bf076
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..ffa02970261e5fd6c6ee7c7c89f28a71c8c1cb80
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..664dd67dd1b310bf15826196f3f32bed9c3db30e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..1026306a026375c58d260dba33e7e4dd921b4d8c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..e47d02bff46273cea19d86bfd2d436741ac501b5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..15b07a9ccd577250db3514055248e247ee8ca5ac
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..70517c8c47fe2344c26b87465cad2ea40f5e082b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..fd86ceca50e091c5d36f8d06c22854be9865701e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..ea0b1413c2f836e668154e840087b108a965c0cf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..7ea3a02f3a5aa6f8340cf74af6bcb60cdc9941ac
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..a3cc29473420a6a6e84a2cba2dca5272ed452636
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..889e2b6cb95717c6978b1c1df26d27a2cf59c58b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..df0c84da34a8bbfff60cea62bfe7affe7827d513
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..58bf957cbce20026512b4f37e8e68de25eb1edc6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..b171204485ec1e649cf7ce79673da7cf18354e76
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..ac66b07e027445e598fa09e5c12feee404ee19f5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..c4a246c96517e4894fffb96593914dc482cc196e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..5ec167846b7f8da92aa16cc60fec7b475c81e8a8
--- /dev/null
+++ 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
@@ -0,0 +1,32 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..f9c3ded12ed06616f706139519336a54251ed8d1
--- /dev/null
+++ 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
@@ -0,0 +1,32 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..457213b07a7b6ea964f2fc98f31d8784d8005e60
--- /dev/null
+++ 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
@@ -0,0 +1,32 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..d434781c220fac77883c4cff413c638b54560262
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095845-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,32 @@
+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/20160908-095958-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160908-095958-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..9a77052c7e7b44b976d23973f6729d2cf0291fab
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-095958-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,2842 @@
+2016-09-08 09:59:58,949 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-08 09:59:58,950 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000152125 Gbytes /!\ 
+2016-09-08 10:00:03,964 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-08 10:00:03,968 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-08 10:00:04,015 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:04,015 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:04,015 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-08 10:00:04,015 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-08 10:00:04,016 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:04,016 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:04,016 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:04,016 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:04,017 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:04,017 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:04,017 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:04,017 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:04,017 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:04,017 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:04,051 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:04,052 DEBUG: Start:	 Training
+2016-09-08 10:00:04,053 DEBUG: Info:	 Time for Training: 0.0387499332428[s]
+2016-09-08 10:00:04,054 DEBUG: Done:	 Training
+2016-09-08 10:00:04,054 DEBUG: Start:	 Predicting
+2016-09-08 10:00:04,056 DEBUG: Done:	 Predicting
+2016-09-08 10:00:04,056 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:04,067 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:04,067 DEBUG: Start:	 Training
+2016-09-08 10:00:04,071 DEBUG: Info:	 Time for Training: 0.0559167861938[s]
+2016-09-08 10:00:04,071 DEBUG: Done:	 Training
+2016-09-08 10:00:04,071 DEBUG: Start:	 Predicting
+2016-09-08 10:00:04,073 DEBUG: Done:	 Predicting
+2016-09-08 10:00:04,074 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:04,106 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:04,106 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, 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
+2016-09-08 10:00:04,106 INFO: Done:	 Result Analysis
+2016-09-08 10:00:04,108 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:04,108 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, 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
+2016-09-08 10:00:04,109 INFO: Done:	 Result Analysis
+2016-09-08 10:00:04,267 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:04,268 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-08 10:00:04,268 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:04,268 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:04,268 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-08 10:00:04,268 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:04,269 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:04,269 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:04,269 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:04,269 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:04,269 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:04,269 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:04,269 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:04,270 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:04,300 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:04,301 DEBUG: Start:	 Training
+2016-09-08 10:00:04,301 DEBUG: Info:	 Time for Training: 0.0347349643707[s]
+2016-09-08 10:00:04,301 DEBUG: Done:	 Training
+2016-09-08 10:00:04,301 DEBUG: Start:	 Predicting
+2016-09-08 10:00:04,307 DEBUG: Done:	 Predicting
+2016-09-08 10:00:04,307 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:04,347 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:04,347 INFO: 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
+2016-09-08 10:00:04,347 INFO: Done:	 Result Analysis
+2016-09-08 10:00:04,669 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:04,669 DEBUG: Start:	 Training
+2016-09-08 10:00:04,730 DEBUG: Info:	 Time for Training: 0.462595939636[s]
+2016-09-08 10:00:04,730 DEBUG: Done:	 Training
+2016-09-08 10:00:04,730 DEBUG: Start:	 Predicting
+2016-09-08 10:00:04,737 DEBUG: Done:	 Predicting
+2016-09-08 10:00:04,737 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:04,771 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:04,771 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 : 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
+2016-09-08 10:00:04,771 INFO: Done:	 Result Analysis
+2016-09-08 10:00:04,916 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:04,916 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-08 10:00:04,917 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:04,917 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:04,917 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-08 10:00:04,917 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:04,918 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:04,918 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:04,918 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:04,918 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:04,919 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:04,919 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:04,919 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:04,919 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:04,964 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:04,964 DEBUG: Start:	 Training
+2016-09-08 10:00:04,965 DEBUG: Info:	 Time for Training: 0.049379825592[s]
+2016-09-08 10:00:04,965 DEBUG: Done:	 Training
+2016-09-08 10:00:04,965 DEBUG: Start:	 Predicting
+2016-09-08 10:00:04,971 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:04,971 DEBUG: Start:	 Training
+2016-09-08 10:00:04,979 DEBUG: Done:	 Predicting
+2016-09-08 10:00:04,980 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:04,997 DEBUG: Info:	 Time for Training: 0.0806729793549[s]
+2016-09-08 10:00:04,997 DEBUG: Done:	 Training
+2016-09-08 10:00:04,997 DEBUG: Start:	 Predicting
+2016-09-08 10:00:05,000 DEBUG: Done:	 Predicting
+2016-09-08 10:00:05,000 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:05,005 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:05,005 INFO: 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
+2016-09-08 10:00:05,005 INFO: Done:	 Result Analysis
+2016-09-08 10:00:05,029 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:05,029 INFO: 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
+2016-09-08 10:00:05,030 INFO: Done:	 Result Analysis
+2016-09-08 10:00:05,163 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:05,164 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-08 10:00:05,164 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:05,164 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:05,164 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-08 10:00:05,164 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:05,164 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:05,165 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:05,165 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:05,165 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:05,165 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:05,165 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:05,165 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:05,165 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:05,212 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:05,213 DEBUG: Start:	 Training
+2016-09-08 10:00:05,218 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:05,219 DEBUG: Start:	 Training
+2016-09-08 10:00:05,230 DEBUG: Info:	 Time for Training: 0.0661790370941[s]
+2016-09-08 10:00:05,230 DEBUG: Done:	 Training
+2016-09-08 10:00:05,230 DEBUG: Start:	 Predicting
+2016-09-08 10:00:05,235 DEBUG: Done:	 Predicting
+2016-09-08 10:00:05,236 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:05,239 DEBUG: Info:	 Time for Training: 0.0759570598602[s]
+2016-09-08 10:00:05,239 DEBUG: Done:	 Training
+2016-09-08 10:00:05,239 DEBUG: Start:	 Predicting
+2016-09-08 10:00:05,243 DEBUG: Done:	 Predicting
+2016-09-08 10:00:05,243 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:05,275 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:05,275 INFO: 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
+2016-09-08 10:00:05,275 INFO: Done:	 Result Analysis
+2016-09-08 10:00:05,283 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:05,283 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 : 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
+2016-09-08 10:00:05,283 INFO: Done:	 Result Analysis
+2016-09-08 10:00:05,410 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:05,410 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:05,411 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-08 10:00:05,411 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-08 10:00:05,411 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:05,411 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:05,411 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-08 10:00:05,411 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-08 10:00:05,411 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-08 10:00:05,411 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-08 10:00:05,412 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:05,412 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:05,412 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:05,412 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:05,447 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:05,447 DEBUG: Start:	 Training
+2016-09-08 10:00:05,450 DEBUG: Info:	 Time for Training: 0.0400369167328[s]
+2016-09-08 10:00:05,450 DEBUG: Done:	 Training
+2016-09-08 10:00:05,450 DEBUG: Start:	 Predicting
+2016-09-08 10:00:05,452 DEBUG: Done:	 Predicting
+2016-09-08 10:00:05,453 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:05,462 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:05,462 DEBUG: Start:	 Training
+2016-09-08 10:00:05,467 DEBUG: Info:	 Time for Training: 0.0573270320892[s]
+2016-09-08 10:00:05,467 DEBUG: Done:	 Training
+2016-09-08 10:00:05,467 DEBUG: Start:	 Predicting
+2016-09-08 10:00:05,471 DEBUG: Done:	 Predicting
+2016-09-08 10:00:05,471 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:05,499 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:05,499 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, 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
+2016-09-08 10:00:05,499 INFO: Done:	 Result Analysis
+2016-09-08 10:00:05,516 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:05,516 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, 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
+2016-09-08 10:00:05,517 INFO: Done:	 Result Analysis
+2016-09-08 10:00:05,656 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:05,656 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-08 10:00:05,656 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:05,656 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:05,656 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-08 10:00:05,657 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:05,657 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-08 10:00:05,657 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-08 10:00:05,657 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-08 10:00:05,657 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:05,657 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-08 10:00:05,657 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:05,657 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:05,657 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:05,688 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:05,688 DEBUG: Start:	 Training
+2016-09-08 10:00:05,689 DEBUG: Info:	 Time for Training: 0.0336298942566[s]
+2016-09-08 10:00:05,689 DEBUG: Done:	 Training
+2016-09-08 10:00:05,689 DEBUG: Start:	 Predicting
+2016-09-08 10:00:05,695 DEBUG: Done:	 Predicting
+2016-09-08 10:00:05,695 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:05,735 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:05,735 INFO: 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
+2016-09-08 10:00:05,735 INFO: Done:	 Result Analysis
+2016-09-08 10:00:06,049 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:06,050 DEBUG: Start:	 Training
+2016-09-08 10:00:06,110 DEBUG: Info:	 Time for Training: 0.454261064529[s]
+2016-09-08 10:00:06,110 DEBUG: Done:	 Training
+2016-09-08 10:00:06,110 DEBUG: Start:	 Predicting
+2016-09-08 10:00:06,117 DEBUG: Done:	 Predicting
+2016-09-08 10:00:06,117 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:06,151 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:06,151 INFO: 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
+2016-09-08 10:00:06,151 INFO: Done:	 Result Analysis
+2016-09-08 10:00:06,304 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:06,304 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-08 10:00:06,304 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:06,304 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:06,305 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-08 10:00:06,305 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:06,305 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-08 10:00:06,305 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-08 10:00:06,305 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:06,305 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:06,305 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-08 10:00:06,306 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-08 10:00:06,306 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:06,306 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:06,348 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:06,349 DEBUG: Start:	 Training
+2016-09-08 10:00:06,349 DEBUG: Info:	 Time for Training: 0.0459690093994[s]
+2016-09-08 10:00:06,349 DEBUG: Done:	 Training
+2016-09-08 10:00:06,350 DEBUG: Start:	 Predicting
+2016-09-08 10:00:06,356 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:06,356 DEBUG: Start:	 Training
+2016-09-08 10:00:06,379 DEBUG: Done:	 Predicting
+2016-09-08 10:00:06,379 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:06,381 DEBUG: Info:	 Time for Training: 0.0768840312958[s]
+2016-09-08 10:00:06,381 DEBUG: Done:	 Training
+2016-09-08 10:00:06,381 DEBUG: Start:	 Predicting
+2016-09-08 10:00:06,387 DEBUG: Done:	 Predicting
+2016-09-08 10:00:06,387 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:06,406 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:06,406 INFO: 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
+2016-09-08 10:00:06,407 INFO: Done:	 Result Analysis
+2016-09-08 10:00:06,420 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:06,420 INFO: 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
+2016-09-08 10:00:06,420 INFO: Done:	 Result Analysis
+2016-09-08 10:00:06,547 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:06,547 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:06,547 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-08 10:00:06,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-08 10:00:06,548 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:06,548 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:06,548 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-08 10:00:06,548 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-08 10:00:06,549 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-08 10:00:06,549 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-08 10:00:06,549 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:06,549 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:06,549 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:06,549 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:06,595 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:06,595 DEBUG: Start:	 Training
+2016-09-08 10:00:06,601 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:06,601 DEBUG: Start:	 Training
+2016-09-08 10:00:06,614 DEBUG: Info:	 Time for Training: 0.0678429603577[s]
+2016-09-08 10:00:06,614 DEBUG: Done:	 Training
+2016-09-08 10:00:06,615 DEBUG: Start:	 Predicting
+2016-09-08 10:00:06,620 DEBUG: Info:	 Time for Training: 0.0734059810638[s]
+2016-09-08 10:00:06,620 DEBUG: Done:	 Training
+2016-09-08 10:00:06,620 DEBUG: Start:	 Predicting
+2016-09-08 10:00:06,621 DEBUG: Done:	 Predicting
+2016-09-08 10:00:06,621 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:06,624 DEBUG: Done:	 Predicting
+2016-09-08 10:00:06,624 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:06,657 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:06,657 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:06,657 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, 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
+2016-09-08 10:00:06,657 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, 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
+2016-09-08 10:00:06,657 INFO: Done:	 Result Analysis
+2016-09-08 10:00:06,657 INFO: Done:	 Result Analysis
+2016-09-08 10:00:06,793 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:06,793 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-08 10:00:06,793 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:06,794 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:06,794 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-08 10:00:06,794 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:06,794 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-08 10:00:06,794 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-08 10:00:06,794 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:06,794 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:06,795 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-08 10:00:06,795 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-08 10:00:06,795 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:06,795 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:06,834 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:06,834 DEBUG: Start:	 Training
+2016-09-08 10:00:06,837 DEBUG: Info:	 Time for Training: 0.0438461303711[s]
+2016-09-08 10:00:06,837 DEBUG: Done:	 Training
+2016-09-08 10:00:06,837 DEBUG: Start:	 Predicting
+2016-09-08 10:00:06,839 DEBUG: Done:	 Predicting
+2016-09-08 10:00:06,839 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:06,847 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:06,847 DEBUG: Start:	 Training
+2016-09-08 10:00:06,852 DEBUG: Info:	 Time for Training: 0.0599958896637[s]
+2016-09-08 10:00:06,852 DEBUG: Done:	 Training
+2016-09-08 10:00:06,852 DEBUG: Start:	 Predicting
+2016-09-08 10:00:06,855 DEBUG: Done:	 Predicting
+2016-09-08 10:00:06,855 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:06,885 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:06,885 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, 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
+2016-09-08 10:00:06,885 INFO: Done:	 Result Analysis
+2016-09-08 10:00:06,896 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:06,896 INFO: 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
+2016-09-08 10:00:06,897 INFO: Done:	 Result Analysis
+2016-09-08 10:00:07,034 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:07,034 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:07,034 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-08 10:00:07,034 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-08 10:00:07,035 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:07,035 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:07,035 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-08 10:00:07,035 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-08 10:00:07,035 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-08 10:00:07,035 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-08 10:00:07,035 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:07,035 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:07,036 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:07,036 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:07,067 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:07,067 DEBUG: Start:	 Training
+2016-09-08 10:00:07,067 DEBUG: Info:	 Time for Training: 0.0337820053101[s]
+2016-09-08 10:00:07,067 DEBUG: Done:	 Training
+2016-09-08 10:00:07,068 DEBUG: Start:	 Predicting
+2016-09-08 10:00:07,074 DEBUG: Done:	 Predicting
+2016-09-08 10:00:07,074 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:07,115 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:07,115 INFO: 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
+2016-09-08 10:00:07,115 INFO: Done:	 Result Analysis
+2016-09-08 10:00:07,444 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:07,444 DEBUG: Start:	 Training
+2016-09-08 10:00:07,507 DEBUG: Info:	 Time for Training: 0.473404169083[s]
+2016-09-08 10:00:07,507 DEBUG: Done:	 Training
+2016-09-08 10:00:07,507 DEBUG: Start:	 Predicting
+2016-09-08 10:00:07,514 DEBUG: Done:	 Predicting
+2016-09-08 10:00:07,514 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:07,544 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:07,544 INFO: 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
+2016-09-08 10:00:07,544 INFO: Done:	 Result Analysis
+2016-09-08 10:00:07,685 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:07,685 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:07,685 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-08 10:00:07,685 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-08 10:00:07,685 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:07,686 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:07,686 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-08 10:00:07,686 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-08 10:00:07,686 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-08 10:00:07,686 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-08 10:00:07,686 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:07,686 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:07,686 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:07,687 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:07,730 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:07,730 DEBUG: Start:	 Training
+2016-09-08 10:00:07,731 DEBUG: Info:	 Time for Training: 0.0463371276855[s]
+2016-09-08 10:00:07,731 DEBUG: Done:	 Training
+2016-09-08 10:00:07,731 DEBUG: Start:	 Predicting
+2016-09-08 10:00:07,737 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:07,737 DEBUG: Start:	 Training
+2016-09-08 10:00:07,744 DEBUG: Done:	 Predicting
+2016-09-08 10:00:07,744 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:07,758 DEBUG: Info:	 Time for Training: 0.0734429359436[s]
+2016-09-08 10:00:07,758 DEBUG: Done:	 Training
+2016-09-08 10:00:07,758 DEBUG: Start:	 Predicting
+2016-09-08 10:00:07,762 DEBUG: Done:	 Predicting
+2016-09-08 10:00:07,762 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:07,767 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:07,768 INFO: 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
+2016-09-08 10:00:07,768 INFO: Done:	 Result Analysis
+2016-09-08 10:00:07,797 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:07,797 INFO: 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
+2016-09-08 10:00:07,797 INFO: Done:	 Result Analysis
+2016-09-08 10:00:07,935 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:07,935 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-08 10:00:07,935 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:07,935 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:07,935 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-08 10:00:07,935 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:07,936 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-08 10:00:07,936 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-08 10:00:07,936 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:07,936 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-08 10:00:07,936 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:07,936 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-08 10:00:07,936 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:07,937 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:07,984 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:07,985 DEBUG: Start:	 Training
+2016-09-08 10:00:07,990 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:07,990 DEBUG: Start:	 Training
+2016-09-08 10:00:08,003 DEBUG: Info:	 Time for Training: 0.0686159133911[s]
+2016-09-08 10:00:08,003 DEBUG: Done:	 Training
+2016-09-08 10:00:08,003 DEBUG: Start:	 Predicting
+2016-09-08 10:00:08,007 DEBUG: Info:	 Time for Training: 0.0732269287109[s]
+2016-09-08 10:00:08,007 DEBUG: Done:	 Training
+2016-09-08 10:00:08,008 DEBUG: Start:	 Predicting
+2016-09-08 10:00:08,009 DEBUG: Done:	 Predicting
+2016-09-08 10:00:08,009 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:08,012 DEBUG: Done:	 Predicting
+2016-09-08 10:00:08,012 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:08,043 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:08,043 INFO: 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
+2016-09-08 10:00:08,044 INFO: Done:	 Result Analysis
+2016-09-08 10:00:08,044 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:08,044 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, 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
+2016-09-08 10:00:08,044 INFO: Done:	 Result Analysis
+2016-09-08 10:00:08,183 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:08,183 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:08,183 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-08 10:00:08,183 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:08,183 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-08 10:00:08,183 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:08,184 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:08,184 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:08,184 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:08,184 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:08,184 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:08,184 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:08,185 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:08,185 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:08,221 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:08,221 DEBUG: Start:	 Training
+2016-09-08 10:00:08,223 DEBUG: Info:	 Time for Training: 0.0409498214722[s]
+2016-09-08 10:00:08,223 DEBUG: Done:	 Training
+2016-09-08 10:00:08,223 DEBUG: Start:	 Predicting
+2016-09-08 10:00:08,226 DEBUG: Done:	 Predicting
+2016-09-08 10:00:08,226 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:08,235 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:08,235 DEBUG: Start:	 Training
+2016-09-08 10:00:08,239 DEBUG: Info:	 Time for Training: 0.0573840141296[s]
+2016-09-08 10:00:08,240 DEBUG: Done:	 Training
+2016-09-08 10:00:08,240 DEBUG: Start:	 Predicting
+2016-09-08 10:00:08,242 DEBUG: Done:	 Predicting
+2016-09-08 10:00:08,243 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:08,267 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:08,268 INFO: 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
+2016-09-08 10:00:08,268 INFO: Done:	 Result Analysis
+2016-09-08 10:00:08,280 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:08,280 INFO: 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
+2016-09-08 10:00:08,281 INFO: Done:	 Result Analysis
+2016-09-08 10:00:08,439 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:08,439 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-08 10:00:08,439 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:08,439 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:08,439 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-08 10:00:08,440 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:08,440 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:08,440 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:08,440 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:08,441 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:08,441 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:08,441 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:08,441 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:08,441 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:08,489 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:08,489 DEBUG: Start:	 Training
+2016-09-08 10:00:08,490 DEBUG: Info:	 Time for Training: 0.0522999763489[s]
+2016-09-08 10:00:08,490 DEBUG: Done:	 Training
+2016-09-08 10:00:08,490 DEBUG: Start:	 Predicting
+2016-09-08 10:00:08,497 DEBUG: Done:	 Predicting
+2016-09-08 10:00:08,497 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:08,532 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:08,532 INFO: 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
+2016-09-08 10:00:08,532 INFO: Done:	 Result Analysis
+2016-09-08 10:00:08,857 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:08,857 DEBUG: Start:	 Training
+2016-09-08 10:00:08,918 DEBUG: Info:	 Time for Training: 0.479932069778[s]
+2016-09-08 10:00:08,918 DEBUG: Done:	 Training
+2016-09-08 10:00:08,918 DEBUG: Start:	 Predicting
+2016-09-08 10:00:08,925 DEBUG: Done:	 Predicting
+2016-09-08 10:00:08,925 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:08,961 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:08,961 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, 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
+2016-09-08 10:00:08,961 INFO: Done:	 Result Analysis
+2016-09-08 10:00:09,084 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:09,084 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-08 10:00:09,084 DEBUG: ### Main Programm for Classification MonoView
+2016-09-08 10:00:09,084 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:09,085 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-08 10:00:09,085 DEBUG: Start:	 Determine Train/Test split
+2016-09-08 10:00:09,085 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:09,086 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:09,086 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-08 10:00:09,086 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:09,086 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-08 10:00:09,086 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:09,086 DEBUG: Done:	 Determine Train/Test split
+2016-09-08 10:00:09,086 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-08 10:00:09,129 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:09,129 DEBUG: Start:	 Training
+2016-09-08 10:00:09,130 DEBUG: Info:	 Time for Training: 0.0473730564117[s]
+2016-09-08 10:00:09,130 DEBUG: Done:	 Training
+2016-09-08 10:00:09,130 DEBUG: Start:	 Predicting
+2016-09-08 10:00:09,136 DEBUG: Done:	 RandomSearch best settings
+2016-09-08 10:00:09,136 DEBUG: Start:	 Training
+2016-09-08 10:00:09,148 DEBUG: Done:	 Predicting
+2016-09-08 10:00:09,148 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:09,155 DEBUG: Info:	 Time for Training: 0.0714659690857[s]
+2016-09-08 10:00:09,155 DEBUG: Done:	 Training
+2016-09-08 10:00:09,155 DEBUG: Start:	 Predicting
+2016-09-08 10:00:09,158 DEBUG: Done:	 Predicting
+2016-09-08 10:00:09,158 DEBUG: Start:	 Getting Results
+2016-09-08 10:00:09,171 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:09,171 INFO: 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
+2016-09-08 10:00:09,171 INFO: Done:	 Result Analysis
+2016-09-08 10:00:09,189 DEBUG: Done:	 Getting Results
+2016-09-08 10:00:09,190 INFO: 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
+2016-09-08 10:00:09,190 INFO: Done:	 Result Analysis
+2016-09-08 10:00:09,488 INFO: ### Main Programm for Multiview Classification
+2016-09-08 10:00:09,488 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-08 10:00:09,489 INFO: Info:	 Shape of View0 :(300, 12)
+2016-09-08 10:00:09,490 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-08 10:00:09,491 INFO: Info:	 Shape of View2 :(300, 18)
+2016-09-08 10:00:09,492 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-08 10:00:09,493 INFO: Done:	 Read Database Files
+2016-09-08 10:00:09,493 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-08 10:00:09,496 INFO: ### Main Programm for Multiview Classification
+2016-09-08 10:00:09,497 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-08 10:00:09,498 INFO: Info:	 Shape of View0 :(300, 12)
+2016-09-08 10:00:09,498 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-08 10:00:09,499 INFO: Info:	 Shape of View2 :(300, 18)
+2016-09-08 10:00:09,500 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-08 10:00:09,500 INFO: Done:	 Read Database Files
+2016-09-08 10:00:09,500 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-08 10:00:09,502 INFO: Done:	 Determine validation split
+2016-09-08 10:00:09,502 INFO: Start:	 Determine 5 folds
+2016-09-08 10:00:09,507 INFO: Done:	 Determine validation split
+2016-09-08 10:00:09,508 INFO: Start:	 Determine 5 folds
+2016-09-08 10:00:09,515 INFO: Info:	 Length of Learning Sets: 170
+2016-09-08 10:00:09,515 INFO: Info:	 Length of Testing Sets: 41
+2016-09-08 10:00:09,516 INFO: Info:	 Length of Validation Set: 89
+2016-09-08 10:00:09,516 INFO: Done:	 Determine folds
+2016-09-08 10:00:09,516 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-08 10:00:09,516 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:09,516 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:09,519 INFO: Info:	 Length of Learning Sets: 170
+2016-09-08 10:00:09,519 INFO: Info:	 Length of Testing Sets: 41
+2016-09-08 10:00:09,519 INFO: Info:	 Length of Validation Set: 89
+2016-09-08 10:00:09,519 INFO: Done:	 Determine folds
+2016-09-08 10:00:09,519 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-08 10:00:09,520 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:09,520 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:09,608 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:09,608 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:09,610 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:09,610 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:09,693 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:09,693 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:09,696 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:09,696 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:09,780 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:09,780 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:09,783 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:09,783 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:09,866 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:09,869 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:09,946 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:09,946 INFO: Start:	 Classification
+2016-09-08 10:00:09,947 INFO: 	Start:	 Fold number 1
+2016-09-08 10:00:09,974 INFO: 	Start: 	 Classification
+2016-09-08 10:00:09,986 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:09,986 INFO: Start:	 Classification
+2016-09-08 10:00:09,986 INFO: 	Start:	 Fold number 1
+2016-09-08 10:00:10,000 INFO: 	Done: 	 Fold number 1
+2016-09-08 10:00:10,000 INFO: 	Start:	 Fold number 2
+2016-09-08 10:00:10,013 INFO: 	Start: 	 Classification
+2016-09-08 10:00:10,026 INFO: 	Start: 	 Classification
+2016-09-08 10:00:10,051 INFO: 	Done: 	 Fold number 2
+2016-09-08 10:00:10,052 INFO: 	Start:	 Fold number 3
+2016-09-08 10:00:10,078 INFO: 	Start: 	 Classification
+2016-09-08 10:00:10,083 INFO: 	Done: 	 Fold number 1
+2016-09-08 10:00:10,083 INFO: 	Start:	 Fold number 2
+2016-09-08 10:00:10,104 INFO: 	Done: 	 Fold number 3
+2016-09-08 10:00:10,104 INFO: 	Start:	 Fold number 4
+2016-09-08 10:00:10,109 INFO: 	Start: 	 Classification
+2016-09-08 10:00:10,130 INFO: 	Start: 	 Classification
+2016-09-08 10:00:10,156 INFO: 	Done: 	 Fold number 4
+2016-09-08 10:00:10,156 INFO: 	Start:	 Fold number 5
+2016-09-08 10:00:10,179 INFO: 	Done: 	 Fold number 2
+2016-09-08 10:00:10,179 INFO: 	Start:	 Fold number 3
+2016-09-08 10:00:10,182 INFO: 	Start: 	 Classification
+2016-09-08 10:00:10,206 INFO: 	Start: 	 Classification
+2016-09-08 10:00:10,208 INFO: 	Done: 	 Fold number 5
+2016-09-08 10:00:10,208 INFO: Done:	 Classification
+2016-09-08 10:00:10,208 INFO: Info:	 Time for Classification: 0[s]
+2016-09-08 10:00:10,208 INFO: Start:	 Result Analysis for Fusion
+2016-09-08 10:00:10,276 INFO: 	Done: 	 Fold number 3
+2016-09-08 10:00:10,276 INFO: 	Start:	 Fold number 4
+2016-09-08 10:00:10,304 INFO: 	Start: 	 Classification
+2016-09-08 10:00:10,339 INFO: 		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.
+
+
+2016-09-08 10:00:10,339 INFO: Done:	 Result Analysis
+2016-09-08 10:00:10,374 INFO: 	Done: 	 Fold number 4
+2016-09-08 10:00:10,374 INFO: 	Start:	 Fold number 5
+2016-09-08 10:00:10,399 INFO: 	Start: 	 Classification
+2016-09-08 10:00:10,465 INFO: 	Done: 	 Fold number 5
+2016-09-08 10:00:10,465 INFO: Done:	 Classification
+2016-09-08 10:00:10,466 INFO: Info:	 Time for Classification: 0[s]
+2016-09-08 10:00:10,466 INFO: Start:	 Result Analysis for Fusion
+2016-09-08 10:00:10,588 INFO: 		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.
+
+
+2016-09-08 10:00:10,588 INFO: Done:	 Result Analysis
+2016-09-08 10:00:10,737 INFO: ### Main Programm for Multiview Classification
+2016-09-08 10:00:10,738 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-08 10:00:10,738 INFO: Info:	 Shape of View0 :(300, 12)
+2016-09-08 10:00:10,739 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-08 10:00:10,740 INFO: Info:	 Shape of View2 :(300, 18)
+2016-09-08 10:00:10,740 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-08 10:00:10,741 INFO: Done:	 Read Database Files
+2016-09-08 10:00:10,741 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-08 10:00:10,744 INFO: ### Main Programm for Multiview Classification
+2016-09-08 10:00:10,745 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-08 10:00:10,745 INFO: Info:	 Shape of View0 :(300, 12)
+2016-09-08 10:00:10,746 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-08 10:00:10,746 INFO: Info:	 Shape of View2 :(300, 18)
+2016-09-08 10:00:10,747 INFO: Done:	 Determine validation split
+2016-09-08 10:00:10,747 INFO: Start:	 Determine 5 folds
+2016-09-08 10:00:10,747 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-08 10:00:10,747 INFO: Done:	 Read Database Files
+2016-09-08 10:00:10,747 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-08 10:00:10,752 INFO: Done:	 Determine validation split
+2016-09-08 10:00:10,752 INFO: Start:	 Determine 5 folds
+2016-09-08 10:00:10,754 INFO: Info:	 Length of Learning Sets: 170
+2016-09-08 10:00:10,754 INFO: Info:	 Length of Testing Sets: 41
+2016-09-08 10:00:10,754 INFO: Info:	 Length of Validation Set: 89
+2016-09-08 10:00:10,755 INFO: Done:	 Determine folds
+2016-09-08 10:00:10,755 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-08 10:00:10,755 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:10,755 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:10,761 INFO: Info:	 Length of Learning Sets: 170
+2016-09-08 10:00:10,761 INFO: Info:	 Length of Testing Sets: 41
+2016-09-08 10:00:10,761 INFO: Info:	 Length of Validation Set: 89
+2016-09-08 10:00:10,761 INFO: Done:	 Determine folds
+2016-09-08 10:00:10,761 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-08 10:00:10,762 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:10,762 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:10,811 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:10,811 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:10,816 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:10,816 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:10,862 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:10,862 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:10,867 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:10,867 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:10,912 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:10,913 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:10,918 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:10,918 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:10,962 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:10,962 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:10,962 INFO: Start:	 Classification
+2016-09-08 10:00:10,963 INFO: 	Start:	 Fold number 1
+2016-09-08 10:00:10,968 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:11,027 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,048 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:11,048 INFO: Start:	 Classification
+2016-09-08 10:00:11,048 INFO: 	Start:	 Fold number 1
+2016-09-08 10:00:11,057 INFO: 	Done: 	 Fold number 1
+2016-09-08 10:00:11,057 INFO: 	Start:	 Fold number 2
+2016-09-08 10:00:11,074 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,103 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,104 INFO: 	Done: 	 Fold number 1
+2016-09-08 10:00:11,105 INFO: 	Start:	 Fold number 2
+2016-09-08 10:00:11,131 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,133 INFO: 	Done: 	 Fold number 2
+2016-09-08 10:00:11,133 INFO: 	Start:	 Fold number 3
+2016-09-08 10:00:11,162 INFO: 	Done: 	 Fold number 2
+2016-09-08 10:00:11,162 INFO: 	Start:	 Fold number 3
+2016-09-08 10:00:11,180 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,189 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,210 INFO: 	Done: 	 Fold number 3
+2016-09-08 10:00:11,210 INFO: 	Start:	 Fold number 4
+2016-09-08 10:00:11,220 INFO: 	Done: 	 Fold number 3
+2016-09-08 10:00:11,220 INFO: 	Start:	 Fold number 4
+2016-09-08 10:00:11,247 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,255 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,277 INFO: 	Done: 	 Fold number 4
+2016-09-08 10:00:11,277 INFO: 	Start:	 Fold number 5
+2016-09-08 10:00:11,285 INFO: 	Done: 	 Fold number 4
+2016-09-08 10:00:11,286 INFO: 	Start:	 Fold number 5
+2016-09-08 10:00:11,304 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,331 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,335 INFO: 	Done: 	 Fold number 5
+2016-09-08 10:00:11,335 INFO: Done:	 Classification
+2016-09-08 10:00:11,335 INFO: Info:	 Time for Classification: 0[s]
+2016-09-08 10:00:11,335 INFO: Start:	 Result Analysis for Fusion
+2016-09-08 10:00:11,361 INFO: 	Done: 	 Fold number 5
+2016-09-08 10:00:11,362 INFO: Done:	 Classification
+2016-09-08 10:00:11,362 INFO: Info:	 Time for Classification: 0[s]
+2016-09-08 10:00:11,362 INFO: Start:	 Result Analysis for Fusion
+2016-09-08 10:00:11,472 INFO: 		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.
+
+
+2016-09-08 10:00:11,473 INFO: Done:	 Result Analysis
+2016-09-08 10:00:11,495 INFO: 		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.
+
+
+2016-09-08 10:00:11,495 INFO: Done:	 Result Analysis
+2016-09-08 10:00:11,576 INFO: ### Main Programm for Multiview Classification
+2016-09-08 10:00:11,576 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-08 10:00:11,577 INFO: ### Main Programm for Multiview Classification
+2016-09-08 10:00:11,577 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-08 10:00:11,578 INFO: Info:	 Shape of View0 :(300, 12)
+2016-09-08 10:00:11,578 INFO: Info:	 Shape of View0 :(300, 12)
+2016-09-08 10:00:11,578 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-08 10:00:11,578 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-08 10:00:11,579 INFO: Info:	 Shape of View2 :(300, 18)
+2016-09-08 10:00:11,579 INFO: Info:	 Shape of View2 :(300, 18)
+2016-09-08 10:00:11,580 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-08 10:00:11,580 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-08 10:00:11,580 INFO: Done:	 Read Database Files
+2016-09-08 10:00:11,580 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-08 10:00:11,580 INFO: Done:	 Read Database Files
+2016-09-08 10:00:11,580 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-08 10:00:11,586 INFO: Done:	 Determine validation split
+2016-09-08 10:00:11,586 INFO: Done:	 Determine validation split
+2016-09-08 10:00:11,586 INFO: Start:	 Determine 5 folds
+2016-09-08 10:00:11,586 INFO: Start:	 Determine 5 folds
+2016-09-08 10:00:11,596 INFO: Info:	 Length of Learning Sets: 170
+2016-09-08 10:00:11,596 INFO: Info:	 Length of Testing Sets: 41
+2016-09-08 10:00:11,596 INFO: Info:	 Length of Validation Set: 89
+2016-09-08 10:00:11,596 INFO: Done:	 Determine folds
+2016-09-08 10:00:11,596 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-08 10:00:11,596 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:11,597 DEBUG: 	Start:	 Random search for Adaboost with 1 iterations
+2016-09-08 10:00:11,597 INFO: Info:	 Length of Learning Sets: 170
+2016-09-08 10:00:11,597 INFO: Info:	 Length of Testing Sets: 41
+2016-09-08 10:00:11,598 INFO: Info:	 Length of Validation Set: 89
+2016-09-08 10:00:11,598 INFO: Done:	 Determine folds
+2016-09-08 10:00:11,598 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-08 10:00:11,598 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:11,598 DEBUG: 	Start:	 Random search for DecisionTree with 1 iterations
+2016-09-08 10:00:11,664 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-08 10:00:11,752 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:11,752 INFO: Start:	 Classification
+2016-09-08 10:00:11,752 INFO: 	Start:	 Fold number 1
+2016-09-08 10:00:11,788 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,830 INFO: 	Done: 	 Fold number 1
+2016-09-08 10:00:11,831 INFO: 	Start:	 Fold number 2
+2016-09-08 10:00:11,864 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,867 DEBUG: 	Done:	 Random search for Adaboost
+2016-09-08 10:00:11,906 INFO: 	Done: 	 Fold number 2
+2016-09-08 10:00:11,906 INFO: 	Start:	 Fold number 3
+2016-09-08 10:00:11,940 INFO: 	Start: 	 Classification
+2016-09-08 10:00:11,944 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:11,944 INFO: Start:	 Classification
+2016-09-08 10:00:11,944 INFO: 	Start:	 Fold number 1
+2016-09-08 10:00:11,980 INFO: 	Done: 	 Fold number 3
+2016-09-08 10:00:11,980 INFO: 	Start:	 Fold number 4
+2016-09-08 10:00:11,984 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,015 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,026 INFO: 	Done: 	 Fold number 1
+2016-09-08 10:00:12,026 INFO: 	Start:	 Fold number 2
+2016-09-08 10:00:12,056 INFO: 	Done: 	 Fold number 4
+2016-09-08 10:00:12,057 INFO: 	Start:	 Fold number 5
+2016-09-08 10:00:12,066 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,091 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,108 INFO: 	Done: 	 Fold number 2
+2016-09-08 10:00:12,108 INFO: 	Start:	 Fold number 3
+2016-09-08 10:00:12,133 INFO: 	Done: 	 Fold number 5
+2016-09-08 10:00:12,133 INFO: Done:	 Classification
+2016-09-08 10:00:12,133 INFO: Info:	 Time for Classification: 0[s]
+2016-09-08 10:00:12,133 INFO: Start:	 Result Analysis for Fusion
+2016-09-08 10:00:12,147 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,192 INFO: 	Done: 	 Fold number 3
+2016-09-08 10:00:12,192 INFO: 	Start:	 Fold number 4
+2016-09-08 10:00:12,232 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,263 INFO: 	Done: 	 Fold number 4
+2016-09-08 10:00:12,263 INFO: 	Start:	 Fold number 5
+2016-09-08 10:00:12,307 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,350 INFO: 	Done: 	 Fold number 5
+2016-09-08 10:00:12,351 INFO: Done:	 Classification
+2016-09-08 10:00:12,351 INFO: Info:	 Time for Classification: 0[s]
+2016-09-08 10:00:12,351 INFO: Start:	 Result Analysis for Fusion
+2016-09-08 10:00:12,358 INFO: 		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.
+
+
+2016-09-08 10:00:12,358 INFO: Done:	 Result Analysis
+2016-09-08 10:00:12,486 INFO: 		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.
+
+
+2016-09-08 10:00:12,486 INFO: Done:	 Result Analysis
+2016-09-08 10:00:12,629 INFO: ### Main Programm for Multiview Classification
+2016-09-08 10:00:12,629 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-08 10:00:12,629 INFO: ### Main Programm for Multiview Classification
+2016-09-08 10:00:12,630 INFO: Info:	 Shape of View0 :(300, 12)
+2016-09-08 10:00:12,630 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-08 10:00:12,630 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-08 10:00:12,630 INFO: Info:	 Shape of View0 :(300, 12)
+2016-09-08 10:00:12,631 INFO: Info:	 Shape of View2 :(300, 18)
+2016-09-08 10:00:12,631 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-08 10:00:12,631 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-08 10:00:12,631 INFO: Done:	 Read Database Files
+2016-09-08 10:00:12,631 INFO: Info:	 Shape of View2 :(300, 18)
+2016-09-08 10:00:12,631 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-08 10:00:12,632 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-08 10:00:12,632 INFO: Done:	 Read Database Files
+2016-09-08 10:00:12,632 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-08 10:00:12,636 INFO: Done:	 Determine validation split
+2016-09-08 10:00:12,636 INFO: Start:	 Determine 5 folds
+2016-09-08 10:00:12,637 INFO: Done:	 Determine validation split
+2016-09-08 10:00:12,637 INFO: Start:	 Determine 5 folds
+2016-09-08 10:00:12,643 INFO: Info:	 Length of Learning Sets: 170
+2016-09-08 10:00:12,643 INFO: Info:	 Length of Testing Sets: 41
+2016-09-08 10:00:12,643 INFO: Info:	 Length of Validation Set: 89
+2016-09-08 10:00:12,643 INFO: Done:	 Determine folds
+2016-09-08 10:00:12,643 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-08 10:00:12,643 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:12,643 DEBUG: 	Start:	 Random search for KNN with 1 iterations
+2016-09-08 10:00:12,644 INFO: Info:	 Length of Learning Sets: 170
+2016-09-08 10:00:12,644 INFO: Info:	 Length of Testing Sets: 41
+2016-09-08 10:00:12,644 INFO: Info:	 Length of Validation Set: 89
+2016-09-08 10:00:12,644 INFO: Done:	 Determine folds
+2016-09-08 10:00:12,644 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-08 10:00:12,644 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:12,644 DEBUG: 	Start:	 Random search for RandomForest with 1 iterations
+2016-09-08 10:00:12,692 DEBUG: 	Done:	 Random search for KNN
+2016-09-08 10:00:12,706 DEBUG: 	Done:	 Random search for RandomForest
+2016-09-08 10:00:12,736 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:12,736 INFO: Start:	 Classification
+2016-09-08 10:00:12,736 INFO: 	Start:	 Fold number 1
+2016-09-08 10:00:12,747 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:12,747 INFO: Start:	 Classification
+2016-09-08 10:00:12,747 INFO: 	Start:	 Fold number 1
+2016-09-08 10:00:12,754 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,768 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,786 INFO: 	Done: 	 Fold number 1
+2016-09-08 10:00:12,786 INFO: 	Start:	 Fold number 2
+2016-09-08 10:00:12,794 INFO: 	Done: 	 Fold number 1
+2016-09-08 10:00:12,794 INFO: 	Start:	 Fold number 2
+2016-09-08 10:00:12,803 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,814 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,836 INFO: 	Done: 	 Fold number 2
+2016-09-08 10:00:12,836 INFO: 	Start:	 Fold number 3
+2016-09-08 10:00:12,839 INFO: 	Done: 	 Fold number 2
+2016-09-08 10:00:12,839 INFO: 	Start:	 Fold number 3
+2016-09-08 10:00:12,853 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,859 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,884 INFO: 	Done: 	 Fold number 3
+2016-09-08 10:00:12,884 INFO: 	Start:	 Fold number 4
+2016-09-08 10:00:12,886 INFO: 	Done: 	 Fold number 3
+2016-09-08 10:00:12,886 INFO: 	Start:	 Fold number 4
+2016-09-08 10:00:12,903 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,903 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,928 INFO: 	Done: 	 Fold number 4
+2016-09-08 10:00:12,928 INFO: 	Start:	 Fold number 5
+2016-09-08 10:00:12,935 INFO: 	Done: 	 Fold number 4
+2016-09-08 10:00:12,935 INFO: 	Start:	 Fold number 5
+2016-09-08 10:00:12,948 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,952 INFO: 	Start: 	 Classification
+2016-09-08 10:00:12,974 INFO: 	Done: 	 Fold number 5
+2016-09-08 10:00:12,974 INFO: Done:	 Classification
+2016-09-08 10:00:12,974 INFO: Info:	 Time for Classification: 0[s]
+2016-09-08 10:00:12,974 INFO: Start:	 Result Analysis for Fusion
+2016-09-08 10:00:12,985 INFO: 	Done: 	 Fold number 5
+2016-09-08 10:00:12,985 INFO: Done:	 Classification
+2016-09-08 10:00:12,985 INFO: Info:	 Time for Classification: 0[s]
+2016-09-08 10:00:12,985 INFO: Start:	 Result Analysis for Fusion
+2016-09-08 10:00:13,124 INFO: 		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.
+
+
+2016-09-08 10:00:13,124 INFO: Done:	 Result Analysis
+2016-09-08 10:00:13,128 INFO: 		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.
+
+
+2016-09-08 10:00:13,128 INFO: Done:	 Result Analysis
+2016-09-08 10:00:13,279 INFO: ### Main Programm for Multiview Classification
+2016-09-08 10:00:13,279 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-08 10:00:13,279 INFO: ### Main Programm for Multiview Classification
+2016-09-08 10:00:13,280 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-08 10:00:13,280 INFO: Info:	 Shape of View0 :(300, 12)
+2016-09-08 10:00:13,280 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-08 10:00:13,280 INFO: Info:	 Shape of View0 :(300, 12)
+2016-09-08 10:00:13,281 INFO: Info:	 Shape of View2 :(300, 18)
+2016-09-08 10:00:13,281 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-08 10:00:13,281 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-08 10:00:13,281 INFO: Info:	 Shape of View2 :(300, 18)
+2016-09-08 10:00:13,282 INFO: Done:	 Read Database Files
+2016-09-08 10:00:13,282 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-08 10:00:13,282 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-08 10:00:13,282 INFO: Done:	 Read Database Files
+2016-09-08 10:00:13,282 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-08 10:00:13,287 INFO: Done:	 Determine validation split
+2016-09-08 10:00:13,287 INFO: Start:	 Determine 5 folds
+2016-09-08 10:00:13,287 INFO: Done:	 Determine validation split
+2016-09-08 10:00:13,287 INFO: Start:	 Determine 5 folds
+2016-09-08 10:00:13,294 INFO: Info:	 Length of Learning Sets: 170
+2016-09-08 10:00:13,294 INFO: Info:	 Length of Testing Sets: 41
+2016-09-08 10:00:13,294 INFO: Info:	 Length of Validation Set: 89
+2016-09-08 10:00:13,294 INFO: Done:	 Determine folds
+2016-09-08 10:00:13,294 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-08 10:00:13,294 INFO: Info:	 Length of Learning Sets: 170
+2016-09-08 10:00:13,294 INFO: Info:	 Length of Testing Sets: 41
+2016-09-08 10:00:13,294 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:13,294 INFO: Info:	 Length of Validation Set: 89
+2016-09-08 10:00:13,294 DEBUG: 	Start:	 Random search for SGD with 1 iterations
+2016-09-08 10:00:13,294 INFO: Done:	 Determine folds
+2016-09-08 10:00:13,295 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-08 10:00:13,295 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:13,295 DEBUG: 	Start:	 Random search for SVMLinear with 1 iterations
+2016-09-08 10:00:13,348 DEBUG: 	Done:	 Random search for SVMLinear
+2016-09-08 10:00:13,351 DEBUG: 	Done:	 Random search for SGD
+2016-09-08 10:00:13,416 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:13,416 INFO: Start:	 Classification
+2016-09-08 10:00:13,416 INFO: 	Start:	 Fold number 1
+2016-09-08 10:00:13,430 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-08 10:00:13,430 INFO: Start:	 Classification
+2016-09-08 10:00:13,430 INFO: 	Start:	 Fold number 1
+2016-09-08 10:00:13,456 INFO: 	Start: 	 Classification
+2016-09-08 10:00:13,456 INFO: 	Start: 	 Classification
+2016-09-08 10:00:13,485 INFO: 	Done: 	 Fold number 1
+2016-09-08 10:00:13,485 INFO: 	Start:	 Fold number 2
+2016-09-08 10:00:13,492 INFO: 	Done: 	 Fold number 1
+2016-09-08 10:00:13,492 INFO: 	Start:	 Fold number 2
+2016-09-08 10:00:13,518 INFO: 	Start: 	 Classification
+2016-09-08 10:00:13,526 INFO: 	Start: 	 Classification
+2016-09-08 10:00:13,555 INFO: 	Done: 	 Fold number 2
+2016-09-08 10:00:13,555 INFO: 	Start:	 Fold number 3
+2016-09-08 10:00:13,556 INFO: 	Done: 	 Fold number 2
+2016-09-08 10:00:13,556 INFO: 	Start:	 Fold number 3
+2016-09-08 10:00:13,581 INFO: 	Start: 	 Classification
+2016-09-08 10:00:13,598 INFO: 	Start: 	 Classification
+2016-09-08 10:00:13,617 INFO: 	Done: 	 Fold number 3
+2016-09-08 10:00:13,617 INFO: 	Start:	 Fold number 4
+2016-09-08 10:00:13,627 INFO: 	Done: 	 Fold number 3
+2016-09-08 10:00:13,627 INFO: 	Start:	 Fold number 4
+2016-09-08 10:00:13,644 INFO: 	Start: 	 Classification
+2016-09-08 10:00:13,668 INFO: 	Start: 	 Classification
+2016-09-08 10:00:13,682 INFO: 	Done: 	 Fold number 4
+2016-09-08 10:00:13,682 INFO: 	Start:	 Fold number 5
+2016-09-08 10:00:13,698 INFO: 	Done: 	 Fold number 4
+2016-09-08 10:00:13,698 INFO: 	Start:	 Fold number 5
+2016-09-08 10:00:13,709 INFO: 	Start: 	 Classification
+2016-09-08 10:00:13,739 INFO: 	Start: 	 Classification
+2016-09-08 10:00:13,747 INFO: 	Done: 	 Fold number 5
+2016-09-08 10:00:13,747 INFO: Done:	 Classification
+2016-09-08 10:00:13,747 INFO: Info:	 Time for Classification: 0[s]
+2016-09-08 10:00:13,747 INFO: Start:	 Result Analysis for Fusion
+2016-09-08 10:00:13,769 INFO: 	Done: 	 Fold number 5
+2016-09-08 10:00:13,769 INFO: Done:	 Classification
+2016-09-08 10:00:13,770 INFO: Info:	 Time for Classification: 0[s]
+2016-09-08 10:00:13,770 INFO: Start:	 Result Analysis for Fusion
+2016-09-08 10:00:13,916 INFO: 		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.
+
+
+2016-09-08 10:00:13,916 INFO: Done:	 Result Analysis
+2016-09-08 10:00:13,962 INFO: 		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.
+
+
+2016-09-08 10:00:13,962 INFO: Done:	 Result Analysis
+2016-09-08 10:00:14,116 DEBUG: Start:	 Deleting 2 temporary datasets for multiprocessing
+2016-09-08 10:00:14,116 DEBUG: Start:	 Deleting datasets for multiprocessing
+2016-09-08 10:00:46,421 INFO: Extraction time : 5.03609514236s, Monoview time : 1473343204.39s, Multiview Time : 9.72813415527s
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
new file mode 100644
index 0000000000000000000000000000000000000000..c10750d44ceac649cbe0a6d590287c9f5c97238b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..ba0382dbd9355b910467e7c3ea45cf08fcbdcc56
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..fc54b504882519b574c965ecf4d8eedeef1cc3aa
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..b24a64e65016e9e0263c359295f6bef2f1a42a50
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..9d76693a39a94571bc41ea125ba5b364719e7777
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..867521e73e098d35580ec0f95af5982d09e98991
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..589edf906844f420e689c2580b484c971d25c7bb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..1881529c6a507787f96a3c34d3eae90866b6286f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..cbbd1a0d3ac70db81be73d8fe7ce021f46f832ab
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..23cf02f75457800ec7e365c2a0e5f522c0a89edf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..d276635c447b2d4b0302a0207ec8ee2adf70e5cb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..caa7c9681cce2811ed8c3da610ba1810d410339e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..836977b7ee31de2f3257684bc8347ee0eacbd0e2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..4dbb4c25550cab8b428692b93e320234b1a075c9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..d66ec8a4ffb7a89486e0947c4e3dbb3653ab71b9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..3b75a933e277742b98aa3bc5f78a4a226c873602
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..d128db52bc2a7a322700fc4fe91f03eff1d6eb7f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..d2a21462829ea28a11dcac00d479a150f15505c0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..d9a3f5a3e6c9f9314127026a077a825115efd21d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..8e7a8c2dac3a63ea72ac1a1d2b1f00e6057b3021
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..9ee2ef1d9e95ac7aee25aa51fcbfbce5d49d2e57
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..d16950a5645f14abfffcd24404af4b15fb8acd2c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..1fadae0e8031c1efee27d914a774f3215f055b8e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,57 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..ade21df13c32fc925324446cc5c0f45faebc650b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..ce39d2332c0af66ff80f32cb189f674d2518b6f0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..a6d23daee024b72d994eff7b8664df8932567014
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..53f3a855a561b6e9c7a74c9197c38666faa2a1be
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..bf98d86d42889b815483d5075d682abc85a87071
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..04d9aee02671ca751ba32e0fde801c90fc433b58
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..d52376ecd580c0741a63b611a9a5411bd55bef66
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+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
new file mode 100644
index 0000000000000000000000000000000000000000..31c5952590b71df2f7b9326a2fed03e501181cfa
--- /dev/null
+++ 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
@@ -0,0 +1,32 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..4d7b96cd623963b3db009f942a91b9ba9c0ae0ea
--- /dev/null
+++ 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
@@ -0,0 +1,32 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..92e2e7eb2640f7c3de43250d3304533afdc9196b
--- /dev/null
+++ 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
@@ -0,0 +1,32 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..9035751820a94da279bbd7183290a6cbe0201fb6
--- /dev/null
+++ 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
@@ -0,0 +1,32 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..3ebeedf2c579860400c701b722f7662e73fd3d20
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,31 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..007add077fe4b19b82933921162236df028dcfe6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,28 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..d10e58096a314c7404284f1d9762253358d9e56a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,28 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..3109c44cf165ee1ea89651472f5ebd130af9afd5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,28 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..e63a7f8163f3366b568c1f02b93096e9fba2ed76
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,28 @@
+		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
new file mode 100644
index 0000000000000000000000000000000000000000..6c39250e0e355ae42944f0ee6289bcb576f2e11d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,28 @@
+		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/Results/Fake-accuracy_score-20160908-100014.png b/Code/MonoMutliViewClassifiers/Results/Fake-accuracy_score-20160908-100014.png
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diff --git a/Code/MonoMutliViewClassifiers/Results/Fake-jaccard_similarity_score-20160908-100026.png b/Code/MonoMutliViewClassifiers/Results/Fake-jaccard_similarity_score-20160908-100026.png
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diff --git a/Code/MonoMutliViewClassifiers/Results/Fake-log_loss-20160908-100029.png b/Code/MonoMutliViewClassifiers/Results/Fake-log_loss-20160908-100029.png
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