diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index 78df72e5a9548786698a24ae44ad08115681ed61..34ab48ffc5028b79c395c61e861dbcda9730f4b4 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -329,6 +329,8 @@ try:
                     argumentDictionaries["Multiview"].append(arguments)
         except:
             pass
+#         bestClassifiers = ["DecisionTree", "DecisionTree", "DecisionTree", "DecisionTree"]
+
         try:
             if benchmark["Multiview"]["Fusion"]:
                 try:
@@ -386,7 +388,8 @@ if nbCores>1:
     logging.debug("Start:\t Deleting datasets for multiprocessing")
 
 times = [dataBaseTime, monoviewTime, multiviewTime]
+# times=[]
 results = (resultsMonoview, resultsMultiview)
-resultAnalysis(benchmark, results, args.name)
+resultAnalysis(benchmark, results, args.name, times, metrics)
 
 
diff --git a/Code/MonoMutliViewClassifiers/Metrics/roc_auc_score.py b/Code/MonoMutliViewClassifiers/Metrics/roc_auc_score.py
index c7f1276cfa70c6ea74387567df23e1ec7b24f295..7beda9f4ea6d744b633a6a4de275bbe580039055 100644
--- a/Code/MonoMutliViewClassifiers/Metrics/roc_auc_score.py
+++ b/Code/MonoMutliViewClassifiers/Metrics/roc_auc_score.py
@@ -40,5 +40,5 @@ def getConfig(**kwargs):
         average = kwargs["3"]
     except:
         average = "micro"
-    configString = "ROS AUC score using "+str(sample_weight)+" as sample_weights, "+average+" as average (higher is better)"
+    configString = "ROC AUC score using "+str(sample_weight)+" as sample_weights, "+average+" as average (higher is better)"
     return configString
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
index 3b2f8dff691fa92d51a7f2e6e9e99b42601cfb9b..89905437318f390c485a84bc030520fefe2fed7d 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
@@ -109,7 +109,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
             logging.info("\tStart:\t Fold number " + str(foldIdx + 1))
             trainIndices = [index for index in range(datasetLength) if (index not in fold) and (index not in validationIndices)]
             DATASET_LENGTH = len(trainIndices)
-            classifier = classifierClass(NB_VIEW, DATASET_LENGTH, DATASET.get("labels").value, NB_CORES=nbCores, **classificationKWARGS)
+            classifier = classifierClass(NB_VIEW, DATASET_LENGTH, DATASET.get("labels").value[trainIndices], NB_CORES=nbCores, **classificationKWARGS)
 
             classifier.fit_hdf5(DATASET, trainIndices=trainIndices)
             kFoldClassifier.append(classifier)
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
index 4f1e43a4b22227e90f43ff596a989b9ef92d25aa..dc4360cc563ad7533fb611c6f363cdfc9d9322d2 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
@@ -44,7 +44,6 @@ def gridSearch_hdf5(DATASET, classificationKWARGS, learningIndices, metric=None,
                                                  nIter=nIter))
         logging.debug("\tDone:\t Random search for "+classifierName)
     classificationKWARGS["classifiersConfigs"] = bestSettings
-    print bestSettings
     fusionMethodConfig = fusionMethodModule.gridSearch(DATASET, classificationKWARGS, learningIndices, nIter=nIter)
     return bestSettings, fusionMethodConfig
 
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
index 79d9f9b3be7e8b9bd2325307d70422285b2bae1b..557b408adf88898190f71e293de3c6decb530674 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
@@ -15,11 +15,11 @@ __author__ 	= "Baptiste Bauvin"
 __status__ 	= "Prototype"                           # Production, Development, Prototype
 
 
-
 def error(testLabels, computedLabels):
     error = sum(map(operator.ne, computedLabels, testLabels))
     return float(error) * 100 / len(computedLabels)
 
+
 def getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred):
     metricModule = getattr(Metrics, metric[0])
     if metric[1]!=None:
@@ -32,6 +32,7 @@ def getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred):
     metricScoreString += "\n"
     return metricScoreString
 
+
 def getTotalMetricScores(metric, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
                          kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds):
     labels = DATASET.get("labels").value
@@ -40,7 +41,7 @@ def getTotalMetricScores(metric, kFoldPredictedTrainLabels, kFoldPredictedTestLa
         metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
     else:
         metricKWARGS = {}
-    trainScore = np.mean(np.array([metricModule.score([label for index, label in enumerate(labels) if index not in fold+validationIndices], predictedLabels, **metricKWARGS) for fold, predictedLabels in zip(kFolds, kFoldPredictedTrainLabels)]))
+    trainScore = np.mean(np.array([metricModule.score([label for index, label in enumerate(labels) if (index not in fold) and (index not in validationIndices)], predictedLabels, **metricKWARGS) for fold, predictedLabels in zip(kFolds, kFoldPredictedTrainLabels)]))
     testScore = np.mean(np.array([metricModule.score(labels[fold], predictedLabels, **metricKWARGS) for fold, predictedLabels in zip(kFolds, kFoldPredictedTestLabels)]))
     validationScore = np.mean(np.array([metricModule.score(labels[validationIndices], predictedLabels, **metricKWARGS) for predictedLabels in kFoldPredictedValidationLabels]))
     return [trainScore, testScore, validationScore]
@@ -55,9 +56,6 @@ def getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabel
     return metricsScores
 
 
-
-
-
 def execute(kFoldClassifier, kFoldPredictedTrainLabels,
             kFoldPredictedTestLabels, kFoldPredictedValidationLabels,
             DATASET, classificationKWARGS, learningRate, LABELS_DICTIONARY,
@@ -72,14 +70,14 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels,
     monoviewClassifiersConfigs = classificationKWARGS["classifiersConfigs"]
     fusionMethodConfig = classificationKWARGS["fusionMethodConfig"]
 
-    DATASET_LENGTH = DATASET.get("Metadata").attrs["datasetLength"]-len(validationIndices)
+    DATASET_LENGTH = DATASET.get("Metadata").attrs["datasetLength"]
     NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
     kFoldAccuracyOnTrain = []
     kFoldAccuracyOnTest = []
     kFoldAccuracyOnValidation = []
     for foldIdx, fold in enumerate(kFolds):
         if fold != range(DATASET_LENGTH):
-            trainIndices = [index for index in range(DATASET_LENGTH) if index not in fold]
+            trainIndices = [index for index in range(DATASET_LENGTH) if (index not in fold) and (index not in validationIndices)]
             testLabels = CLASS_LABELS[fold]
             trainLabels = CLASS_LABELS[trainIndices]
             validationLabels = CLASS_LABELS[validationIndices]
diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index 032eee4fb9b8ff998b6e785b58b7b4e851df313f..ff02817fe307d69623975821d7f52d1e4398a75b 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -8,6 +8,9 @@ import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 
+#Import own Modules
+import Metrics
+
 # Author-Info
 __author__ 	= "Baptiste Bauvin"
 __status__ 	= "Prototype"                           # Production, Development, Prototype
@@ -25,7 +28,11 @@ def resultAnalysis(benchmark, results, name, times, metrics):
         fig = plt.gcf()
         fig.subplots_adjust(bottom=105.0, top=105.01)
         ax = f.add_axes([0.1, 0.1, 0.8, 0.8])
-        ax.set_title(metric[0]+" on validation set for each classifier")
+        if metric[1]!=None:
+            metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+        else:
+            metricKWARGS = {}
+        ax.set_title(getattr(Metrics, metric[0]).getConfig(**metricKWARGS)+" on validation set for each classifier")
         ax.bar(range(nbResults), validationScores, align='center')
         ax.set_xticks(range(nbResults))
         ax.set_xticklabels(names, rotation="vertical")
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112150-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-112150-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..5f0724c6443589f2e3804127a17e4a946a479810
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112150-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,2150 @@
+2016-09-06 11:21:50,451 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 11:21:50,452 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000124 Gbytes /!\ 
+2016-09-06 11:21:55,466 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 11:21:55,470 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 11:21:55,524 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:55,525 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 11:21:55,525 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:55,525 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:55,525 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 11:21:55,525 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:55,526 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 11:21:55,526 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 11:21:55,526 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:55,526 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 11:21:55,526 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:55,526 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 11:21:55,526 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:55,526 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:55,561 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:55,561 DEBUG: Start:	 Training
+2016-09-06 11:21:55,563 DEBUG: Info:	 Time for Training: 0.0388250350952[s]
+2016-09-06 11:21:55,563 DEBUG: Done:	 Training
+2016-09-06 11:21:55,563 DEBUG: Start:	 Predicting
+2016-09-06 11:21:55,565 DEBUG: Done:	 Predicting
+2016-09-06 11:21:55,565 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:55,575 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:55,575 DEBUG: Start:	 Training
+2016-09-06 11:21:55,579 DEBUG: Info:	 Time for Training: 0.0551791191101[s]
+2016-09-06 11:21:55,579 DEBUG: Done:	 Training
+2016-09-06 11:21:55,579 DEBUG: Start:	 Predicting
+2016-09-06 11:21:55,582 DEBUG: Done:	 Predicting
+2016-09-06 11:21:55,582 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:55,604 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:55,605 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.413793103448
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.413793103448
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.566666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.131912640639
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.391304347826
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.439024390244
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433797909408
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:55,605 INFO: Done:	 Result Analysis
+2016-09-06 11:21:55,619 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:55,619 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.494382022472
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.494382022472
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.5
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.00596274193664
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.458333333333
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.536585365854
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.502986560478
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:55,619 INFO: Done:	 Result Analysis
+2016-09-06 11:21:55,774 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:55,774 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:55,774 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 11:21:55,774 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 11:21:55,774 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:55,774 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:55,774 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 11:21:55,774 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 11:21:55,775 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 11:21:55,775 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 11:21:55,775 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:55,775 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:55,775 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:55,775 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:55,806 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:55,806 DEBUG: Start:	 Training
+2016-09-06 11:21:55,807 DEBUG: Info:	 Time for Training: 0.0337290763855[s]
+2016-09-06 11:21:55,807 DEBUG: Done:	 Training
+2016-09-06 11:21:55,807 DEBUG: Start:	 Predicting
+2016-09-06 11:21:55,814 DEBUG: Done:	 Predicting
+2016-09-06 11:21:55,814 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:55,852 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:55,852 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 47
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.422222222222
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.550724637681
+		- Score on test : 0.315789473684
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.550724637681
+		- Score on test : 0.315789473684
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.442857142857
+		- Score on test : 0.577777777778
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.422222222222
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.11473701202
+		- Score on test : -0.180519041032
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.564356435644
+		- Score on test : 0.342857142857
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.537735849057
+		- Score on test : 0.292682926829
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.55732946299
+		- Score on test : 0.411647585864
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.442857142857
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:55,852 INFO: Done:	 Result Analysis
+2016-09-06 11:21:55,945 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:55,946 DEBUG: Start:	 Training
+2016-09-06 11:21:55,964 DEBUG: Info:	 Time for Training: 0.190694093704[s]
+2016-09-06 11:21:55,964 DEBUG: Done:	 Training
+2016-09-06 11:21:55,964 DEBUG: Start:	 Predicting
+2016-09-06 11:21:55,968 DEBUG: Done:	 Predicting
+2016-09-06 11:21:55,968 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:55,996 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:55,996 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.957142857143
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.957142857143
+		- Score on test : 0.4
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.956937799043
+		- Score on test : 0.325
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.956937799043
+		- Score on test : 0.325
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0428571428571
+		- Score on test : 0.6
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.957142857143
+		- Score on test : 0.4
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.914674537841
+		- Score on test : -0.214609988978
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.970873786408
+		- Score on test : 0.333333333333
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.943396226415
+		- Score on test : 0.317073170732
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.957275036284
+		- Score on test : 0.393230462917
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0428571428571
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:55,997 INFO: Done:	 Result Analysis
+2016-09-06 11:21:56,124 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:56,125 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 11:21:56,125 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:56,125 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:56,125 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 11:21:56,125 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:56,125 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 11:21:56,125 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 11:21:56,126 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 11:21:56,126 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:56,126 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 11:21:56,126 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:56,126 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:56,126 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:56,173 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:56,173 DEBUG: Start:	 Training
+2016-09-06 11:21:56,174 DEBUG: Info:	 Time for Training: 0.0500919818878[s]
+2016-09-06 11:21:56,174 DEBUG: Done:	 Training
+2016-09-06 11:21:56,174 DEBUG: Start:	 Predicting
+2016-09-06 11:21:56,181 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:56,181 DEBUG: Start:	 Training
+2016-09-06 11:21:56,185 DEBUG: Done:	 Predicting
+2016-09-06 11:21:56,185 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:56,202 DEBUG: Info:	 Time for Training: 0.0777740478516[s]
+2016-09-06 11:21:56,202 DEBUG: Done:	 Training
+2016-09-06 11:21:56,202 DEBUG: Start:	 Predicting
+2016-09-06 11:21:56,206 DEBUG: Done:	 Predicting
+2016-09-06 11:21:56,206 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:56,213 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:56,213 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.604761904762
+		- Score on test : 0.433333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.62443438914
+		- Score on test : 0.43956043956
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.62443438914
+		- Score on test : 0.43956043956
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.395238095238
+		- Score on test : 0.566666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.604761904762
+		- Score on test : 0.433333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.209578877963
+		- Score on test : -0.124719695673
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.6
+		- Score on test : 0.4
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.650943396226
+		- Score on test : 0.487804878049
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.604317851959
+		- Score on test : 0.437779990045
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.395238095238
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:56,214 INFO: Done:	 Result Analysis
+2016-09-06 11:21:56,250 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:56,251 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.504672897196
+		- Score on test : 0.404761904762
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.504672897196
+		- Score on test : 0.404761904762
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.504761904762
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : -0.00980036362201
+		- Score on test : -0.115633266975
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.395348837209
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.509433962264
+		- Score on test : 0.414634146341
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.495101596517
+		- Score on test : 0.442010950722
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.504761904762
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:56,251 INFO: Done:	 Result Analysis
+2016-09-06 11:21:56,375 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:56,375 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:56,376 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 11:21:56,376 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 11:21:56,376 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:56,376 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:56,377 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 11:21:56,377 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 11:21:56,377 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 11:21:56,377 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 11:21:56,377 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:56,377 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:56,377 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:56,377 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:56,447 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:56,448 DEBUG: Start:	 Training
+2016-09-06 11:21:56,458 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:56,458 DEBUG: Start:	 Training
+2016-09-06 11:21:56,471 DEBUG: Info:	 Time for Training: 0.0959920883179[s]
+2016-09-06 11:21:56,471 DEBUG: Done:	 Training
+2016-09-06 11:21:56,471 DEBUG: Start:	 Predicting
+2016-09-06 11:21:56,479 DEBUG: Done:	 Predicting
+2016-09-06 11:21:56,479 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:56,482 DEBUG: Info:	 Time for Training: 0.107470989227[s]
+2016-09-06 11:21:56,482 DEBUG: Done:	 Training
+2016-09-06 11:21:56,483 DEBUG: Start:	 Predicting
+2016-09-06 11:21:56,488 DEBUG: Done:	 Predicting
+2016-09-06 11:21:56,488 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:56,517 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:56,517 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.345679012346
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.345679012346
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.588888888889
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.189573937423
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.35
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.341463414634
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.405425584868
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:56,518 INFO: Done:	 Result Analysis
+2016-09-06 11:21:56,536 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:56,537 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.150943396226
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.150943396226
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.5
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.096260040145
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.333333333333
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.0975609756098
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.467147834744
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:56,537 INFO: Done:	 Result Analysis
+2016-09-06 11:21:56,625 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:56,625 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:56,625 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 11:21:56,625 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 11:21:56,626 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:56,626 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:56,626 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 11:21:56,626 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 11:21:56,626 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 11:21:56,626 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 11:21:56,626 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:56,626 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:56,626 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:56,626 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:56,664 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:56,664 DEBUG: Start:	 Training
+2016-09-06 11:21:56,666 DEBUG: Info:	 Time for Training: 0.0415709018707[s]
+2016-09-06 11:21:56,666 DEBUG: Done:	 Training
+2016-09-06 11:21:56,666 DEBUG: Start:	 Predicting
+2016-09-06 11:21:56,669 DEBUG: Done:	 Predicting
+2016-09-06 11:21:56,669 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:56,678 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:56,678 DEBUG: Start:	 Training
+2016-09-06 11:21:56,684 DEBUG: Info:	 Time for Training: 0.059385061264[s]
+2016-09-06 11:21:56,684 DEBUG: Done:	 Training
+2016-09-06 11:21:56,684 DEBUG: Start:	 Predicting
+2016-09-06 11:21:56,688 DEBUG: Done:	 Predicting
+2016-09-06 11:21:56,688 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:56,718 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:56,718 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.418604651163
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.418604651163
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.111552687063
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.4
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.439024390244
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.44400199104
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:56,719 INFO: Done:	 Result Analysis
+2016-09-06 11:21:56,731 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:56,731 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.404761904762
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.404761904762
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.115633266975
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.395348837209
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.414634146341
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.442010950722
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:56,731 INFO: Done:	 Result Analysis
+2016-09-06 11:21:56,878 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:56,878 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 11:21:56,878 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:56,879 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 11:21:56,879 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 11:21:56,879 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:56,879 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:56,879 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:56,880 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 11:21:56,880 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:56,880 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 11:21:56,881 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 11:21:56,881 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:56,881 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:56,909 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:56,909 DEBUG: Start:	 Training
+2016-09-06 11:21:56,910 DEBUG: Info:	 Time for Training: 0.032821893692[s]
+2016-09-06 11:21:56,910 DEBUG: Done:	 Training
+2016-09-06 11:21:56,910 DEBUG: Start:	 Predicting
+2016-09-06 11:21:56,917 DEBUG: Done:	 Predicting
+2016-09-06 11:21:56,918 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:56,972 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:56,973 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 47
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.455555555556
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.633204633205
+		- Score on test : 0.558558558559
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.633204633205
+		- Score on test : 0.558558558559
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.452380952381
+		- Score on test : 0.544444444444
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.455555555556
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.102191547553
+		- Score on test : -0.0477019354931
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.535947712418
+		- Score on test : 0.442857142857
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.77358490566
+		- Score on test : 0.756097560976
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.545446298984
+		- Score on test : 0.480089596814
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.452380952381
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:56,973 INFO: Done:	 Result Analysis
+2016-09-06 11:21:57,059 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:57,059 DEBUG: Start:	 Training
+2016-09-06 11:21:57,082 DEBUG: Info:	 Time for Training: 0.203009128571[s]
+2016-09-06 11:21:57,082 DEBUG: Done:	 Training
+2016-09-06 11:21:57,082 DEBUG: Start:	 Predicting
+2016-09-06 11:21:57,088 DEBUG: Done:	 Predicting
+2016-09-06 11:21:57,088 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:57,136 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:57,136 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.522222222222
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.976525821596
+		- Score on test : 0.516853932584
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.976525821596
+		- Score on test : 0.516853932584
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0238095238095
+		- Score on test : 0.477777777778
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.522222222222
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.952415522541
+		- Score on test : 0.0506833064614
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.971962616822
+		- Score on test : 0.479166666667
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.981132075472
+		- Score on test : 0.560975609756
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.976142960813
+		- Score on test : 0.525385764062
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0238095238095
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:57,136 INFO: Done:	 Result Analysis
+2016-09-06 11:21:57,224 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:57,224 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 11:21:57,224 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:57,225 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:57,225 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 11:21:57,225 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:57,225 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 11:21:57,225 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 11:21:57,225 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 11:21:57,225 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:57,225 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 11:21:57,226 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:57,226 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:57,226 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:57,271 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:57,271 DEBUG: Start:	 Training
+2016-09-06 11:21:57,272 DEBUG: Info:	 Time for Training: 0.0480880737305[s]
+2016-09-06 11:21:57,272 DEBUG: Done:	 Training
+2016-09-06 11:21:57,272 DEBUG: Start:	 Predicting
+2016-09-06 11:21:57,281 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:57,281 DEBUG: Start:	 Training
+2016-09-06 11:21:57,288 DEBUG: Done:	 Predicting
+2016-09-06 11:21:57,289 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:57,310 DEBUG: Info:	 Time for Training: 0.0865099430084[s]
+2016-09-06 11:21:57,311 DEBUG: Done:	 Training
+2016-09-06 11:21:57,311 DEBUG: Start:	 Predicting
+2016-09-06 11:21:57,315 DEBUG: Done:	 Predicting
+2016-09-06 11:21:57,315 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:57,316 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:57,316 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.522222222222
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.564593301435
+		- Score on test : 0.516853932584
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.564593301435
+		- Score on test : 0.516853932584
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.433333333333
+		- Score on test : 0.477777777778
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.522222222222
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.133545022783
+		- Score on test : 0.0506833064614
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.572815533981
+		- Score on test : 0.479166666667
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.556603773585
+		- Score on test : 0.560975609756
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.566763425254
+		- Score on test : 0.525385764062
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.433333333333
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:57,316 INFO: Done:	 Result Analysis
+2016-09-06 11:21:57,383 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:57,383 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.528571428571
+		- Score on test : 0.533333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.535211267606
+		- Score on test : 0.475
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.535211267606
+		- Score on test : 0.475
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.471428571429
+		- Score on test : 0.466666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.528571428571
+		- Score on test : 0.533333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.0569743711331
+		- Score on test : 0.0555284586866
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.532710280374
+		- Score on test : 0.487179487179
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.537735849057
+		- Score on test : 0.463414634146
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.528483309144
+		- Score on test : 0.52762568442
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.471428571429
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:57,383 INFO: Done:	 Result Analysis
+2016-09-06 11:21:57,473 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:57,473 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:57,474 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 11:21:57,474 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 11:21:57,474 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:57,474 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:57,475 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 11:21:57,475 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 11:21:57,475 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 11:21:57,475 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 11:21:57,475 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:57,475 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:57,475 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:57,475 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:57,549 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:57,549 DEBUG: Start:	 Training
+2016-09-06 11:21:57,561 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:57,561 DEBUG: Start:	 Training
+2016-09-06 11:21:57,573 DEBUG: Info:	 Time for Training: 0.100679159164[s]
+2016-09-06 11:21:57,574 DEBUG: Done:	 Training
+2016-09-06 11:21:57,574 DEBUG: Start:	 Predicting
+2016-09-06 11:21:57,582 DEBUG: Done:	 Predicting
+2016-09-06 11:21:57,582 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:57,590 DEBUG: Info:	 Time for Training: 0.117436170578[s]
+2016-09-06 11:21:57,590 DEBUG: Done:	 Training
+2016-09-06 11:21:57,590 DEBUG: Start:	 Predicting
+2016-09-06 11:21:57,596 DEBUG: Done:	 Predicting
+2016-09-06 11:21:57,597 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:57,627 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:57,627 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511627906977
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511627906977
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.466666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.066931612238
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.536585365854
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533598805376
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:57,627 INFO: Done:	 Result Analysis
+2016-09-06 11:21:57,645 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:57,645 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.561403508772
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.561403508772
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.0715653145323
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.438356164384
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.780487804878
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.4718765555
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:57,645 INFO: Done:	 Result Analysis
+2016-09-06 11:21:57,719 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:57,719 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 11:21:57,719 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:57,719 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:57,719 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 11:21:57,719 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:57,720 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 11:21:57,720 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 11:21:57,720 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 11:21:57,720 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 11:21:57,720 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:57,720 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:57,720 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:57,720 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:57,753 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:57,753 DEBUG: Start:	 Training
+2016-09-06 11:21:57,755 DEBUG: Info:	 Time for Training: 0.0360541343689[s]
+2016-09-06 11:21:57,755 DEBUG: Done:	 Training
+2016-09-06 11:21:57,755 DEBUG: Start:	 Predicting
+2016-09-06 11:21:57,757 DEBUG: Done:	 Predicting
+2016-09-06 11:21:57,757 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:57,767 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:57,767 DEBUG: Start:	 Training
+2016-09-06 11:21:57,771 DEBUG: Info:	 Time for Training: 0.052619934082[s]
+2016-09-06 11:21:57,771 DEBUG: Done:	 Training
+2016-09-06 11:21:57,771 DEBUG: Start:	 Predicting
+2016-09-06 11:21:57,774 DEBUG: Done:	 Predicting
+2016-09-06 11:21:57,774 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:57,802 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:57,802 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.533333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.995305164319
+		- Score on test : 0.511627906977
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.995305164319
+		- Score on test : 0.511627906977
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0047619047619
+		- Score on test : 0.466666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.533333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.990519401324
+		- Score on test : 0.066931612238
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.990654205607
+		- Score on test : 0.488888888889
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.536585365854
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.995192307692
+		- Score on test : 0.533598805376
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0047619047619
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:57,803 INFO: Done:	 Result Analysis
+2016-09-06 11:21:57,820 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:57,820 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.512195121951
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.512195121951
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.444444444444
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.104031856645
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.512195121951
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.512195121951
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.552015928323
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:57,820 INFO: Done:	 Result Analysis
+2016-09-06 11:21:57,970 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:57,970 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:57,970 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 11:21:57,970 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 11:21:57,970 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:57,970 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:57,971 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 11:21:57,971 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 11:21:57,971 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 11:21:57,971 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 11:21:57,971 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:57,971 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:57,971 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:57,971 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:58,004 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:58,004 DEBUG: Start:	 Training
+2016-09-06 11:21:58,005 DEBUG: Info:	 Time for Training: 0.0356760025024[s]
+2016-09-06 11:21:58,005 DEBUG: Done:	 Training
+2016-09-06 11:21:58,005 DEBUG: Start:	 Predicting
+2016-09-06 11:21:58,013 DEBUG: Done:	 Predicting
+2016-09-06 11:21:58,013 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:58,054 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:58,054 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 47
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.477777777778
+		- Score on test : 0.342105263158
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.477777777778
+		- Score on test : 0.342105263158
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.447619047619
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.112597765671
+		- Score on test : -0.134753650348
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.581081081081
+		- Score on test : 0.371428571429
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.405660377358
+		- Score on test : 0.317073170732
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.553791727141
+		- Score on test : 0.434046789447
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.447619047619
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:58,054 INFO: Done:	 Result Analysis
+2016-09-06 11:21:58,134 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:58,134 DEBUG: Start:	 Training
+2016-09-06 11:21:58,151 DEBUG: Info:	 Time for Training: 0.181129932404[s]
+2016-09-06 11:21:58,151 DEBUG: Done:	 Training
+2016-09-06 11:21:58,151 DEBUG: Start:	 Predicting
+2016-09-06 11:21:58,154 DEBUG: Done:	 Predicting
+2016-09-06 11:21:58,155 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:58,182 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:58,182 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.588888888889
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.96682464455
+		- Score on test : 0.53164556962
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.96682464455
+		- Score on test : 0.53164556962
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0333333333333
+		- Score on test : 0.411111111111
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.588888888889
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.933375664255
+		- Score on test : 0.166630556676
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.971428571429
+		- Score on test : 0.552631578947
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.962264150943
+		- Score on test : 0.512195121951
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.966708998549
+		- Score on test : 0.582628173221
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0333333333333
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:58,182 INFO: Done:	 Result Analysis
+2016-09-06 11:21:58,321 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:58,321 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 11:21:58,321 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:58,321 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:58,321 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 11:21:58,322 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:58,322 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 11:21:58,322 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 11:21:58,322 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 11:21:58,322 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 11:21:58,323 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:58,323 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:58,323 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:58,323 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:58,392 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:58,392 DEBUG: Start:	 Training
+2016-09-06 11:21:58,393 DEBUG: Info:	 Time for Training: 0.0729160308838[s]
+2016-09-06 11:21:58,393 DEBUG: Done:	 Training
+2016-09-06 11:21:58,393 DEBUG: Start:	 Predicting
+2016-09-06 11:21:58,396 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:58,396 DEBUG: Start:	 Training
+2016-09-06 11:21:58,406 DEBUG: Done:	 Predicting
+2016-09-06 11:21:58,406 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:58,415 DEBUG: Info:	 Time for Training: 0.095123052597[s]
+2016-09-06 11:21:58,416 DEBUG: Done:	 Training
+2016-09-06 11:21:58,416 DEBUG: Start:	 Predicting
+2016-09-06 11:21:58,419 DEBUG: Done:	 Predicting
+2016-09-06 11:21:58,419 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:58,431 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:58,431 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.561904761905
+		- Score on test : 0.5
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.544554455446
+		- Score on test : 0.470588235294
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.544554455446
+		- Score on test : 0.470588235294
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.438095238095
+		- Score on test : 0.5
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.561904761905
+		- Score on test : 0.5
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.125091870983
+		- Score on test : -0.00198364873142
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.572916666667
+		- Score on test : 0.454545454545
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.518867924528
+		- Score on test : 0.487804878049
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.562318577649
+		- Score on test : 0.499004479841
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.438095238095
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:58,431 INFO: Done:	 Result Analysis
+2016-09-06 11:21:58,448 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:58,448 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.633333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.633333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.51376146789
+		- Score on test : 0.637362637363
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.51376146789
+		- Score on test : 0.637362637363
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.504761904762
+		- Score on test : 0.366666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.633333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : -0.0101818424163
+		- Score on test : 0.279372118308
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.58
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.528301886792
+		- Score on test : 0.707317073171
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.494920174165
+		- Score on test : 0.6393728223
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.504761904762
+		- Score on test : 0.366666666667
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:58,449 INFO: Done:	 Result Analysis
+2016-09-06 11:21:58,568 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:58,568 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:58,568 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 11:21:58,568 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 11:21:58,568 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:58,568 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:58,569 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 11:21:58,569 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 11:21:58,569 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 11:21:58,569 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 11:21:58,569 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:58,569 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:58,569 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:58,569 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:58,614 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:58,614 DEBUG: Start:	 Training
+2016-09-06 11:21:58,619 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:58,619 DEBUG: Start:	 Training
+2016-09-06 11:21:58,632 DEBUG: Info:	 Time for Training: 0.064493894577[s]
+2016-09-06 11:21:58,632 DEBUG: Done:	 Training
+2016-09-06 11:21:58,632 DEBUG: Start:	 Predicting
+2016-09-06 11:21:58,637 DEBUG: Done:	 Predicting
+2016-09-06 11:21:58,637 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:58,638 DEBUG: Info:	 Time for Training: 0.0703361034393[s]
+2016-09-06 11:21:58,638 DEBUG: Done:	 Training
+2016-09-06 11:21:58,638 DEBUG: Start:	 Predicting
+2016-09-06 11:21:58,641 DEBUG: Done:	 Predicting
+2016-09-06 11:21:58,641 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:58,668 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:58,668 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.533333333333
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.0592334494774
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.428571428571
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.512195121951
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.470383275261
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:58,668 INFO: Done:	 Result Analysis
+2016-09-06 11:21:58,680 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:58,680 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.995260663507
+		- Score on test : 0.479166666667
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.995260663507
+		- Score on test : 0.479166666667
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0047619047619
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.990521113087
+		- Score on test : -0.0940733030728
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.418181818182
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.990566037736
+		- Score on test : 0.560975609756
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.995283018868
+		- Score on test : 0.453957192633
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0047619047619
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:58,680 INFO: Done:	 Result Analysis
+2016-09-06 11:21:58,822 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:58,822 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:58,822 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 11:21:58,822 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 11:21:58,822 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:58,822 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:58,823 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 11:21:58,823 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 11:21:58,823 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 11:21:58,823 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 11:21:58,823 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:58,823 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:58,823 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:58,823 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:58,874 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:58,874 DEBUG: Start:	 Training
+2016-09-06 11:21:58,875 DEBUG: Info:	 Time for Training: 0.0545539855957[s]
+2016-09-06 11:21:58,876 DEBUG: Done:	 Training
+2016-09-06 11:21:58,876 DEBUG: Start:	 Predicting
+2016-09-06 11:21:58,880 DEBUG: Done:	 Predicting
+2016-09-06 11:21:58,880 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:58,899 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:58,899 DEBUG: Start:	 Training
+2016-09-06 11:21:58,905 DEBUG: Info:	 Time for Training: 0.0836429595947[s]
+2016-09-06 11:21:58,905 DEBUG: Done:	 Training
+2016-09-06 11:21:58,905 DEBUG: Start:	 Predicting
+2016-09-06 11:21:58,908 DEBUG: Done:	 Predicting
+2016-09-06 11:21:58,908 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:58,930 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:58,930 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.428571428571
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.428571428571
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.533333333333
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.0709680565554
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.418604651163
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.439024390244
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.464410154306
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:58,931 INFO: Done:	 Result Analysis
+2016-09-06 11:21:58,947 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:58,947 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.404761904762
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.404761904762
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.115633266975
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.395348837209
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.414634146341
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.442010950722
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:58,947 INFO: Done:	 Result Analysis
+2016-09-06 11:21:59,070 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:59,070 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:59,070 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 11:21:59,070 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 11:21:59,070 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:59,070 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:59,071 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 11:21:59,071 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 11:21:59,071 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 11:21:59,071 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 11:21:59,071 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:59,071 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:59,071 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:59,071 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:59,101 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:59,102 DEBUG: Start:	 Training
+2016-09-06 11:21:59,102 DEBUG: Info:	 Time for Training: 0.0325701236725[s]
+2016-09-06 11:21:59,102 DEBUG: Done:	 Training
+2016-09-06 11:21:59,102 DEBUG: Start:	 Predicting
+2016-09-06 11:21:59,108 DEBUG: Done:	 Predicting
+2016-09-06 11:21:59,108 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:59,150 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:59,150 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 47
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.411111111111
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.562770562771
+		- Score on test : 0.453608247423
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.562770562771
+		- Score on test : 0.453608247423
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.480952380952
+		- Score on test : 0.588888888889
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.411111111111
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.0369594857345
+		- Score on test : -0.161571085301
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.52
+		- Score on test : 0.392857142857
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.61320754717
+		- Score on test : 0.536585365854
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.518142235123
+		- Score on test : 0.421353907417
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.480952380952
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:59,150 INFO: Done:	 Result Analysis
+2016-09-06 11:21:59,228 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:59,228 DEBUG: Start:	 Training
+2016-09-06 11:21:59,246 DEBUG: Info:	 Time for Training: 0.176759958267[s]
+2016-09-06 11:21:59,246 DEBUG: Done:	 Training
+2016-09-06 11:21:59,246 DEBUG: Start:	 Predicting
+2016-09-06 11:21:59,250 DEBUG: Done:	 Predicting
+2016-09-06 11:21:59,250 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:59,282 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:59,282 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.488888888889
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.976303317536
+		- Score on test : 0.410256410256
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.976303317536
+		- Score on test : 0.410256410256
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0238095238095
+		- Score on test : 0.511111111111
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.488888888889
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.952424147199
+		- Score on test : -0.0387937676182
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.980952380952
+		- Score on test : 0.432432432432
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.971698113208
+		- Score on test : 0.390243902439
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.976233671988
+		- Score on test : 0.480836236934
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0238095238095
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:59,283 INFO: Done:	 Result Analysis
+2016-09-06 11:21:59,419 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:59,419 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 11:21:59,419 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 11:21:59,419 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 11:21:59,419 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:59,419 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 11:21:59,419 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 11:21:59,419 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 11:21:59,420 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 11:21:59,420 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 11:21:59,420 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:59,420 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 11:21:59,420 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:59,420 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 11:21:59,464 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:59,464 DEBUG: Start:	 Training
+2016-09-06 11:21:59,465 DEBUG: Info:	 Time for Training: 0.0465881824493[s]
+2016-09-06 11:21:59,465 DEBUG: Done:	 Training
+2016-09-06 11:21:59,465 DEBUG: Start:	 Predicting
+2016-09-06 11:21:59,468 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 11:21:59,468 DEBUG: Start:	 Training
+2016-09-06 11:21:59,481 DEBUG: Done:	 Predicting
+2016-09-06 11:21:59,481 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:59,487 DEBUG: Info:	 Time for Training: 0.0686330795288[s]
+2016-09-06 11:21:59,487 DEBUG: Done:	 Training
+2016-09-06 11:21:59,487 DEBUG: Start:	 Predicting
+2016-09-06 11:21:59,490 DEBUG: Done:	 Predicting
+2016-09-06 11:21:59,490 DEBUG: Start:	 Getting Results
+2016-09-06 11:21:59,505 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:59,505 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.466666666667
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.559633027523
+		- Score on test : 0.478260869565
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.559633027523
+		- Score on test : 0.478260869565
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.457142857143
+		- Score on test : 0.533333333333
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.466666666667
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.0852729302366
+		- Score on test : -0.0555284586866
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.544642857143
+		- Score on test : 0.43137254902
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.575471698113
+		- Score on test : 0.536585365854
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.542543541364
+		- Score on test : 0.47237431558
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.457142857143
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:59,505 INFO: Done:	 Result Analysis
+2016-09-06 11:21:59,523 DEBUG: Done:	 Getting Results
+2016-09-06 11:21:59,523 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.566666666667
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.610878661088
+		- Score on test : 0.571428571429
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.610878661088
+		- Score on test : 0.571428571429
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.442857142857
+		- Score on test : 0.433333333333
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.566666666667
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.11594977827
+		- Score on test : 0.144674846981
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.548872180451
+		- Score on test : 0.52
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.688679245283
+		- Score on test : 0.634146341463
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.55587808418
+		- Score on test : 0.572175211548
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.442857142857
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 11:21:59,523 INFO: Done:	 Result Analysis
+2016-09-06 11:21:59,818 INFO: ### Main Programm for Multiview Classification
+2016-09-06 11:21:59,818 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 11:21:59,819 INFO: Info:	 Shape of View0 :(300, 13)
+2016-09-06 11:21:59,819 INFO: Info:	 Shape of View1 :(300, 18)
+2016-09-06 11:21:59,820 INFO: Info:	 Shape of View2 :(300, 8)
+2016-09-06 11:21:59,820 INFO: Info:	 Shape of View3 :(300, 6)
+2016-09-06 11:21:59,820 INFO: Done:	 Read Database Files
+2016-09-06 11:21:59,820 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 11:21:59,823 INFO: ### Main Programm for Multiview Classification
+2016-09-06 11:21:59,823 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 11:21:59,823 INFO: Info:	 Shape of View0 :(300, 13)
+2016-09-06 11:21:59,824 INFO: Info:	 Shape of View1 :(300, 18)
+2016-09-06 11:21:59,824 INFO: Done:	 Determine validation split
+2016-09-06 11:21:59,824 INFO: Start:	 Determine 5 folds
+2016-09-06 11:21:59,824 INFO: Info:	 Shape of View2 :(300, 8)
+2016-09-06 11:21:59,825 INFO: Info:	 Shape of View3 :(300, 6)
+2016-09-06 11:21:59,825 INFO: Done:	 Read Database Files
+2016-09-06 11:21:59,825 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 11:21:59,829 INFO: Done:	 Determine validation split
+2016-09-06 11:21:59,829 INFO: Start:	 Determine 5 folds
+2016-09-06 11:21:59,830 INFO: Info:	 Length of Learning Sets: 170
+2016-09-06 11:21:59,831 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 11:21:59,831 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 11:21:59,831 INFO: Done:	 Determine folds
+2016-09-06 11:21:59,831 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 11:21:59,831 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 11:21:59,831 DEBUG: 	Start:	 Gridsearch for DecisionTree on View0
+2016-09-06 11:21:59,834 INFO: Info:	 Length of Learning Sets: 170
+2016-09-06 11:21:59,834 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 11:21:59,834 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 11:21:59,834 INFO: Done:	 Determine folds
+2016-09-06 11:21:59,834 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 11:21:59,834 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 11:21:59,834 DEBUG: 	Start:	 Random search for Adaboost with 30 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7310aa13413c0fe51d30595a0611fbf8868e4fab
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-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 : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.494382022472
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.494382022472
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.5
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.00596274193664
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.458333333333
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.536585365854
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.502986560478
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..60733f7f4372d2fbe0d01db2bf0ef9f663fe2087
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-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 : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.413793103448
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.413793103448
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.566666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.131912640639
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.391304347826
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.439024390244
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433797909408
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..98c9ed2a5aec9b146da2753a524452b44e1a9d01
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 47
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.422222222222
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.550724637681
+		- Score on test : 0.315789473684
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.550724637681
+		- Score on test : 0.315789473684
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.442857142857
+		- Score on test : 0.577777777778
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.422222222222
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.11473701202
+		- Score on test : -0.180519041032
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.564356435644
+		- Score on test : 0.342857142857
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.537735849057
+		- Score on test : 0.292682926829
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.55732946299
+		- Score on test : 0.411647585864
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.442857142857
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..11143f3f7b7d16b5e170c5ab4fd7f72ea6e130b2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.957142857143
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.957142857143
+		- Score on test : 0.4
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.956937799043
+		- Score on test : 0.325
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.956937799043
+		- Score on test : 0.325
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0428571428571
+		- Score on test : 0.6
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.957142857143
+		- Score on test : 0.4
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.914674537841
+		- Score on test : -0.214609988978
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.970873786408
+		- Score on test : 0.333333333333
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.943396226415
+		- Score on test : 0.317073170732
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.957275036284
+		- Score on test : 0.393230462917
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0428571428571
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..22da83be580cf08f7fe511bc720910ca2202c3f6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-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, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.404761904762
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.404761904762
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.115633266975
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.395348837209
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.414634146341
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.442010950722
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..97bcfb03eede2f30c7c09839dcbc52d8578c8b88
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-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 : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.418604651163
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.418604651163
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.111552687063
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.4
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.439024390244
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.44400199104
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..db1e7c90d39f84e92df0500245ea9b9e26d1dedb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 47
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.455555555556
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.633204633205
+		- Score on test : 0.558558558559
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.633204633205
+		- Score on test : 0.558558558559
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.452380952381
+		- Score on test : 0.544444444444
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.455555555556
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.102191547553
+		- Score on test : -0.0477019354931
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.535947712418
+		- Score on test : 0.442857142857
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.77358490566
+		- Score on test : 0.756097560976
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.545446298984
+		- Score on test : 0.480089596814
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.452380952381
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..52272501df553f770e104ca4715e1eb70fe7a93f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.604761904762
+		- Score on test : 0.433333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.62443438914
+		- Score on test : 0.43956043956
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.62443438914
+		- Score on test : 0.43956043956
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.395238095238
+		- Score on test : 0.566666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.604761904762
+		- Score on test : 0.433333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.209578877963
+		- Score on test : -0.124719695673
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.6
+		- Score on test : 0.4
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.650943396226
+		- Score on test : 0.487804878049
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.604317851959
+		- Score on test : 0.437779990045
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.395238095238
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..023c955b3646997254d66b6f74a22072139d0cdc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.504672897196
+		- Score on test : 0.404761904762
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.504672897196
+		- Score on test : 0.404761904762
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.504761904762
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : -0.00980036362201
+		- Score on test : -0.115633266975
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.395348837209
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.509433962264
+		- Score on test : 0.414634146341
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.495101596517
+		- Score on test : 0.442010950722
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.504761904762
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4a817800d802fe8d5147dc163ddbc8afb6cdbb72
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-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 : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.150943396226
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.150943396226
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.5
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.096260040145
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.333333333333
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.0975609756098
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.467147834744
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9c326ee0a2e05dea68d6e24bbb431c6b26d3dc19
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-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 : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.345679012346
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.345679012346
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.588888888889
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.189573937423
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.35
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.341463414634
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.405425584868
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bc43f864d2ea828df712fbae60f20043f68cfb9c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-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 : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.512195121951
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.512195121951
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.444444444444
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.104031856645
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.512195121951
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.512195121951
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.552015928323
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c42bb400ae8a8fdedb841cb3c754293d696319cb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.533333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.995305164319
+		- Score on test : 0.511627906977
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.995305164319
+		- Score on test : 0.511627906977
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0047619047619
+		- Score on test : 0.466666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.533333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.990519401324
+		- Score on test : 0.066931612238
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.990654205607
+		- Score on test : 0.488888888889
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.536585365854
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.995192307692
+		- Score on test : 0.533598805376
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0047619047619
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7bf769f1bc0c2c156f175b1fe70658f88c9b6906
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.522222222222
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.976525821596
+		- Score on test : 0.516853932584
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.976525821596
+		- Score on test : 0.516853932584
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0238095238095
+		- Score on test : 0.477777777778
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.522222222222
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.952415522541
+		- Score on test : 0.0506833064614
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.971962616822
+		- Score on test : 0.479166666667
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.981132075472
+		- Score on test : 0.560975609756
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.976142960813
+		- Score on test : 0.525385764062
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0238095238095
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..45ae81a4f03834da811af554902b0ff07b317177
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-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 : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.522222222222
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.564593301435
+		- Score on test : 0.516853932584
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.564593301435
+		- Score on test : 0.516853932584
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.433333333333
+		- Score on test : 0.477777777778
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.522222222222
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.133545022783
+		- Score on test : 0.0506833064614
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.572815533981
+		- Score on test : 0.479166666667
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.556603773585
+		- Score on test : 0.560975609756
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.566763425254
+		- Score on test : 0.525385764062
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.433333333333
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..395792e34ea36d0f71040c5bd654478caf5c099b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.528571428571
+		- Score on test : 0.533333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.535211267606
+		- Score on test : 0.475
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.535211267606
+		- Score on test : 0.475
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.471428571429
+		- Score on test : 0.466666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.528571428571
+		- Score on test : 0.533333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.0569743711331
+		- Score on test : 0.0555284586866
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.532710280374
+		- Score on test : 0.487179487179
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.537735849057
+		- Score on test : 0.463414634146
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.528483309144
+		- Score on test : 0.52762568442
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.471428571429
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..96ee48b4709eca185b46819a817e5c8f27db9696
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-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 : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.561403508772
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.561403508772
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.0715653145323
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.438356164384
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.780487804878
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.4718765555
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..75cb5217f476620552e022b850221b11be87393f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-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 : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511627906977
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511627906977
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.466666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.066931612238
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.536585365854
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533598805376
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..92282ac5d982afa7b67e798392101b6eab92cabd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-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 : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.404761904762
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.404761904762
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.115633266975
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.395348837209
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.414634146341
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.442010950722
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ed81b59a61d0887a2038565f79039b2a5ebb6c36
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-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 : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.428571428571
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.428571428571
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.533333333333
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.0709680565554
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.418604651163
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.439024390244
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.464410154306
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bddd0960441ccf4c67f24b028906a144474cd1c2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 47
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.477777777778
+		- Score on test : 0.342105263158
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.477777777778
+		- Score on test : 0.342105263158
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.447619047619
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.112597765671
+		- Score on test : -0.134753650348
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.581081081081
+		- Score on test : 0.371428571429
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.405660377358
+		- Score on test : 0.317073170732
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.553791727141
+		- Score on test : 0.434046789447
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.447619047619
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9d35d1a6da182138e6ae1ad331a4e2d7316dbb03
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.588888888889
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.96682464455
+		- Score on test : 0.53164556962
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.96682464455
+		- Score on test : 0.53164556962
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0333333333333
+		- Score on test : 0.411111111111
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.588888888889
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.933375664255
+		- Score on test : 0.166630556676
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.971428571429
+		- Score on test : 0.552631578947
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.962264150943
+		- Score on test : 0.512195121951
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.966708998549
+		- Score on test : 0.582628173221
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0333333333333
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..98fcb2e6715737097b534f2d5a4e9c1553160301
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.561904761905
+		- Score on test : 0.5
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.544554455446
+		- Score on test : 0.470588235294
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.544554455446
+		- Score on test : 0.470588235294
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.438095238095
+		- Score on test : 0.5
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.561904761905
+		- Score on test : 0.5
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.125091870983
+		- Score on test : -0.00198364873142
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.572916666667
+		- Score on test : 0.454545454545
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.518867924528
+		- Score on test : 0.487804878049
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.562318577649
+		- Score on test : 0.499004479841
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.438095238095
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4471f09ed96536e0167cd416edb89fe3dff7ef2b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.633333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.633333333333
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.51376146789
+		- Score on test : 0.637362637363
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.51376146789
+		- Score on test : 0.637362637363
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.504761904762
+		- Score on test : 0.366666666667
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.633333333333
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : -0.0101818424163
+		- Score on test : 0.279372118308
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.58
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.528301886792
+		- Score on test : 0.707317073171
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.494920174165
+		- Score on test : 0.6393728223
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.504761904762
+		- Score on test : 0.366666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..25f841d0b5e3d23af058cf951b334597f5e1f51a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.444444444444
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.995260663507
+		- Score on test : 0.479166666667
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.995260663507
+		- Score on test : 0.479166666667
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0047619047619
+		- Score on test : 0.555555555556
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.444444444444
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.990521113087
+		- Score on test : -0.0940733030728
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.418181818182
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.990566037736
+		- Score on test : 0.560975609756
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.995283018868
+		- Score on test : 0.453957192633
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0047619047619
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..70e52533fb7bce3e44528ba58ac0135e2e7730c8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-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, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.533333333333
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : -0.0592334494774
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.428571428571
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.512195121951
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.470383275261
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e57793d4a36e3f6ef7af1095b20a8028d4f664c8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 47
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.411111111111
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.562770562771
+		- Score on test : 0.453608247423
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.562770562771
+		- Score on test : 0.453608247423
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.480952380952
+		- Score on test : 0.588888888889
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.411111111111
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.0369594857345
+		- Score on test : -0.161571085301
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.52
+		- Score on test : 0.392857142857
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.61320754717
+		- Score on test : 0.536585365854
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.518142235123
+		- Score on test : 0.421353907417
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.480952380952
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e01149648085dff1c6ff500035e0a1f24eec1e2d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 15
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.488888888889
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.976303317536
+		- Score on test : 0.410256410256
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.976303317536
+		- Score on test : 0.410256410256
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.0238095238095
+		- Score on test : 0.511111111111
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.488888888889
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.952424147199
+		- Score on test : -0.0387937676182
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.980952380952
+		- Score on test : 0.432432432432
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.971698113208
+		- Score on test : 0.390243902439
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.976233671988
+		- Score on test : 0.480836236934
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.0238095238095
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..08b2b5e9b92fa2691c9cf68a26275399640d2306
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.466666666667
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.559633027523
+		- Score on test : 0.478260869565
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.559633027523
+		- Score on test : 0.478260869565
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.457142857143
+		- Score on test : 0.533333333333
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.466666666667
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.0852729302366
+		- Score on test : -0.0555284586866
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.544642857143
+		- Score on test : 0.43137254902
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.575471698113
+		- Score on test : 0.536585365854
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.542543541364
+		- Score on test : 0.47237431558
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.457142857143
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..75c3ebcffd6e56bb4cde5a45b8b7327951ad3ad2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,54 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2991
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.566666666667
+	For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) : 
+		- Score on train : 0.610878661088
+		- Score on test : 0.571428571429
+	For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) : 
+		- Score on train : 0.610878661088
+		- Score on test : 0.571428571429
+	For Hamming loss using None as classes (lower is better) : 
+		- Score on train : 0.442857142857
+		- Score on test : 0.433333333333
+	For Jaccard similarity score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.566666666667
+	For Log loss using None as sample_weights, 1e-15 as eps (lower is better) : 
+		- Score on train : nan
+		- Score on test : nan
+	For Matthews correlation coefficient (higher is better) : 
+		- Score on train : 0.11594977827
+		- Score on test : 0.144674846981
+	For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.548872180451
+		- Score on test : 0.52
+	For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) : 
+		- Score on train : 0.688679245283
+		- Score on test : 0.634146341463
+	For ROS AUC score using None as sample_weights, micro as average (higher is better) : 
+		- Score on train : 0.55587808418
+		- Score on test : 0.572175211548
+	For Zero one loss using None as sample_weights (lower is better) : 
+		- Score on train : 0.442857142857
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161431-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-161431-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..5d2fd1b2aa1e875c07cdef76f8f21a644659e684
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-161431-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,50 @@
+2016-09-06 16:14:31,063 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 16:14:31,063 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00012634375 Gbytes /!\ 
+2016-09-06 16:14:36,078 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 16:14:36,080 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 16:14:36,142 INFO: ### Main Programm for Multiview Classification
+2016-09-06 16:14:36,142 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 16:14:36,143 INFO: Info:	 Shape of View0 :(300, 9)
+2016-09-06 16:14:36,143 INFO: ### Main Programm for Multiview Classification
+2016-09-06 16:14:36,143 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 16:14:36,143 INFO: Info:	 Shape of View1 :(300, 13)
+2016-09-06 16:14:36,144 INFO: Info:	 Shape of View0 :(300, 9)
+2016-09-06 16:14:36,144 INFO: Info:	 Shape of View2 :(300, 12)
+2016-09-06 16:14:36,144 INFO: Info:	 Shape of View1 :(300, 13)
+2016-09-06 16:14:36,144 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-06 16:14:36,145 INFO: Done:	 Read Database Files
+2016-09-06 16:14:36,145 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 16:14:36,145 INFO: Info:	 Shape of View2 :(300, 12)
+2016-09-06 16:14:36,145 INFO: Info:	 Shape of View3 :(300, 12)
+2016-09-06 16:14:36,145 INFO: Done:	 Read Database Files
+2016-09-06 16:14:36,145 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 16:14:36,150 INFO: Done:	 Determine validation split
+2016-09-06 16:14:36,150 INFO: Start:	 Determine 5 folds
+2016-09-06 16:14:36,151 INFO: Done:	 Determine validation split
+2016-09-06 16:14:36,151 INFO: Start:	 Determine 5 folds
+2016-09-06 16:14:36,159 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 16:14:36,159 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 16:14:36,159 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 16:14:36,159 INFO: Done:	 Determine folds
+2016-09-06 16:14:36,159 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 16:14:36,159 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:14:36,160 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:14:36,162 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 16:14:36,162 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 16:14:36,162 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 16:14:36,162 INFO: Done:	 Determine folds
+2016-09-06 16:14:36,162 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 16:14:36,162 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:14:36,162 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:14:36,589 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:14:36,589 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:14:36,594 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:14:36,594 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:14:37,006 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:14:37,006 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:14:37,012 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:14:37,012 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:14:37,423 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:14:37,423 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:14:37,429 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:14:37,429 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161457-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-161457-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..cd3713da6bdd48e685e1a2a5d11509f7380a4cf2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-161457-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,352 @@
+2016-09-06 16:14:57,297 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 16:14:57,298 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00015446875 Gbytes /!\ 
+2016-09-06 16:15:02,311 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 16:15:02,313 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 16:15:02,362 INFO: ### Main Programm for Multiview Classification
+2016-09-06 16:15:02,362 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 16:15:02,362 INFO: ### Main Programm for Multiview Classification
+2016-09-06 16:15:02,363 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 16:15:02,363 INFO: Info:	 Shape of View0 :(300, 15)
+2016-09-06 16:15:02,363 INFO: Info:	 Shape of View0 :(300, 15)
+2016-09-06 16:15:02,363 INFO: Info:	 Shape of View1 :(300, 16)
+2016-09-06 16:15:02,364 INFO: Info:	 Shape of View1 :(300, 16)
+2016-09-06 16:15:02,364 INFO: Info:	 Shape of View2 :(300, 14)
+2016-09-06 16:15:02,364 INFO: Info:	 Shape of View2 :(300, 14)
+2016-09-06 16:15:02,364 INFO: Info:	 Shape of View3 :(300, 13)
+2016-09-06 16:15:02,364 INFO: Done:	 Read Database Files
+2016-09-06 16:15:02,365 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 16:15:02,365 INFO: Info:	 Shape of View3 :(300, 13)
+2016-09-06 16:15:02,365 INFO: Done:	 Read Database Files
+2016-09-06 16:15:02,365 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 16:15:02,369 INFO: Done:	 Determine validation split
+2016-09-06 16:15:02,369 INFO: Start:	 Determine 5 folds
+2016-09-06 16:15:02,369 INFO: Done:	 Determine validation split
+2016-09-06 16:15:02,369 INFO: Start:	 Determine 5 folds
+2016-09-06 16:15:02,375 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 16:15:02,375 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 16:15:02,375 INFO: Info:	 Length of Validation Set: 90
+2016-09-06 16:15:02,375 INFO: Done:	 Determine folds
+2016-09-06 16:15:02,375 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 16:15:02,375 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:15:02,375 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:02,376 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 16:15:02,376 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 16:15:02,376 INFO: Info:	 Length of Validation Set: 90
+2016-09-06 16:15:02,376 INFO: Done:	 Determine folds
+2016-09-06 16:15:02,376 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 16:15:02,376 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:15:02,377 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:02,805 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:02,805 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:02,809 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:02,809 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:03,243 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:03,243 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:03,311 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:03,311 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:03,666 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:03,666 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:03,737 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:03,737 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:04,086 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:04,161 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:05,703 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:15:05,703 INFO: Start:	 Classification
+2016-09-06 16:15:05,703 INFO: 	Start:	 Fold number 1
+2016-09-06 16:15:05,733 INFO: 	Start: 	 Classification
+2016-09-06 16:15:05,759 INFO: 	Done: 	 Fold number 1
+2016-09-06 16:15:05,759 INFO: 	Start:	 Fold number 2
+2016-09-06 16:15:05,790 INFO: 	Start: 	 Classification
+2016-09-06 16:15:05,817 INFO: 	Done: 	 Fold number 2
+2016-09-06 16:15:05,817 INFO: 	Start:	 Fold number 3
+2016-09-06 16:15:05,848 INFO: 	Start: 	 Classification
+2016-09-06 16:15:05,875 INFO: 	Done: 	 Fold number 3
+2016-09-06 16:15:05,876 INFO: 	Start:	 Fold number 4
+2016-09-06 16:15:05,906 INFO: 	Start: 	 Classification
+2016-09-06 16:15:05,933 INFO: 	Done: 	 Fold number 4
+2016-09-06 16:15:05,933 INFO: 	Start:	 Fold number 5
+2016-09-06 16:15:05,964 INFO: 	Start: 	 Classification
+2016-09-06 16:15:05,991 INFO: 	Done: 	 Fold number 5
+2016-09-06 16:15:05,991 INFO: Done:	 Classification
+2016-09-06 16:15:05,991 INFO: Info:	 Time for Classification: 3[s]
+2016-09-06 16:15:05,992 INFO: Start:	 Result Analysis for Fusion
+2016-09-06 16:15:06,172 INFO: 		Result for Multiview classification with LateFusion
+
+Average accuracy :
+	-On Train : 100.0
+	-On Test : 48.2926829268
+	-On Validation : 49.3333333333
+
+Dataset info :
+	-Database name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : Methyl, MiRNA_, RNASeq, Clinic
+	-5 folds
+
+Classification configuration : 
+	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.4761253742, 0.00270164468431, 0.0406662390422, 0.480506742074
+	-With monoview classifiers : 
+		- Decision Tree with max_depth : 7
+		- Decision Tree with max_depth : 3
+		- Decision Tree with max_depth : 24
+		- Decision Tree with max_depth : 4
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:03        0:00:00
+	         Fold 2        0:00:03        0:00:00
+	         Fold 3        0:00:03        0:00:00
+	         Fold 4        0:00:03        0:00:00
+	         Fold 5        0:00:03        0:00:00
+	          Total        0:00:17        0:00:00
+	So a total classification time of 0:00:03.
+
+
+2016-09-06 16:15:06,172 INFO: Done:	 Result Analysis
+2016-09-06 16:15:06,570 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:15:06,570 INFO: Start:	 Classification
+2016-09-06 16:15:06,570 INFO: 	Start:	 Fold number 1
+2016-09-06 16:15:06,599 INFO: 	Start: 	 Classification
+2016-09-06 16:15:06,667 INFO: 	Done: 	 Fold number 1
+2016-09-06 16:15:06,668 INFO: 	Start:	 Fold number 2
+2016-09-06 16:15:06,697 INFO: 	Start: 	 Classification
+2016-09-06 16:15:06,765 INFO: 	Done: 	 Fold number 2
+2016-09-06 16:15:06,765 INFO: 	Start:	 Fold number 3
+2016-09-06 16:15:06,794 INFO: 	Start: 	 Classification
+2016-09-06 16:15:06,862 INFO: 	Done: 	 Fold number 3
+2016-09-06 16:15:06,862 INFO: 	Start:	 Fold number 4
+2016-09-06 16:15:06,896 INFO: 	Start: 	 Classification
+2016-09-06 16:15:06,964 INFO: 	Done: 	 Fold number 4
+2016-09-06 16:15:06,964 INFO: 	Start:	 Fold number 5
+2016-09-06 16:15:06,993 INFO: 	Start: 	 Classification
+2016-09-06 16:15:07,061 INFO: 	Done: 	 Fold number 5
+2016-09-06 16:15:07,061 INFO: Done:	 Classification
+2016-09-06 16:15:07,061 INFO: Info:	 Time for Classification: 4[s]
+2016-09-06 16:15:07,061 INFO: Start:	 Result Analysis for Fusion
+2016-09-06 16:15:07,194 INFO: 		Result for Multiview classification with LateFusion
+
+Average accuracy :
+	-On Train : 100.0
+	-On Test : 46.8292682927
+	-On Validation : 44.0
+
+Dataset info :
+	-Database name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : Methyl, MiRNA_, RNASeq, Clinic
+	-5 folds
+
+Classification configuration : 
+	-Algorithm used : LateFusion with Majority Voting 
+	-With monoview classifiers : 
+		- Decision Tree with max_depth : 7
+		- Decision Tree with max_depth : 3
+		- Decision Tree with max_depth : 24
+		- Decision Tree with max_depth : 4
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:04        0:00:00
+	         Fold 2        0:00:04        0:00:00
+	         Fold 3        0:00:04        0:00:00
+	         Fold 4        0:00:04        0:00:00
+	         Fold 5        0:00:04        0:00:00
+	          Total        0:00:22        0:00:00
+	So a total classification time of 0:00:04.
+
+
+2016-09-06 16:15:07,195 INFO: Done:	 Result Analysis
+2016-09-06 16:15:07,318 INFO: ### Main Programm for Multiview Classification
+2016-09-06 16:15:07,318 INFO: ### Main Programm for Multiview Classification
+2016-09-06 16:15:07,319 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 16:15:07,319 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 16:15:07,319 INFO: Info:	 Shape of View0 :(300, 15)
+2016-09-06 16:15:07,319 INFO: Info:	 Shape of View0 :(300, 15)
+2016-09-06 16:15:07,320 INFO: Info:	 Shape of View1 :(300, 16)
+2016-09-06 16:15:07,320 INFO: Info:	 Shape of View1 :(300, 16)
+2016-09-06 16:15:07,321 INFO: Info:	 Shape of View2 :(300, 14)
+2016-09-06 16:15:07,321 INFO: Info:	 Shape of View2 :(300, 14)
+2016-09-06 16:15:07,321 INFO: Info:	 Shape of View3 :(300, 13)
+2016-09-06 16:15:07,321 INFO: Info:	 Shape of View3 :(300, 13)
+2016-09-06 16:15:07,321 INFO: Done:	 Read Database Files
+2016-09-06 16:15:07,321 INFO: Done:	 Read Database Files
+2016-09-06 16:15:07,322 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 16:15:07,322 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 16:15:07,327 INFO: Done:	 Determine validation split
+2016-09-06 16:15:07,328 INFO: Done:	 Determine validation split
+2016-09-06 16:15:07,328 INFO: Start:	 Determine 5 folds
+2016-09-06 16:15:07,328 INFO: Start:	 Determine 5 folds
+2016-09-06 16:15:07,339 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 16:15:07,339 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 16:15:07,339 INFO: Info:	 Length of Validation Set: 90
+2016-09-06 16:15:07,339 INFO: Done:	 Determine folds
+2016-09-06 16:15:07,339 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 16:15:07,339 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:15:07,339 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:07,344 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 16:15:07,345 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 16:15:07,345 INFO: Info:	 Length of Validation Set: 90
+2016-09-06 16:15:07,345 INFO: Done:	 Determine folds
+2016-09-06 16:15:07,345 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 16:15:07,345 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:15:07,345 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:08,051 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:08,051 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:08,056 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:08,057 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:08,762 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:08,762 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:08,764 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:08,765 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:09,471 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:09,471 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:09,474 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:09,474 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:10,189 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:10,190 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:15:10,190 INFO: Start:	 Classification
+2016-09-06 16:15:10,190 INFO: 	Start:	 Fold number 1
+2016-09-06 16:15:10,192 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:10,252 INFO: 	Start: 	 Classification
+2016-09-06 16:15:10,280 INFO: 	Done: 	 Fold number 1
+2016-09-06 16:15:10,280 INFO: 	Start:	 Fold number 2
+2016-09-06 16:15:10,327 INFO: 	Start: 	 Classification
+2016-09-06 16:15:10,355 INFO: 	Done: 	 Fold number 2
+2016-09-06 16:15:10,355 INFO: 	Start:	 Fold number 3
+2016-09-06 16:15:10,402 INFO: 	Start: 	 Classification
+2016-09-06 16:15:10,430 INFO: 	Done: 	 Fold number 3
+2016-09-06 16:15:10,430 INFO: 	Start:	 Fold number 4
+2016-09-06 16:15:10,477 INFO: 	Start: 	 Classification
+2016-09-06 16:15:10,505 INFO: 	Done: 	 Fold number 4
+2016-09-06 16:15:10,505 INFO: 	Start:	 Fold number 5
+2016-09-06 16:15:10,553 INFO: 	Start: 	 Classification
+2016-09-06 16:15:10,581 INFO: 	Done: 	 Fold number 5
+2016-09-06 16:15:10,581 INFO: Done:	 Classification
+2016-09-06 16:15:10,581 INFO: Info:	 Time for Classification: 3[s]
+2016-09-06 16:15:10,581 INFO: Start:	 Result Analysis for Fusion
+2016-09-06 16:15:10,751 INFO: 		Result for Multiview classification with LateFusion
+
+Average accuracy :
+	-On Train : 100.0
+	-On Test : 44.8780487805
+	-On Validation : 50.6666666667
+
+Dataset info :
+	-Database name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : Methyl, MiRNA_, RNASeq, Clinic
+	-5 folds
+
+Classification configuration : 
+	-Algorithm used : LateFusion with SVM for linear 
+	-With monoview classifiers : 
+		- Decision Tree with max_depth : 7
+		- Decision Tree with max_depth : 3
+		- Decision Tree with max_depth : 24
+		- Decision Tree with max_depth : 4
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:02        0:00:00
+	         Fold 2        0:00:02        0:00:00
+	         Fold 3        0:00:03        0:00:00
+	         Fold 4        0:00:03        0:00:00
+	         Fold 5        0:00:03        0:00:00
+	          Total        0:00:15        0:00:00
+	So a total classification time of 0:00:03.
+
+
+2016-09-06 16:15:10,765 INFO: Done:	 Result Analysis
+2016-09-06 16:15:11,829 INFO: Done:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:15:11,830 INFO: Start:	 Classification
+2016-09-06 16:15:11,830 INFO: 	Start:	 Fold number 1
+2016-09-06 16:15:11,858 INFO: 	Start: 	 Classification
+2016-09-06 16:15:11,888 INFO: 	Done: 	 Fold number 1
+2016-09-06 16:15:11,888 INFO: 	Start:	 Fold number 2
+2016-09-06 16:15:11,917 INFO: 	Start: 	 Classification
+2016-09-06 16:15:11,947 INFO: 	Done: 	 Fold number 2
+2016-09-06 16:15:11,947 INFO: 	Start:	 Fold number 3
+2016-09-06 16:15:11,975 INFO: 	Start: 	 Classification
+2016-09-06 16:15:12,005 INFO: 	Done: 	 Fold number 3
+2016-09-06 16:15:12,005 INFO: 	Start:	 Fold number 4
+2016-09-06 16:15:12,033 INFO: 	Start: 	 Classification
+2016-09-06 16:15:12,063 INFO: 	Done: 	 Fold number 4
+2016-09-06 16:15:12,064 INFO: 	Start:	 Fold number 5
+2016-09-06 16:15:12,092 INFO: 	Start: 	 Classification
+2016-09-06 16:15:12,122 INFO: 	Done: 	 Fold number 5
+2016-09-06 16:15:12,122 INFO: Done:	 Classification
+2016-09-06 16:15:12,122 INFO: Info:	 Time for Classification: 4[s]
+2016-09-06 16:15:12,122 INFO: Start:	 Result Analysis for Fusion
+2016-09-06 16:15:12,264 INFO: 		Result for Multiview classification with LateFusion
+
+Average accuracy :
+	-On Train : 53.2544378698
+	-On Test : 53.6585365854
+	-On Validation : 53.3333333333
+
+Dataset info :
+	-Database name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : Methyl, MiRNA_, RNASeq, Clinic
+	-5 folds
+
+Classification configuration : 
+	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 0.111861495999, 0.371763764635, 0.0125470094031
+	-With monoview classifiers : 
+		- Decision Tree with max_depth : 7
+		- Decision Tree with max_depth : 3
+		- Decision Tree with max_depth : 24
+		- Decision Tree with max_depth : 4
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:04        0:00:00
+	         Fold 2        0:00:04        0:00:00
+	         Fold 3        0:00:04        0:00:00
+	         Fold 4        0:00:04        0:00:00
+	         Fold 5        0:00:04        0:00:00
+	          Total        0:00:23        0:00:00
+	So a total classification time of 0:00:04.
+
+
+2016-09-06 16:15:12,264 INFO: Done:	 Result Analysis
+2016-09-06 16:15:12,376 INFO: ### Main Programm for Multiview Classification
+2016-09-06 16:15:12,376 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 16:15:12,377 INFO: ### Main Programm for Multiview Classification
+2016-09-06 16:15:12,377 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 16:15:12,377 INFO: Info:	 Shape of View0 :(300, 15)
+2016-09-06 16:15:12,378 INFO: Info:	 Shape of View0 :(300, 15)
+2016-09-06 16:15:12,378 INFO: Info:	 Shape of View1 :(300, 16)
+2016-09-06 16:15:12,379 INFO: Info:	 Shape of View1 :(300, 16)
+2016-09-06 16:15:12,379 INFO: Info:	 Shape of View2 :(300, 14)
+2016-09-06 16:15:12,380 INFO: Info:	 Shape of View2 :(300, 14)
+2016-09-06 16:15:12,380 INFO: Info:	 Shape of View3 :(300, 13)
+2016-09-06 16:15:12,380 INFO: Done:	 Read Database Files
+2016-09-06 16:15:12,380 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 16:15:12,381 INFO: Info:	 Shape of View3 :(300, 13)
+2016-09-06 16:15:12,381 INFO: Done:	 Read Database Files
+2016-09-06 16:15:12,381 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 16:15:12,385 INFO: Done:	 Determine validation split
+2016-09-06 16:15:12,385 INFO: Start:	 Determine 5 folds
+2016-09-06 16:15:12,385 INFO: Done:	 Determine validation split
+2016-09-06 16:15:12,385 INFO: Start:	 Determine 5 folds
+2016-09-06 16:15:12,392 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 16:15:12,392 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 16:15:12,392 INFO: Info:	 Length of Validation Set: 90
+2016-09-06 16:15:12,392 INFO: Done:	 Determine folds
+2016-09-06 16:15:12,392 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 16:15:12,393 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:15:12,393 DEBUG: 	Start:	 Random search for DecisionTree with 30 iterations
+2016-09-06 16:15:12,393 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 16:15:12,393 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 16:15:12,393 INFO: Info:	 Length of Validation Set: 90
+2016-09-06 16:15:12,393 INFO: Done:	 Determine folds
+2016-09-06 16:15:12,393 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 16:15:12,393 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 16:15:12,393 DEBUG: 	Start:	 Random search for Adaboost with 30 iterations
+2016-09-06 16:15:12,822 DEBUG: 	Done:	 Random search for DecisionTree
+2016-09-06 16:15:15,451 DEBUG: 	Done:	 Random search for Adaboost
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161506Results-Fusion-LateFusion-BayesianInference-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161506Results-Fusion-LateFusion-BayesianInference-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..05f6d2404a2dcd983b37b1df7c9d17ab53b37cbf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-161506Results-Fusion-LateFusion-BayesianInference-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,32 @@
+		Result for Multiview classification with LateFusion
+
+Average accuracy :
+	-On Train : 100.0
+	-On Test : 48.2926829268
+	-On Validation : 49.3333333333
+
+Dataset info :
+	-Database name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : Methyl, MiRNA_, RNASeq, Clinic
+	-5 folds
+
+Classification configuration : 
+	-Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.4761253742, 0.00270164468431, 0.0406662390422, 0.480506742074
+	-With monoview classifiers : 
+		- Decision Tree with max_depth : 7
+		- Decision Tree with max_depth : 3
+		- Decision Tree with max_depth : 24
+		- Decision Tree with max_depth : 4
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:03        0:00:00
+	         Fold 2        0:00:03        0:00:00
+	         Fold 3        0:00:03        0:00:00
+	         Fold 4        0:00:03        0:00:00
+	         Fold 5        0:00:03        0:00:00
+	          Total        0:00:17        0:00:00
+	So a total classification time of 0:00:03.
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161507Results-Fusion-LateFusion-MajorityVoting-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161507Results-Fusion-LateFusion-MajorityVoting-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..83b87a9205a913e9cdd6483808f8d96f2c702d32
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-161507Results-Fusion-LateFusion-MajorityVoting-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,32 @@
+		Result for Multiview classification with LateFusion
+
+Average accuracy :
+	-On Train : 100.0
+	-On Test : 46.8292682927
+	-On Validation : 44.0
+
+Dataset info :
+	-Database name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : Methyl, MiRNA_, RNASeq, Clinic
+	-5 folds
+
+Classification configuration : 
+	-Algorithm used : LateFusion with Majority Voting 
+	-With monoview classifiers : 
+		- Decision Tree with max_depth : 7
+		- Decision Tree with max_depth : 3
+		- Decision Tree with max_depth : 24
+		- Decision Tree with max_depth : 4
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:04        0:00:00
+	         Fold 2        0:00:04        0:00:00
+	         Fold 3        0:00:04        0:00:00
+	         Fold 4        0:00:04        0:00:00
+	         Fold 5        0:00:04        0:00:00
+	          Total        0:00:22        0:00:00
+	So a total classification time of 0:00:04.
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161510Results-Fusion-LateFusion-SVMForLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161510Results-Fusion-LateFusion-SVMForLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c190e8d144c1431ffbef8cd728c7e9311e78d508
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-161510Results-Fusion-LateFusion-SVMForLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,32 @@
+		Result for Multiview classification with LateFusion
+
+Average accuracy :
+	-On Train : 100.0
+	-On Test : 44.8780487805
+	-On Validation : 50.6666666667
+
+Dataset info :
+	-Database name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : Methyl, MiRNA_, RNASeq, Clinic
+	-5 folds
+
+Classification configuration : 
+	-Algorithm used : LateFusion with SVM for linear 
+	-With monoview classifiers : 
+		- Decision Tree with max_depth : 7
+		- Decision Tree with max_depth : 3
+		- Decision Tree with max_depth : 24
+		- Decision Tree with max_depth : 4
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:02        0:00:00
+	         Fold 2        0:00:02        0:00:00
+	         Fold 3        0:00:03        0:00:00
+	         Fold 4        0:00:03        0:00:00
+	         Fold 5        0:00:03        0:00:00
+	          Total        0:00:15        0:00:00
+	So a total classification time of 0:00:03.
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161512Results-Fusion-LateFusion-WeightedLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161512Results-Fusion-LateFusion-WeightedLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5e8e31ae0495caadf8b277decbca8b06dca480e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-161512Results-Fusion-LateFusion-WeightedLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,32 @@
+		Result for Multiview classification with LateFusion
+
+Average accuracy :
+	-On Train : 53.2544378698
+	-On Test : 53.6585365854
+	-On Validation : 53.3333333333
+
+Dataset info :
+	-Database name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : Methyl, MiRNA_, RNASeq, Clinic
+	-5 folds
+
+Classification configuration : 
+	-Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 0.111861495999, 0.371763764635, 0.0125470094031
+	-With monoview classifiers : 
+		- Decision Tree with max_depth : 7
+		- Decision Tree with max_depth : 3
+		- Decision Tree with max_depth : 24
+		- Decision Tree with max_depth : 4
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:04        0:00:00
+	         Fold 2        0:00:04        0:00:00
+	         Fold 3        0:00:04        0:00:00
+	         Fold 4        0:00:04        0:00:00
+	         Fold 5        0:00:04        0:00:00
+	          Total        0:00:23        0:00:00
+	So a total classification time of 0:00:04.
+