diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index 5a97f9a55e43ae464ae60382ecdbaf72d3197576..490c4a27d1c7ebb3cd705f5d078296928da724aa 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -160,6 +160,7 @@ groupFusion.add_argument('--FU_cl_config', metavar='STRING', action='store', nar
 args = parser.parse_args()
 os.nice(args.nice)
 nbCores = args.CL_cores
+start = time.time()
 if args.name not in ["MultiOmic", "ModifiedMultiOmic", "Caltech", "Fake"]:
     getDatabase = getattr(DB, "getClassicDB" + args.type[1:])
 else:
@@ -265,8 +266,9 @@ SGDKWARGS = {"2": map(float, args.CL_SGD_alpha.split(":"))[0], "1": args.CL_SGD_
 KNNKWARGS = {"0": map(float, args.CL_KNN_neigh.split(":"))[0]}
 AdaboostKWARGS = {"0": args.CL_Ada_n_est.split(":")[0], "1": args.CL_Ada_b_est.split(":")[0]}
 
-
+dataBaseTime = time.time()-start
 argumentDictionaries = {"Monoview": {}, "Multiview": []}
+print benchmark
 try:
     if benchmark["Monoview"]:
         argumentDictionaries["Monoview"] = []
@@ -308,6 +310,7 @@ else:
     for viewIndex, view in enumerate(views):
         bestClassifiers.append(classifiersNames[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
         bestClassifiersConfigs.append(classifiersConfigs[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
+monoviewTime = time.time()-dataBaseTime
 try:
     if benchmark["Multiview"]:
         try:
@@ -376,12 +379,13 @@ else:
     resultsMultiview = [ExecMultiview(DATASET, args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
                                LABELS_DICTIONARY, gridSearch=gridSearch,
                                metrics=metrics, **arguments) for arguments in argumentDictionaries["Multiview"]]
-
+multiviewTime = time.time()-monoviewTime
 if nbCores>1:
     logging.debug("Start:\t Deleting "+str(nbCores)+" temporary datasets for multiprocessing")
     datasetFiles = DB.deleteHDF5(args.pathF, args.name, nbCores)
     logging.debug("Start:\t Deleting datasets for multiprocessing")
 
+times = [dataBaseTime, monoviewTime, multiviewTime]
 results = (resultsMonoview, resultsMultiview)
 resultAnalysis(benchmark, results, args.name)
 
diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index cda01eec1ee7b90f15440abe428e08aa79e4285f..a3024b984dffcc9d308f43fa5b8c9bbc2a30b856 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -105,7 +105,7 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
     logging.debug("Start:\t Getting Results")
 
     #Accuracy classification score
-    stringAnalysis, imagesAnalysis, train, ham, test = execute(name, learningRate, nbFolds, nbCores, gridSearch, metrics, nIter, feat, CL_type,
+    stringAnalysis, imagesAnalysis, metricsScores = execute(name, learningRate, nbFolds, nbCores, gridSearch, metrics, nIter, feat, CL_type,
                                          clKWARGS, classLabelsNames, X.shape,
                                          y_train, y_train_pred, y_test, y_test_pred, t_end)
     cl_desc = [value for key, value in sorted(clKWARGS.iteritems())]
@@ -134,7 +134,7 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
 
     logging.info("Done:\t Result Analysis")
     viewIndex = args["viewIndex"]
-    return viewIndex, [CL_type, test, cl_desc, feat]
+    return viewIndex, [CL_type, cl_desc.append(feat), metricsScores]
     # # Classification Report with Precision, Recall, F1 , Support
     # logging.debug("Info:\t Classification report:")
     # filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-Report"
diff --git a/Code/MonoMutliViewClassifiers/Monoview/analyzeResult.py b/Code/MonoMutliViewClassifiers/Monoview/analyzeResult.py
index b4586af6df28211a2fc73cbe46e5084d1dc8d688..091021f1d1d128674f2fe3f54facd57aa30c92ec 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/analyzeResult.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/analyzeResult.py
@@ -38,6 +38,7 @@ def getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred):
 
 def execute(name, learningRate, nbFolds, nbCores, gridSearch, metrics, nIter, feat, CL_type, clKWARGS, classLabelsNames,
             shape, y_train, y_train_pred, y_test, y_test_pred, time):
+    metricsScores = {}
     metricModule = getattr(Metrics, metrics[0][0])
     train = metricModule.score(y_train, y_train_pred)
     val = metricModule.score(y_test, y_test_pred)
@@ -47,7 +48,13 @@ def execute(name, learningRate, nbFolds, nbCores, gridSearch, metrics, nIter, fe
     stringAnalysis += getClassifierConfigString(CL_type, gridSearch, nbCores, nIter, clKWARGS)
     for metric in metrics:
         stringAnalysis+=getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred)
+        if metric[1]!=None:
+            metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+        else:
+            metricKWARGS = {}
+        metricsScores[metric[0]] = [getattr(Metrics, metric[0]).score(y_train, y_train_pred, **metricKWARGS), "",
+                                    getattr(Metrics, metric[0]).score(y_test, y_test_pred, **metricKWARGS)]
     stringAnalysis += "\n\n Classification took "+ str(hms(seconds=int(time)))
 
     imageAnalysis = {}
-    return stringAnalysis, imageAnalysis, train, "", val
\ No newline at end of file
+    return stringAnalysis, imageAnalysis, metricsScores
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
index b932d33b33cd4d362fadd8590da5c8da2a783de0..95125ee86536b7e58eea38941b7b8cd4139896dd 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
@@ -26,7 +26,7 @@ def ExecMultiview_multicore(coreIndex, name, learningRate, nbFolds, databaseType
                             gridSearch=False, nbCores=1, metrics=None, nIter=30, **arguments):
     DATASET = h5py.File(path+name+str(coreIndex)+".hdf5", "r")
     return ExecMultiview(DATASET, name, learningRate, nbFolds, 1, databaseType, path, LABELS_DICTIONARY,
-                         gridSearch=False, metrics=None, nIter=30, **arguments)
+                         gridSearch=gridSearch, metrics=None, nIter=30, **arguments)
 
 
 def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, path, LABELS_DICTIONARY,
@@ -59,15 +59,15 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
     logging.info("Start:\t Determine validation split for ratio " + str(learningRate))
     validationIndices = DB.splitDataset(DATASET, learningRate, datasetLength)
     learningIndices = [index for index in range(datasetLength) if index not in validationIndices]
-    datasetLength = len(learningIndices)
+    classificationSetLength = len(learningIndices)
     logging.info("Done:\t Determine validation split")
 
     logging.info("Start:\t Determine "+str(nbFolds)+" folds")
     if nbFolds != 1:
         kFolds = DB.getKFoldIndices(nbFolds, DATASET.get("labels")[...], NB_CLASS, learningIndices)
     else:
-        kFolds = [[], range(datasetLength)]
-    logging.info("Info:\t Length of Learning Sets: " + str(datasetLength - len(kFolds[0])))
+        kFolds = [[], range(classificationSetLength)]
+    logging.info("Info:\t Length of Learning Sets: " + str(classificationSetLength - len(kFolds[0])))
     logging.info("Info:\t Length of Testing Sets: " + str(len(kFolds[0])))
     logging.info("Info:\t Length of Validation Set: " + str(len(validationIndices)))
     logging.info("Done:\t Determine folds")
@@ -89,7 +89,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
     kFoldPredictionTime = []
     kFoldClassifier = []
 
-
+    gridSearch=True
     if gridSearch:
         logging.info("Start:\t Randomsearching best settings for monoview classifiers")
         bestSettings, fusionConfig = classifierGridSearch(DATASET, classificationKWARGS, learningIndices
@@ -104,10 +104,10 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
     logging.info("Start:\t Classification")
     # Begin Classification
     for foldIdx, fold in enumerate(kFolds):
-        if fold != range(datasetLength):
+        if fold != range(classificationSetLength):
             fold.sort()
             logging.info("\tStart:\t Fold number " + str(foldIdx + 1))
-            trainIndices = [index for index in range(datasetLength) if index not in fold]
+            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)
 
@@ -132,7 +132,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
 
     times = (extractionTime, kFoldLearningTime, kFoldPredictionTime, classificationTime)
 
-    stringAnalysis, imagesAnalysis, train, test, val = analysisModule.execute(kFoldClassifier, kFoldPredictedTrainLabels,
+    stringAnalysis, imagesAnalysis, metricsScores = analysisModule.execute(kFoldClassifier, kFoldPredictedTrainLabels,
                                                             kFoldPredictedTestLabels, kFoldPredictedValidationLabels,
                                                             DATASET, classificationKWARGS, learningRate, LABELS_DICTIONARY,
                                                             views, nbCores, times, kFolds, name, nbFolds,
@@ -166,7 +166,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
             imagesAnalysis[imageName].savefig(outputFileName + imageName + '.png')
 
     logging.info("Done:\t Result Analysis")
-    return CL_type, classificationKWARGS, train, test, val
+    return CL_type, classificationKWARGS, metricsScores
 
 
 if __name__=='__main__':
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
index dc4360cc563ad7533fb611c6f363cdfc9d9322d2..4f1e43a4b22227e90f43ff596a989b9ef92d25aa 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
@@ -44,6 +44,7 @@ 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/Methods/LateFusionPackage/BayesianInference.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/BayesianInference.py
index 1b6ffd4278962a041bcfa9668a9a6e8e26d0529b..5abae3d134a4099f8a5f43fc1e28c5a4b0702cc7 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/BayesianInference.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/BayesianInference.py
@@ -51,6 +51,5 @@ class BayesianInference(LateFusionClassifier):
                        "\n\t-With monoview classifiers : "
         for monoviewClassifierConfig, monoviewClassifierName in zip(monoviewClassifiersConfigs, monoviewClassifiersNames):
             monoviewClassifierModule = getattr(MonoviewClassifiers, monoviewClassifierName)
-            print monoviewClassifierConfig
             configString += monoviewClassifierModule.getConfig(monoviewClassifierConfig)
         return configString
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
index ea37f7447316317b3233649a9c0d51374b2db348..79d9f9b3be7e8b9bd2325307d70422285b2bae1b 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
@@ -32,6 +32,31 @@ 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
+    metricModule = getattr(Metrics, metric[0])
+    if metric[1]!=None:
+        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)]))
+    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]
+
+
+def getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+                     kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds):
+    metricsScores = {}
+    for metric in metrics:
+        metricsScores[metric[0]] = getTotalMetricScores(metric, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+                                                        kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
+    return metricsScores
+
+
+
+
 
 def execute(kFoldClassifier, kFoldPredictedTrainLabels,
             kFoldPredictedTestLabels, kFoldPredictedValidationLabels,
@@ -95,4 +120,6 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels,
                                                    str(hms(seconds=int(sum(kFoldPredictionTime))))])
     stringAnalysis += "\n\tSo a total classification time of " + str(hms(seconds=int(classificationTime))) + ".\n\n"
     imagesAnalysis = {}
-    return stringAnalysis, imagesAnalysis, totalAccuracyOnTrain, totalAccuracyOnTest, totalAccuracyOnValidation
+    metricsScores = getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+                                     kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
+    return stringAnalysis, imagesAnalysis, metricsScores
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
index 075022476f68251c41ec2142ad91a37c50a05b0d..6230c983d139d59577f86f40a24f07d875105053 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
@@ -131,7 +131,7 @@ def getAlgoConfig(initKWARGS, NB_CORES, viewNames, gridSearch, nIter, times):
 
 def getClassificationReport(kFolds, kFoldClassifier, CLASS_LABELS, validationIndices, DATASET,
                             kFoldPredictedTrainLabels, kFoldPredictedTestLabels, kFoldPredictedValidationLabels):
-    DATASET_LENGTH = DATASET.get("Metadata").attrs["datasetLength"]-len(validationIndices)
+    DATASET_LENGTH = DATASET.get("Metadata").attrs["datasetLength"]
     nbView = DATASET.get("Metadata").attrs["nbView"]
     NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
     kFoldPredictedTrainLabelsByIter = []
@@ -152,7 +152,7 @@ def getClassificationReport(kFolds, kFoldClassifier, CLASS_LABELS, validationInd
             mumboClassifier = kFoldClassifier[foldIdx]
             meanAverageAccuracies = np.mean(mumboClassifier.averageAccuracies, axis=0)
             kFoldMeanAverageAccuracies.append(meanAverageAccuracies)
-            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]
@@ -232,6 +232,31 @@ 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
+    metricModule = getattr(Metrics, metric[0])
+    if metric[1]!=None:
+        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) 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]
+
+
+def getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+                 kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds,):
+    metricsScores = {}
+    for metric in metrics:
+        metricsScores[metric[0]] = getTotalMetricScores(metric, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+                                                        kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
+    return metricsScores
+
+
+
 def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels, kFoldPredictedValidationLabels,
             DATASET, initKWARGS, LEARNING_RATE, LABELS_DICTIONARY, views, NB_CORES, times, kFolds, databaseName,
             nbFolds, validationIndices, gridSearch, nIter, metrics):
@@ -280,7 +305,6 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels
                             " : \n\t\t\t- Mean average Accuracy : "+str(meanAverageAccuracy)+\
                             "\n\t\t\t- Percentage of time chosen : "+str(bestViewStat)
     stringAnalysis += "\n\n For each iteration : "
-    print iterRelevant(0, kFoldClassifier)
     for iterIndex in range(maxIter):
         if iterRelevant(iterIndex, kFoldClassifier).any():
             stringAnalysis += "\n\t- Iteration " + str(iterIndex + 1)
@@ -294,9 +318,9 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels
     trainAccuracyByIter = list(formatedAccuracies["Train"].mean(axis=0))+modifiedMean(surplusAccuracies["Train"])
     testAccuracyByIter = list(formatedAccuracies["Test"].mean(axis=0))+modifiedMean(surplusAccuracies["Test"])
     validationAccuracyByIter = list(formatedAccuracies["Validation"].mean(axis=0))+modifiedMean(surplusAccuracies["Validation"])
-    print nbMaxIter
     name, image = plotAccuracyByIter(trainAccuracyByIter, testAccuracyByIter, validationAccuracyByIter, nbMaxIter,
                                      bestViews, views, classifierAnalysis)
     imagesAnalysis = {name: image}
-
-    return stringAnalysis, imagesAnalysis, totalAccuracyOnTrain, totalAccuracyOnTest, totalAccuracyOnValidation
+    metricsScores = getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+                                     kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
+    return stringAnalysis, imagesAnalysis, metricsScores
diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index 2a5fb0a5752c55c40653a30d410735f626ba0d60..032eee4fb9b8ff998b6e785b58b7b4e851df313f 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -1,6 +1,7 @@
 # Import built-in modules
 import time
 import pylab
+import logging
 
 # Import third party modules
 import matplotlib
@@ -12,22 +13,24 @@ __author__ 	= "Baptiste Bauvin"
 __status__ 	= "Prototype"                           # Production, Development, Prototype
 
 
-def resultAnalysis(benchmark, results, name):
-    mono, multi = results
-    names = [res[1][0]+res[1][3] for res in mono]
-    names+=[type_ if type_ != "Fusion" else a["fusionType"]+a["fusionMethod"] for type_, a, b, c, d in multi]
-    nbResults = len(mono)+len(multi)
-    accuracies = [100*float(res[1][1]) for res in mono]
-    accuracies += [float(accuracy) for a, b, c, d, accuracy in multi]
-    f = pylab.figure(figsize=(40, 30))
-    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("Accuracies on validation set for each classifier")
-    ax.bar(range(nbResults), accuracies, align='center')
-    ax.set_xticks(range(nbResults))
-    ax.set_xticklabels(names, rotation="vertical")
+def resultAnalysis(benchmark, results, name, times, metrics):
+    for metric in metrics:
+        mono, multi = results
+        names = [res[1][0]+"-"+res[1][1][-1] for res in mono]
+        names+=[type_ if type_ != "Fusion" else a["fusionType"]+"-"+a["fusionMethod"] for type_, a, b in multi]
+        nbResults = len(mono)+len(multi)
+        validationScores = [float(res[1][2][metric[0]][2]) for res in mono]
+        validationScores += [float(scores[metric[0]][2]) for a, b, scores in multi]
+        f = pylab.figure(figsize=(40, 30))
+        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")
+        ax.bar(range(nbResults), validationScores, align='center')
+        ax.set_xticks(range(nbResults))
+        ax.set_xticklabels(names, rotation="vertical")
 
-    f.savefig("Results/"+name+time.strftime("%Y%m%d-%H%M%S")+".png")
+        f.savefig("Results/"+name+"-"+metric[0]+"-"+time.strftime("%Y%m%d-%H%M%S")+".png")
+    logging.info("Extraction time : "+str(times[0])+"s, Monoview time : "+str(times[1])+"s, Multiview Time : "+str(times[2])+"s")
 
 
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..c45e025b25568e4e18bd3cae72e495b3e0d20abd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,8 @@
+2016-09-06 09:25:57,714 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 09:25:57,717 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 09:25:57,717 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 09:25:57,717 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 09:25:57,717 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 09:25:57,717 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 09:25:57,717 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 09:25:57,717 DEBUG: Start:	 RandomSearch best settings with 30 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..8bd52115dc8051d6d39f7aa867ab793b4db84c1a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,15 @@
+2016-09-06 10:06:22,879 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:06:22,881 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:06:22,881 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:06:22,881 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:06:22,882 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:06:22,882 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:06:22,882 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:06:22,882 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:06:22,965 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:06:22,965 DEBUG: Start:	 Training
+2016-09-06 10:06:22,970 DEBUG: Info:	 Time for Training: 0.0897569656372[s]
+2016-09-06 10:06:22,971 DEBUG: Done:	 Training
+2016-09-06 10:06:22,971 DEBUG: Start:	 Predicting
+2016-09-06 10:06:22,984 DEBUG: Done:	 Predicting
+2016-09-06 10:06:22,984 DEBUG: Start:	 Getting Results
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..b3758f074b7c859f23b08a7aea92ac6164b7b190
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1310 @@
+2016-09-06 10:07:29,943 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:07:29,945 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:29,945 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:29,945 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:29,946 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:29,946 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:29,946 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:29,946 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,025 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:30,025 DEBUG: Start:	 Training
+2016-09-06 10:07:30,029 DEBUG: Info:	 Time for Training: 0.0845639705658[s]
+2016-09-06 10:07:30,029 DEBUG: Done:	 Training
+2016-09-06 10:07:30,029 DEBUG: Start:	 Predicting
+2016-09-06 10:07:30,032 DEBUG: Done:	 Predicting
+2016-09-06 10:07:30,032 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:30,033 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:30,034 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,034 INFO: Done:	 Result Analysis
+2016-09-06 10:07:30,035 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,035 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:30,035 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:30,035 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,035 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,035 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:30,035 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,065 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:30,065 DEBUG: Start:	 Training
+2016-09-06 10:07:30,066 DEBUG: Info:	 Time for Training: 0.0320420265198[s]
+2016-09-06 10:07:30,067 DEBUG: Done:	 Training
+2016-09-06 10:07:30,067 DEBUG: Start:	 Predicting
+2016-09-06 10:07:30,068 DEBUG: Done:	 Predicting
+2016-09-06 10:07:30,068 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:30,069 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:30,069 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.866666666667
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.866666666667
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,070 INFO: Done:	 Result Analysis
+2016-09-06 10:07:30,071 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,071 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:30,071 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:30,071 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,071 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,071 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:30,071 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,113 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:30,113 DEBUG: Start:	 Training
+2016-09-06 10:07:30,113 DEBUG: Info:	 Time for Training: 0.0430898666382[s]
+2016-09-06 10:07:30,113 DEBUG: Done:	 Training
+2016-09-06 10:07:30,114 DEBUG: Start:	 Predicting
+2016-09-06 10:07:30,119 DEBUG: Done:	 Predicting
+2016-09-06 10:07:30,120 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:30,121 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:30,121 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 44
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,121 INFO: Done:	 Result Analysis
+2016-09-06 10:07:30,122 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,122 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:30,122 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:30,122 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,123 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,123 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:30,123 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,425 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:30,425 DEBUG: Start:	 Training
+2016-09-06 10:07:30,470 DEBUG: Info:	 Time for Training: 0.348073959351[s]
+2016-09-06 10:07:30,470 DEBUG: Done:	 Training
+2016-09-06 10:07:30,470 DEBUG: Start:	 Predicting
+2016-09-06 10:07:30,475 DEBUG: Done:	 Predicting
+2016-09-06 10:07:30,475 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:30,476 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:30,476 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.766666666667
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 18, max_depth : 3
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.766666666667
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,477 INFO: Done:	 Result Analysis
+2016-09-06 10:07:30,478 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,478 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:30,478 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:30,478 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,478 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,478 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:30,478 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,540 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:30,540 DEBUG: Start:	 Training
+2016-09-06 10:07:30,541 DEBUG: Info:	 Time for Training: 0.0633749961853[s]
+2016-09-06 10:07:30,541 DEBUG: Done:	 Training
+2016-09-06 10:07:30,541 DEBUG: Start:	 Predicting
+2016-09-06 10:07:30,587 DEBUG: Done:	 Predicting
+2016-09-06 10:07:30,587 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:30,589 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:30,590 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.62380952381
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,590 INFO: Done:	 Result Analysis
+2016-09-06 10:07:30,592 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,592 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:30,592 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:30,593 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,593 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,593 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:30,593 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,640 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:30,640 DEBUG: Start:	 Training
+2016-09-06 10:07:30,658 DEBUG: Info:	 Time for Training: 0.0669250488281[s]
+2016-09-06 10:07:30,658 DEBUG: Done:	 Training
+2016-09-06 10:07:30,658 DEBUG: Start:	 Predicting
+2016-09-06 10:07:30,661 DEBUG: Done:	 Predicting
+2016-09-06 10:07:30,661 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:30,662 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:30,662 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 491
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.52380952381
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,662 INFO: Done:	 Result Analysis
+2016-09-06 10:07:30,663 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,664 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:30,664 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:30,664 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,664 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,664 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:30,664 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,709 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:30,710 DEBUG: Start:	 Training
+2016-09-06 10:07:30,726 DEBUG: Info:	 Time for Training: 0.0627450942993[s]
+2016-09-06 10:07:30,726 DEBUG: Done:	 Training
+2016-09-06 10:07:30,726 DEBUG: Start:	 Predicting
+2016-09-06 10:07:30,729 DEBUG: Done:	 Predicting
+2016-09-06 10:07:30,729 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:30,730 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:30,730 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2405
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,730 INFO: Done:	 Result Analysis
+2016-09-06 10:07:30,731 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,731 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:30,731 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:30,732 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:07:30,732 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:07:30,732 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:30,732 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,773 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:30,773 DEBUG: Start:	 Training
+2016-09-06 10:07:30,790 DEBUG: Info:	 Time for Training: 0.0591881275177[s]
+2016-09-06 10:07:30,790 DEBUG: Done:	 Training
+2016-09-06 10:07:30,790 DEBUG: Start:	 Predicting
+2016-09-06 10:07:30,794 DEBUG: Done:	 Predicting
+2016-09-06 10:07:30,795 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:30,796 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:30,796 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9676
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,796 INFO: Done:	 Result Analysis
+2016-09-06 10:07:30,797 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,797 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:30,797 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:30,797 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:30,798 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:30,798 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:30,798 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,840 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:30,840 DEBUG: Start:	 Training
+2016-09-06 10:07:30,844 DEBUG: Info:	 Time for Training: 0.0470898151398[s]
+2016-09-06 10:07:30,844 DEBUG: Done:	 Training
+2016-09-06 10:07:30,844 DEBUG: Start:	 Predicting
+2016-09-06 10:07:30,846 DEBUG: Done:	 Predicting
+2016-09-06 10:07:30,846 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:30,848 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:30,848 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,848 INFO: Done:	 Result Analysis
+2016-09-06 10:07:30,849 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,849 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:30,849 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:30,849 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:30,849 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:30,850 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:30,850 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,877 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:30,877 DEBUG: Start:	 Training
+2016-09-06 10:07:30,879 DEBUG: Info:	 Time for Training: 0.0301620960236[s]
+2016-09-06 10:07:30,879 DEBUG: Done:	 Training
+2016-09-06 10:07:30,879 DEBUG: Start:	 Predicting
+2016-09-06 10:07:30,880 DEBUG: Done:	 Predicting
+2016-09-06 10:07:30,880 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:30,882 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:30,882 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 21
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,882 INFO: Done:	 Result Analysis
+2016-09-06 10:07:30,883 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,883 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:30,883 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:30,883 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:30,883 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:30,883 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:30,884 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:30,909 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:30,909 DEBUG: Start:	 Training
+2016-09-06 10:07:30,910 DEBUG: Info:	 Time for Training: 0.0272629261017[s]
+2016-09-06 10:07:30,910 DEBUG: Done:	 Training
+2016-09-06 10:07:30,910 DEBUG: Start:	 Predicting
+2016-09-06 10:07:30,914 DEBUG: Done:	 Predicting
+2016-09-06 10:07:30,914 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:30,916 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:30,916 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 26
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.619047619048
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:30,916 INFO: Done:	 Result Analysis
+2016-09-06 10:07:30,917 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:30,917 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:30,917 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:30,917 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:30,918 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:30,918 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:30,918 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,132 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:31,133 DEBUG: Start:	 Training
+2016-09-06 10:07:31,164 DEBUG: Info:	 Time for Training: 0.247138977051[s]
+2016-09-06 10:07:31,164 DEBUG: Done:	 Training
+2016-09-06 10:07:31,164 DEBUG: Start:	 Predicting
+2016-09-06 10:07:31,168 DEBUG: Done:	 Predicting
+2016-09-06 10:07:31,168 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:31,169 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:31,169 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.980952380952
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 12, max_depth : 17
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.980952380952
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,170 INFO: Done:	 Result Analysis
+2016-09-06 10:07:31,171 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,171 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:31,171 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:31,171 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:31,171 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:31,171 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:31,171 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,209 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:31,209 DEBUG: Start:	 Training
+2016-09-06 10:07:31,210 DEBUG: Info:	 Time for Training: 0.0394690036774[s]
+2016-09-06 10:07:31,210 DEBUG: Done:	 Training
+2016-09-06 10:07:31,210 DEBUG: Start:	 Predicting
+2016-09-06 10:07:31,211 DEBUG: Done:	 Predicting
+2016-09-06 10:07:31,212 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:31,213 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:31,213 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.538095238095
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,213 INFO: Done:	 Result Analysis
+2016-09-06 10:07:31,214 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,214 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:31,214 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:31,214 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:31,215 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:31,215 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:31,215 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,257 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:31,257 DEBUG: Start:	 Training
+2016-09-06 10:07:31,275 DEBUG: Info:	 Time for Training: 0.061126947403[s]
+2016-09-06 10:07:31,275 DEBUG: Done:	 Training
+2016-09-06 10:07:31,275 DEBUG: Start:	 Predicting
+2016-09-06 10:07:31,277 DEBUG: Done:	 Predicting
+2016-09-06 10:07:31,277 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:31,279 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:31,279 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1250
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.466666666667
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,279 INFO: Done:	 Result Analysis
+2016-09-06 10:07:31,280 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,280 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:31,280 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:31,280 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:31,280 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:31,281 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:31,281 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,329 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:31,329 DEBUG: Start:	 Training
+2016-09-06 10:07:31,347 DEBUG: Info:	 Time for Training: 0.0671949386597[s]
+2016-09-06 10:07:31,347 DEBUG: Done:	 Training
+2016-09-06 10:07:31,347 DEBUG: Start:	 Predicting
+2016-09-06 10:07:31,349 DEBUG: Done:	 Predicting
+2016-09-06 10:07:31,349 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:31,351 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:31,351 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.633333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 432
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,351 INFO: Done:	 Result Analysis
+2016-09-06 10:07:31,352 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,352 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:31,352 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:31,352 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:07:31,352 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:07:31,352 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:31,353 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,393 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:31,393 DEBUG: Start:	 Training
+2016-09-06 10:07:31,409 DEBUG: Info:	 Time for Training: 0.0578751564026[s]
+2016-09-06 10:07:31,410 DEBUG: Done:	 Training
+2016-09-06 10:07:31,410 DEBUG: Start:	 Predicting
+2016-09-06 10:07:31,414 DEBUG: Done:	 Predicting
+2016-09-06 10:07:31,414 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:31,415 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:31,415 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6005
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,415 INFO: Done:	 Result Analysis
+2016-09-06 10:07:31,416 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,416 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:31,417 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:31,417 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,417 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,417 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:31,417 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,461 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:31,461 DEBUG: Start:	 Training
+2016-09-06 10:07:31,465 DEBUG: Info:	 Time for Training: 0.0488121509552[s]
+2016-09-06 10:07:31,465 DEBUG: Done:	 Training
+2016-09-06 10:07:31,465 DEBUG: Start:	 Predicting
+2016-09-06 10:07:31,467 DEBUG: Done:	 Predicting
+2016-09-06 10:07:31,467 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:31,469 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:31,469 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,469 INFO: Done:	 Result Analysis
+2016-09-06 10:07:31,470 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,470 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:31,470 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:31,471 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,471 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,471 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:31,471 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,502 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:31,502 DEBUG: Start:	 Training
+2016-09-06 10:07:31,505 DEBUG: Info:	 Time for Training: 0.0348341464996[s]
+2016-09-06 10:07:31,505 DEBUG: Done:	 Training
+2016-09-06 10:07:31,505 DEBUG: Start:	 Predicting
+2016-09-06 10:07:31,506 DEBUG: Done:	 Predicting
+2016-09-06 10:07:31,506 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:31,507 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:31,507 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 23
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,508 INFO: Done:	 Result Analysis
+2016-09-06 10:07:31,509 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,509 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:31,509 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:31,509 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,509 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,509 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:31,509 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,535 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:31,535 DEBUG: Start:	 Training
+2016-09-06 10:07:31,535 DEBUG: Info:	 Time for Training: 0.0270109176636[s]
+2016-09-06 10:07:31,535 DEBUG: Done:	 Training
+2016-09-06 10:07:31,535 DEBUG: Start:	 Predicting
+2016-09-06 10:07:31,540 DEBUG: Done:	 Predicting
+2016-09-06 10:07:31,540 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:31,541 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:31,541 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.628571428571
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,541 INFO: Done:	 Result Analysis
+2016-09-06 10:07:31,542 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,542 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:31,542 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:31,543 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,543 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,543 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:31,543 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,808 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:31,808 DEBUG: Start:	 Training
+2016-09-06 10:07:31,847 DEBUG: Info:	 Time for Training: 0.305594921112[s]
+2016-09-06 10:07:31,848 DEBUG: Done:	 Training
+2016-09-06 10:07:31,848 DEBUG: Start:	 Predicting
+2016-09-06 10:07:31,852 DEBUG: Done:	 Predicting
+2016-09-06 10:07:31,852 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:31,854 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:31,854 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 15, max_depth : 13
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,854 INFO: Done:	 Result Analysis
+2016-09-06 10:07:31,855 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,855 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:31,855 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:31,855 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,855 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,856 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:31,856 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,893 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:31,894 DEBUG: Start:	 Training
+2016-09-06 10:07:31,894 DEBUG: Info:	 Time for Training: 0.0398690700531[s]
+2016-09-06 10:07:31,895 DEBUG: Done:	 Training
+2016-09-06 10:07:31,895 DEBUG: Start:	 Predicting
+2016-09-06 10:07:31,896 DEBUG: Done:	 Predicting
+2016-09-06 10:07:31,896 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:31,897 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:31,897 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,898 INFO: Done:	 Result Analysis
+2016-09-06 10:07:31,899 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,899 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:31,899 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:31,899 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,899 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,899 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:31,899 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:31,943 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:31,943 DEBUG: Start:	 Training
+2016-09-06 10:07:31,963 DEBUG: Info:	 Time for Training: 0.0644710063934[s]
+2016-09-06 10:07:31,963 DEBUG: Done:	 Training
+2016-09-06 10:07:31,963 DEBUG: Start:	 Predicting
+2016-09-06 10:07:31,965 DEBUG: Done:	 Predicting
+2016-09-06 10:07:31,965 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:31,967 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:31,967 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6060
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:31,967 INFO: Done:	 Result Analysis
+2016-09-06 10:07:31,968 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:31,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:31,968 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:31,968 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:31,968 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:31,969 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:31,969 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,017 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:32,017 DEBUG: Start:	 Training
+2016-09-06 10:07:32,034 DEBUG: Info:	 Time for Training: 0.0663030147552[s]
+2016-09-06 10:07:32,034 DEBUG: Done:	 Training
+2016-09-06 10:07:32,034 DEBUG: Start:	 Predicting
+2016-09-06 10:07:32,037 DEBUG: Done:	 Predicting
+2016-09-06 10:07:32,037 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:32,038 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:32,039 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 569
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,039 INFO: Done:	 Result Analysis
+2016-09-06 10:07:32,040 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,040 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:32,040 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:32,040 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:07:32,040 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:07:32,040 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:32,041 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,082 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:32,082 DEBUG: Start:	 Training
+2016-09-06 10:07:32,098 DEBUG: Info:	 Time for Training: 0.0586409568787[s]
+2016-09-06 10:07:32,098 DEBUG: Done:	 Training
+2016-09-06 10:07:32,098 DEBUG: Start:	 Predicting
+2016-09-06 10:07:32,103 DEBUG: Done:	 Predicting
+2016-09-06 10:07:32,103 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:32,104 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:32,104 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1510
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,104 INFO: Done:	 Result Analysis
+2016-09-06 10:07:32,105 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,105 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:32,106 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:32,106 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,106 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,106 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:32,106 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,152 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:32,152 DEBUG: Start:	 Training
+2016-09-06 10:07:32,156 DEBUG: Info:	 Time for Training: 0.0510900020599[s]
+2016-09-06 10:07:32,156 DEBUG: Done:	 Training
+2016-09-06 10:07:32,156 DEBUG: Start:	 Predicting
+2016-09-06 10:07:32,158 DEBUG: Done:	 Predicting
+2016-09-06 10:07:32,158 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:32,160 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:32,160 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,160 INFO: Done:	 Result Analysis
+2016-09-06 10:07:32,161 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,161 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:32,161 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:32,162 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,162 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,162 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:32,162 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,195 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:32,195 DEBUG: Start:	 Training
+2016-09-06 10:07:32,198 DEBUG: Info:	 Time for Training: 0.0367488861084[s]
+2016-09-06 10:07:32,198 DEBUG: Done:	 Training
+2016-09-06 10:07:32,198 DEBUG: Start:	 Predicting
+2016-09-06 10:07:32,199 DEBUG: Done:	 Predicting
+2016-09-06 10:07:32,199 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:32,201 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:32,201 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,201 INFO: Done:	 Result Analysis
+2016-09-06 10:07:32,202 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,202 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:32,202 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:32,202 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,202 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,202 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:32,203 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,228 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:32,228 DEBUG: Start:	 Training
+2016-09-06 10:07:32,229 DEBUG: Info:	 Time for Training: 0.0273449420929[s]
+2016-09-06 10:07:32,229 DEBUG: Done:	 Training
+2016-09-06 10:07:32,229 DEBUG: Start:	 Predicting
+2016-09-06 10:07:32,234 DEBUG: Done:	 Predicting
+2016-09-06 10:07:32,234 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:32,235 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:32,235 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 13
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,235 INFO: Done:	 Result Analysis
+2016-09-06 10:07:32,236 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,236 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:32,236 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:32,237 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,237 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,237 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:32,237 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,394 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:32,395 DEBUG: Start:	 Training
+2016-09-06 10:07:32,416 DEBUG: Info:	 Time for Training: 0.180504083633[s]
+2016-09-06 10:07:32,417 DEBUG: Done:	 Training
+2016-09-06 10:07:32,417 DEBUG: Start:	 Predicting
+2016-09-06 10:07:32,420 DEBUG: Done:	 Predicting
+2016-09-06 10:07:32,420 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:32,421 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:32,421 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 8, max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,421 INFO: Done:	 Result Analysis
+2016-09-06 10:07:32,423 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,423 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:32,423 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:32,423 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,423 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,423 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:32,423 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,461 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:32,461 DEBUG: Start:	 Training
+2016-09-06 10:07:32,462 DEBUG: Info:	 Time for Training: 0.0399260520935[s]
+2016-09-06 10:07:32,462 DEBUG: Done:	 Training
+2016-09-06 10:07:32,462 DEBUG: Start:	 Predicting
+2016-09-06 10:07:32,464 DEBUG: Done:	 Predicting
+2016-09-06 10:07:32,464 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:32,465 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:32,465 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.6
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,465 INFO: Done:	 Result Analysis
+2016-09-06 10:07:32,466 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,466 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:32,466 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:32,467 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,467 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,467 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:32,467 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,512 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:32,512 DEBUG: Start:	 Training
+2016-09-06 10:07:32,532 DEBUG: Info:	 Time for Training: 0.0659711360931[s]
+2016-09-06 10:07:32,532 DEBUG: Done:	 Training
+2016-09-06 10:07:32,532 DEBUG: Start:	 Predicting
+2016-09-06 10:07:32,535 DEBUG: Done:	 Predicting
+2016-09-06 10:07:32,535 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:32,536 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:32,536 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3802
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,536 INFO: Done:	 Result Analysis
+2016-09-06 10:07:32,537 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,537 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:32,537 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:32,538 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,538 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,538 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:32,538 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,587 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:32,587 DEBUG: Start:	 Training
+2016-09-06 10:07:32,607 DEBUG: Info:	 Time for Training: 0.0699560642242[s]
+2016-09-06 10:07:32,607 DEBUG: Done:	 Training
+2016-09-06 10:07:32,607 DEBUG: Start:	 Predicting
+2016-09-06 10:07:32,610 DEBUG: Done:	 Predicting
+2016-09-06 10:07:32,610 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:32,612 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:32,612 INFO: Classification on Fake database for View3 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6378
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,612 INFO: Done:	 Result Analysis
+2016-09-06 10:07:32,613 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:32,613 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:32,613 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:32,613 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:32,614 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:32,614 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:32,614 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:32,656 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:32,656 DEBUG: Start:	 Training
+2016-09-06 10:07:32,673 DEBUG: Info:	 Time for Training: 0.0603799819946[s]
+2016-09-06 10:07:32,673 DEBUG: Done:	 Training
+2016-09-06 10:07:32,673 DEBUG: Start:	 Predicting
+2016-09-06 10:07:32,678 DEBUG: Done:	 Predicting
+2016-09-06 10:07:32,678 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:32,679 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:32,679 INFO: Classification on Fake database for View3 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6783
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:32,680 INFO: Done:	 Result Analysis
+2016-09-06 10:07:32,681 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:07:32,681 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:07:32,682 INFO: Info:	 Shape of View0 :(300, 11)
+2016-09-06 10:07:32,682 INFO: Info:	 Shape of View1 :(300, 8)
+2016-09-06 10:07:32,683 INFO: Info:	 Shape of View2 :(300, 14)
+2016-09-06 10:07:32,683 INFO: Info:	 Shape of View3 :(300, 17)
+2016-09-06 10:07:32,683 INFO: Done:	 Read Database Files
+2016-09-06 10:07:32,683 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:07:32,687 INFO: Done:	 Determine validation split
+2016-09-06 10:07:32,687 INFO: Start:	 Determine 5 folds
+2016-09-06 10:07:32,693 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:07:32,693 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:07:32,693 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:07:32,693 INFO: Done:	 Determine folds
+2016-09-06 10:07:32,693 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:07:32,693 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 10:07:32,694 DEBUG: 	Start:	 Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..eb326c0fc6bcd447955f86b145727dc3e8ff26a9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ae733e1929e1fed929217346a5778fecd6d24bbe
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 21
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3db0decfd13e4809f32d387c2294101916f6d217
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 26
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.619047619048
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..67f77df13c82767b09a14984ef010522d76ea256
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.766666666667
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 18, max_depth : 3
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.766666666667
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..20a567ad4268629af76247ce19c513a1bfa0c3d1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.62380952381
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..22e8c0747170ca6d854ec7a1b202073c1e824957
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 491
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.52380952381
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8c9cc330c2407ddec3757900e68d0d80cda77d7c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2405
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..410684488936523cc786b3cb99cdc3d1af517f50
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9676
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7029410e61e2defbf0c3d6e0a04793cee95c42a2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..13ea4b2bd1a9c3fba231da153d22d20d29a66e6d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 23
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f008d4bd08a7e52a2022a752cc496953663b8b26
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.628571428571
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e9ecf6a0dde3197db2479f22375921b46affe8bd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 15, max_depth : 13
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5522a9de8ebabadaf173f22caa10ec161bcdf64c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..da23f07f8bf468a26b1e28e16e93f8c6626438f4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6060
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..112d345dbd4a4d073ff80588f498674bf01c6f82
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.633333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 432
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..271f35f0db34c50d7cb1dec55ba9f49b465fa14a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6005
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dded33adf64dfde2f4a884c90384a58550736d62
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..13882acdefaf4969fb9147557dc0c24b766a78bb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6dd96027caf46c0f007b861206a51669831a460c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 13
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..218c51f12b84c5e7564a76147dee57556114a13d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 8, max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e4f1a860801220bf4e8171c3580e91ae4c1c1d59
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.6
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..96c36fc10a589a4c758c055af91b2630d4224536
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3802
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..371176119cb7a828e922f38ddea2afa8d19977e2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6378
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..824dde8583270a06d7b34ad3e0a4386c6cc6a4f8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6783
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..d22ae631189680a7633be60c9606007d36dfb195
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1376 @@
+2016-09-06 10:07:38,682 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:07:38,682 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00015915625 Gbytes /!\ 
+2016-09-06 10:07:43,694 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:07:43,696 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:07:43,766 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:43,766 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:43,767 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:43,767 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:43,768 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:43,768 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:43,768 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:43,768 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:43,768 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:43,768 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:43,769 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:43,769 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:43,769 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:43,769 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:43,808 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:43,808 DEBUG: Start:	 Training
+2016-09-06 10:07:43,810 DEBUG: Info:	 Time for Training: 0.0434219837189[s]
+2016-09-06 10:07:43,810 DEBUG: Done:	 Training
+2016-09-06 10:07:43,810 DEBUG: Start:	 Predicting
+2016-09-06 10:07:43,813 DEBUG: Done:	 Predicting
+2016-09-06 10:07:43,813 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:43,815 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:43,815 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:43,816 INFO: Done:	 Result Analysis
+2016-09-06 10:07:43,825 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:43,826 DEBUG: Start:	 Training
+2016-09-06 10:07:43,831 DEBUG: Info:	 Time for Training: 0.0656042098999[s]
+2016-09-06 10:07:43,831 DEBUG: Done:	 Training
+2016-09-06 10:07:43,831 DEBUG: Start:	 Predicting
+2016-09-06 10:07:43,834 DEBUG: Done:	 Predicting
+2016-09-06 10:07:43,834 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:43,836 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:43,836 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:43,836 INFO: Done:	 Result Analysis
+2016-09-06 10:07:43,916 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:43,916 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:43,916 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:43,916 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:43,916 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:43,916 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:43,917 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:43,917 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:43,917 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:43,917 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:43,917 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:43,917 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:43,918 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:43,918 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:43,951 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:43,951 DEBUG: Start:	 Training
+2016-09-06 10:07:43,952 DEBUG: Info:	 Time for Training: 0.0365099906921[s]
+2016-09-06 10:07:43,952 DEBUG: Done:	 Training
+2016-09-06 10:07:43,952 DEBUG: Start:	 Predicting
+2016-09-06 10:07:43,958 DEBUG: Done:	 Predicting
+2016-09-06 10:07:43,959 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:43,960 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:43,960 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 32
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.619047619048
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:43,960 INFO: Done:	 Result Analysis
+2016-09-06 10:07:44,207 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:44,207 DEBUG: Start:	 Training
+2016-09-06 10:07:44,249 DEBUG: Info:	 Time for Training: 0.333906173706[s]
+2016-09-06 10:07:44,249 DEBUG: Done:	 Training
+2016-09-06 10:07:44,249 DEBUG: Start:	 Predicting
+2016-09-06 10:07:44,255 DEBUG: Done:	 Predicting
+2016-09-06 10:07:44,255 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:44,257 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:44,257 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 16, max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,257 INFO: Done:	 Result Analysis
+2016-09-06 10:07:44,364 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,364 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,364 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:44,364 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:44,365 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:44,365 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:44,365 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:44,365 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:44,366 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:44,366 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:44,366 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:44,366 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:44,366 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,366 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,435 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:44,436 DEBUG: Start:	 Training
+2016-09-06 10:07:44,437 DEBUG: Info:	 Time for Training: 0.0736479759216[s]
+2016-09-06 10:07:44,437 DEBUG: Done:	 Training
+2016-09-06 10:07:44,437 DEBUG: Start:	 Predicting
+2016-09-06 10:07:44,441 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:44,441 DEBUG: Start:	 Training
+2016-09-06 10:07:44,450 DEBUG: Done:	 Predicting
+2016-09-06 10:07:44,450 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:44,453 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:44,453 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,454 INFO: Done:	 Result Analysis
+2016-09-06 10:07:44,468 DEBUG: Info:	 Time for Training: 0.104951143265[s]
+2016-09-06 10:07:44,468 DEBUG: Done:	 Training
+2016-09-06 10:07:44,468 DEBUG: Start:	 Predicting
+2016-09-06 10:07:44,472 DEBUG: Done:	 Predicting
+2016-09-06 10:07:44,472 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:44,473 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:44,473 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.514285714286
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,473 INFO: Done:	 Result Analysis
+2016-09-06 10:07:44,609 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,609 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,609 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:44,609 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:44,609 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:44,609 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:44,610 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:44,610 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:44,610 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:07:44,610 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:44,610 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:07:44,610 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,610 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:44,610 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,657 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:44,657 DEBUG: Start:	 Training
+2016-09-06 10:07:44,663 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:44,663 DEBUG: Start:	 Training
+2016-09-06 10:07:44,675 DEBUG: Info:	 Time for Training: 0.066269159317[s]
+2016-09-06 10:07:44,675 DEBUG: Done:	 Training
+2016-09-06 10:07:44,675 DEBUG: Start:	 Predicting
+2016-09-06 10:07:44,680 DEBUG: Done:	 Predicting
+2016-09-06 10:07:44,681 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:44,682 DEBUG: Info:	 Time for Training: 0.0731539726257[s]
+2016-09-06 10:07:44,682 DEBUG: Done:	 Training
+2016-09-06 10:07:44,682 DEBUG: Start:	 Predicting
+2016-09-06 10:07:44,682 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:44,682 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,682 INFO: Done:	 Result Analysis
+2016-09-06 10:07:44,686 DEBUG: Done:	 Predicting
+2016-09-06 10:07:44,686 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:44,687 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:44,687 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,688 INFO: Done:	 Result Analysis
+2016-09-06 10:07:44,764 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,764 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,764 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:44,764 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:44,764 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:44,764 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:44,766 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:44,766 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:44,766 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:44,766 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:44,766 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:44,766 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:44,766 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,766 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,823 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:44,823 DEBUG: Start:	 Training
+2016-09-06 10:07:44,826 DEBUG: Info:	 Time for Training: 0.0639500617981[s]
+2016-09-06 10:07:44,827 DEBUG: Done:	 Training
+2016-09-06 10:07:44,827 DEBUG: Start:	 Predicting
+2016-09-06 10:07:44,830 DEBUG: Done:	 Predicting
+2016-09-06 10:07:44,831 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:44,832 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:44,833 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,833 INFO: Done:	 Result Analysis
+2016-09-06 10:07:44,841 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:44,841 DEBUG: Start:	 Training
+2016-09-06 10:07:44,845 DEBUG: Info:	 Time for Training: 0.0828351974487[s]
+2016-09-06 10:07:44,845 DEBUG: Done:	 Training
+2016-09-06 10:07:44,846 DEBUG: Start:	 Predicting
+2016-09-06 10:07:44,848 DEBUG: Done:	 Predicting
+2016-09-06 10:07:44,848 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:44,850 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:44,850 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,851 INFO: Done:	 Result Analysis
+2016-09-06 10:07:44,909 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,910 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:44,910 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:44,910 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:44,910 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:44,910 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:44,911 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:44,911 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:44,911 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:44,912 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:44,912 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:44,912 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,912 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:44,912 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:44,945 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:44,946 DEBUG: Start:	 Training
+2016-09-06 10:07:44,946 DEBUG: Info:	 Time for Training: 0.037682056427[s]
+2016-09-06 10:07:44,946 DEBUG: Done:	 Training
+2016-09-06 10:07:44,946 DEBUG: Start:	 Predicting
+2016-09-06 10:07:44,953 DEBUG: Done:	 Predicting
+2016-09-06 10:07:44,954 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:44,955 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:44,955 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 32
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:44,955 INFO: Done:	 Result Analysis
+2016-09-06 10:07:45,240 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:45,240 DEBUG: Start:	 Training
+2016-09-06 10:07:45,287 DEBUG: Info:	 Time for Training: 0.378414154053[s]
+2016-09-06 10:07:45,287 DEBUG: Done:	 Training
+2016-09-06 10:07:45,288 DEBUG: Start:	 Predicting
+2016-09-06 10:07:45,295 DEBUG: Done:	 Predicting
+2016-09-06 10:07:45,295 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:45,296 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:45,296 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 16, max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,296 INFO: Done:	 Result Analysis
+2016-09-06 10:07:45,357 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,358 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:45,358 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:45,359 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,359 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:45,359 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:45,359 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:45,359 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:45,359 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:45,359 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,360 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:45,360 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:45,360 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:45,360 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,411 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:45,411 DEBUG: Start:	 Training
+2016-09-06 10:07:45,412 DEBUG: Info:	 Time for Training: 0.0539691448212[s]
+2016-09-06 10:07:45,412 DEBUG: Done:	 Training
+2016-09-06 10:07:45,412 DEBUG: Start:	 Predicting
+2016-09-06 10:07:45,417 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:45,417 DEBUG: Start:	 Training
+2016-09-06 10:07:45,423 DEBUG: Done:	 Predicting
+2016-09-06 10:07:45,423 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:45,426 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:45,426 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.633333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.619047619048
+		- Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,426 INFO: Done:	 Result Analysis
+2016-09-06 10:07:45,441 DEBUG: Info:	 Time for Training: 0.0843439102173[s]
+2016-09-06 10:07:45,441 DEBUG: Done:	 Training
+2016-09-06 10:07:45,441 DEBUG: Start:	 Predicting
+2016-09-06 10:07:45,445 DEBUG: Done:	 Predicting
+2016-09-06 10:07:45,445 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:45,446 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:45,446 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.528571428571
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,447 INFO: Done:	 Result Analysis
+2016-09-06 10:07:45,509 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,509 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,509 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:45,509 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:45,510 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:45,510 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:45,510 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:45,510 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:07:45,511 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:45,511 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:07:45,511 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:45,511 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:45,511 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,511 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,559 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:45,559 DEBUG: Start:	 Training
+2016-09-06 10:07:45,562 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:45,562 DEBUG: Start:	 Training
+2016-09-06 10:07:45,577 DEBUG: Info:	 Time for Training: 0.0689029693604[s]
+2016-09-06 10:07:45,577 DEBUG: Done:	 Training
+2016-09-06 10:07:45,577 DEBUG: Start:	 Predicting
+2016-09-06 10:07:45,583 DEBUG: Info:	 Time for Training: 0.0745868682861[s]
+2016-09-06 10:07:45,583 DEBUG: Done:	 Training
+2016-09-06 10:07:45,583 DEBUG: Start:	 Predicting
+2016-09-06 10:07:45,583 DEBUG: Done:	 Predicting
+2016-09-06 10:07:45,583 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:45,585 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:45,585 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,585 INFO: Done:	 Result Analysis
+2016-09-06 10:07:45,587 DEBUG: Done:	 Predicting
+2016-09-06 10:07:45,587 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:45,588 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:45,588 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,589 INFO: Done:	 Result Analysis
+2016-09-06 10:07:45,663 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,663 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,664 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:45,664 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:45,664 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:45,664 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:45,665 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:45,665 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:45,665 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:45,665 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:45,666 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:45,666 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:45,666 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,666 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,725 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:45,725 DEBUG: Start:	 Training
+2016-09-06 10:07:45,729 DEBUG: Info:	 Time for Training: 0.0669369697571[s]
+2016-09-06 10:07:45,729 DEBUG: Done:	 Training
+2016-09-06 10:07:45,729 DEBUG: Start:	 Predicting
+2016-09-06 10:07:45,733 DEBUG: Done:	 Predicting
+2016-09-06 10:07:45,733 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:45,735 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:45,735 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,735 INFO: Done:	 Result Analysis
+2016-09-06 10:07:45,743 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:45,744 DEBUG: Start:	 Training
+2016-09-06 10:07:45,748 DEBUG: Info:	 Time for Training: 0.086000919342[s]
+2016-09-06 10:07:45,748 DEBUG: Done:	 Training
+2016-09-06 10:07:45,748 DEBUG: Start:	 Predicting
+2016-09-06 10:07:45,751 DEBUG: Done:	 Predicting
+2016-09-06 10:07:45,751 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:45,753 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:45,753 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,754 INFO: Done:	 Result Analysis
+2016-09-06 10:07:45,915 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,915 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:45,915 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:45,916 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:45,916 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:45,916 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:45,917 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:45,917 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:45,917 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:45,917 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:45,917 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:45,918 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:45,918 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,918 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:45,967 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:45,967 DEBUG: Start:	 Training
+2016-09-06 10:07:45,968 DEBUG: Info:	 Time for Training: 0.0544729232788[s]
+2016-09-06 10:07:45,968 DEBUG: Done:	 Training
+2016-09-06 10:07:45,968 DEBUG: Start:	 Predicting
+2016-09-06 10:07:45,979 DEBUG: Done:	 Predicting
+2016-09-06 10:07:45,979 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:45,982 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:45,982 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 32
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.62380952381
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:45,982 INFO: Done:	 Result Analysis
+2016-09-06 10:07:46,233 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:46,233 DEBUG: Start:	 Training
+2016-09-06 10:07:46,276 DEBUG: Info:	 Time for Training: 0.361407995224[s]
+2016-09-06 10:07:46,276 DEBUG: Done:	 Training
+2016-09-06 10:07:46,276 DEBUG: Start:	 Predicting
+2016-09-06 10:07:46,281 DEBUG: Done:	 Predicting
+2016-09-06 10:07:46,281 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:46,283 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:46,283 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 16, max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,283 INFO: Done:	 Result Analysis
+2016-09-06 10:07:46,361 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,361 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:46,361 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:46,362 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,362 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:46,362 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:46,362 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:46,362 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:46,362 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:46,362 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,362 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:46,363 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:46,363 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:46,363 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,413 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:46,414 DEBUG: Start:	 Training
+2016-09-06 10:07:46,415 DEBUG: Info:	 Time for Training: 0.0534558296204[s]
+2016-09-06 10:07:46,415 DEBUG: Done:	 Training
+2016-09-06 10:07:46,415 DEBUG: Start:	 Predicting
+2016-09-06 10:07:46,416 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:46,416 DEBUG: Start:	 Training
+2016-09-06 10:07:46,437 DEBUG: Done:	 Predicting
+2016-09-06 10:07:46,437 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:46,437 DEBUG: Info:	 Time for Training: 0.077164888382[s]
+2016-09-06 10:07:46,437 DEBUG: Done:	 Training
+2016-09-06 10:07:46,438 DEBUG: Start:	 Predicting
+2016-09-06 10:07:46,438 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:46,439 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,439 INFO: Done:	 Result Analysis
+2016-09-06 10:07:46,442 DEBUG: Done:	 Predicting
+2016-09-06 10:07:46,442 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:46,443 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:46,443 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.433333333333
+accuracy_score on test : 0.377777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.433333333333
+		- Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,443 INFO: Done:	 Result Analysis
+2016-09-06 10:07:46,505 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,505 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:07:46,505 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:46,505 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,506 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:07:46,506 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:46,506 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:46,506 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:46,507 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:46,507 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:07:46,507 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,507 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:07:46,507 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:46,507 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,556 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:46,557 DEBUG: Start:	 Training
+2016-09-06 10:07:46,561 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:46,562 DEBUG: Start:	 Training
+2016-09-06 10:07:46,575 DEBUG: Info:	 Time for Training: 0.0704419612885[s]
+2016-09-06 10:07:46,575 DEBUG: Done:	 Training
+2016-09-06 10:07:46,575 DEBUG: Start:	 Predicting
+2016-09-06 10:07:46,581 DEBUG: Done:	 Predicting
+2016-09-06 10:07:46,581 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:46,583 DEBUG: Info:	 Time for Training: 0.0788052082062[s]
+2016-09-06 10:07:46,583 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:46,583 DEBUG: Done:	 Training
+2016-09-06 10:07:46,583 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,583 DEBUG: Start:	 Predicting
+2016-09-06 10:07:46,583 INFO: Done:	 Result Analysis
+2016-09-06 10:07:46,588 DEBUG: Done:	 Predicting
+2016-09-06 10:07:46,588 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:46,589 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:46,589 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,589 INFO: Done:	 Result Analysis
+2016-09-06 10:07:46,653 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,653 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,654 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:07:46,654 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:07:46,654 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:46,654 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:46,655 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:46,655 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:46,655 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:46,655 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:46,655 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:46,655 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:46,655 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,655 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,709 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:46,709 DEBUG: Start:	 Training
+2016-09-06 10:07:46,712 DEBUG: Info:	 Time for Training: 0.0588240623474[s]
+2016-09-06 10:07:46,712 DEBUG: Done:	 Training
+2016-09-06 10:07:46,712 DEBUG: Start:	 Predicting
+2016-09-06 10:07:46,715 DEBUG: Done:	 Predicting
+2016-09-06 10:07:46,716 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:46,718 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:46,718 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,718 INFO: Done:	 Result Analysis
+2016-09-06 10:07:46,727 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:46,727 DEBUG: Start:	 Training
+2016-09-06 10:07:46,731 DEBUG: Info:	 Time for Training: 0.0781581401825[s]
+2016-09-06 10:07:46,731 DEBUG: Done:	 Training
+2016-09-06 10:07:46,731 DEBUG: Start:	 Predicting
+2016-09-06 10:07:46,734 DEBUG: Done:	 Predicting
+2016-09-06 10:07:46,734 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:46,736 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:46,736 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,736 INFO: Done:	 Result Analysis
+2016-09-06 10:07:46,802 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,803 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:07:46,802 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:46,803 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:46,803 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:07:46,803 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:46,804 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:46,804 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:46,804 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:46,804 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:46,804 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:46,804 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:46,804 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,804 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:46,837 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:46,837 DEBUG: Start:	 Training
+2016-09-06 10:07:46,838 DEBUG: Info:	 Time for Training: 0.0363059043884[s]
+2016-09-06 10:07:46,838 DEBUG: Done:	 Training
+2016-09-06 10:07:46,838 DEBUG: Start:	 Predicting
+2016-09-06 10:07:46,844 DEBUG: Done:	 Predicting
+2016-09-06 10:07:46,845 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:46,846 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:46,846 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 32
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.580952380952
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:46,846 INFO: Done:	 Result Analysis
+2016-09-06 10:07:47,123 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:47,123 DEBUG: Start:	 Training
+2016-09-06 10:07:47,170 DEBUG: Info:	 Time for Training: 0.368059158325[s]
+2016-09-06 10:07:47,170 DEBUG: Done:	 Training
+2016-09-06 10:07:47,170 DEBUG: Start:	 Predicting
+2016-09-06 10:07:47,175 DEBUG: Done:	 Predicting
+2016-09-06 10:07:47,176 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:47,177 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:47,177 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 16, max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:47,177 INFO: Done:	 Result Analysis
+2016-09-06 10:07:47,249 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:47,249 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:07:47,249 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:07:47,249 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:07:47,249 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:47,249 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:07:47,250 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:47,250 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:07:47,250 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:47,250 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:07:47,250 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:47,250 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:07:47,250 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:47,251 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:07:47,297 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:47,297 DEBUG: Start:	 Training
+2016-09-06 10:07:47,298 DEBUG: Info:	 Time for Training: 0.049859046936[s]
+2016-09-06 10:07:47,298 DEBUG: Done:	 Training
+2016-09-06 10:07:47,298 DEBUG: Start:	 Predicting
+2016-09-06 10:07:47,305 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:07:47,305 DEBUG: Start:	 Training
+2016-09-06 10:07:47,324 DEBUG: Info:	 Time for Training: 0.0752458572388[s]
+2016-09-06 10:07:47,324 DEBUG: Done:	 Training
+2016-09-06 10:07:47,324 DEBUG: Start:	 Predicting
+2016-09-06 10:07:47,325 DEBUG: Done:	 Predicting
+2016-09-06 10:07:47,325 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:47,327 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:47,327 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:47,327 INFO: Done:	 Result Analysis
+2016-09-06 10:07:47,327 DEBUG: Done:	 Predicting
+2016-09-06 10:07:47,327 DEBUG: Start:	 Getting Results
+2016-09-06 10:07:47,329 DEBUG: Done:	 Getting Results
+2016-09-06 10:07:47,329 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.490476190476
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:07:47,329 INFO: Done:	 Result Analysis
+2016-09-06 10:07:47,543 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:07:47,544 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:07:47,544 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:07:47,545 INFO: Info:	 Shape of View0 :(300, 13)
+2016-09-06 10:07:47,545 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:07:47,546 INFO: Info:	 Shape of View1 :(300, 17)
+2016-09-06 10:07:47,546 INFO: Info:	 Shape of View0 :(300, 13)
+2016-09-06 10:07:47,547 INFO: Info:	 Shape of View2 :(300, 20)
+2016-09-06 10:07:47,547 INFO: Info:	 Shape of View1 :(300, 17)
+2016-09-06 10:07:47,547 INFO: Info:	 Shape of View2 :(300, 20)
+2016-09-06 10:07:47,547 INFO: Info:	 Shape of View3 :(300, 10)
+2016-09-06 10:07:47,548 INFO: Done:	 Read Database Files
+2016-09-06 10:07:47,548 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:07:47,548 INFO: Info:	 Shape of View3 :(300, 10)
+2016-09-06 10:07:47,548 INFO: Done:	 Read Database Files
+2016-09-06 10:07:47,549 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:07:47,553 INFO: Done:	 Determine validation split
+2016-09-06 10:07:47,553 INFO: Start:	 Determine 5 folds
+2016-09-06 10:07:47,553 INFO: Done:	 Determine validation split
+2016-09-06 10:07:47,553 INFO: Start:	 Determine 5 folds
+2016-09-06 10:07:47,560 INFO: Info:	 Length of Learning Sets: 170
+2016-09-06 10:07:47,560 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 10:07:47,560 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:07:47,560 INFO: Done:	 Determine folds
+2016-09-06 10:07:47,560 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:07:47,561 INFO: Start:	 Classification
+2016-09-06 10:07:47,561 INFO: Info:	 Length of Learning Sets: 170
+2016-09-06 10:07:47,561 INFO: 	Start:	 Fold number 1
+2016-09-06 10:07:47,561 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 10:07:47,561 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:07:47,561 INFO: Done:	 Determine folds
+2016-09-06 10:07:47,561 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:07:47,561 INFO: Start:	 Classification
+2016-09-06 10:07:47,561 INFO: 	Start:	 Fold number 1
+2016-09-06 10:07:47,598 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:07:47,609 DEBUG: 			View 0 : 0.497175141243
+2016-09-06 10:07:47,616 DEBUG: 			View 1 : 0.480225988701
+2016-09-06 10:07:47,625 DEBUG: 			View 2 : 0.570621468927
+2016-09-06 10:07:47,632 DEBUG: 			View 3 : 0.570621468927
+2016-09-06 10:07:47,672 DEBUG: 			 Best view : 		View2
+2016-09-06 10:07:47,759 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:07:47,767 DEBUG: 			View 0 : 0.71186440678
+2016-09-06 10:07:47,778 DEBUG: 			View 1 : 0.717514124294
+2016-09-06 10:07:47,788 DEBUG: 			View 2 : 0.706214689266
+2016-09-06 10:07:47,798 DEBUG: 			View 3 : 0.745762711864
+2016-09-06 10:07:47,844 DEBUG: 			 Best view : 		View3
+2016-09-06 10:07:48,124 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:07:48,132 DEBUG: 			View 0 : 0.71186440678
+2016-09-06 10:07:48,140 DEBUG: 			View 1 : 0.717514124294
+2016-09-06 10:07:48,148 DEBUG: 			View 2 : 0.706214689266
+2016-09-06 10:07:48,155 DEBUG: 			View 3 : 0.745762711864
+2016-09-06 10:07:48,196 DEBUG: 			 Best view : 		View3
+2016-09-06 10:07:48,423 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:07:48,430 DEBUG: 			View 0 : 0.683615819209
+2016-09-06 10:07:48,438 DEBUG: 			View 1 : 0.677966101695
+2016-09-06 10:07:48,446 DEBUG: 			View 2 : 0.666666666667
+2016-09-06 10:07:48,453 DEBUG: 			View 3 : 0.706214689266
+2016-09-06 10:07:48,497 DEBUG: 			 Best view : 		View3
+2016-09-06 10:07:48,779 INFO: 	Start: 	 Classification
+2016-09-06 10:07:49,244 INFO: 	Done: 	 Fold number 1
+2016-09-06 10:07:49,244 INFO: 	Start:	 Fold number 2
+2016-09-06 10:07:49,274 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:07:49,285 DEBUG: 			View 0 : 0.508287292818
+2016-09-06 10:07:49,301 DEBUG: 			View 1 : 0.563535911602
+2016-09-06 10:07:49,309 DEBUG: 			View 2 : 0.497237569061
+2016-09-06 10:07:49,316 DEBUG: 			View 3 : 0.519337016575
+2016-09-06 10:07:49,348 DEBUG: 			 Best view : 		View0
+2016-09-06 10:07:49,429 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:07:49,436 DEBUG: 			View 0 : 0.707182320442
+2016-09-06 10:07:49,444 DEBUG: 			View 1 : 0.71270718232
+2016-09-06 10:07:49,451 DEBUG: 			View 2 : 0.723756906077
+2016-09-06 10:07:49,458 DEBUG: 			View 3 : 0.657458563536
+2016-09-06 10:07:49,497 DEBUG: 			 Best view : 		View2
+2016-09-06 10:07:49,646 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:07:49,654 DEBUG: 			View 0 : 0.707182320442
+2016-09-06 10:07:49,661 DEBUG: 			View 1 : 0.71270718232
+2016-09-06 10:07:49,669 DEBUG: 			View 2 : 0.723756906077
+2016-09-06 10:07:49,675 DEBUG: 			View 3 : 0.657458563536
+2016-09-06 10:07:49,717 DEBUG: 			 Best view : 		View2
+2016-09-06 10:07:49,935 INFO: 	Start: 	 Classification
+2016-09-06 10:07:50,288 INFO: 	Done: 	 Fold number 2
+2016-09-06 10:07:50,289 INFO: 	Start:	 Fold number 3
+2016-09-06 10:07:50,319 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:07:50,327 DEBUG: 			View 0 : 0.481081081081
+2016-09-06 10:07:50,334 DEBUG: 			View 1 : 0.491891891892
+2016-09-06 10:07:50,342 DEBUG: 			View 2 : 0.47027027027
+2016-09-06 10:07:50,349 DEBUG: 			View 3 : 0.508108108108
+2016-09-06 10:07:50,382 DEBUG: 			 Best view : 		View3
+2016-09-06 10:07:50,463 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:07:50,471 DEBUG: 			View 0 : 0.713513513514
+2016-09-06 10:07:50,478 DEBUG: 			View 1 : 0.702702702703
+2016-09-06 10:07:50,486 DEBUG: 			View 2 : 0.675675675676
+2016-09-06 10:07:50,493 DEBUG: 			View 3 : 0.654054054054
+2016-09-06 10:07:50,533 DEBUG: 			 Best view : 		View0
+2016-09-06 10:07:50,686 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:07:50,694 DEBUG: 			View 0 : 0.713513513514
+2016-09-06 10:07:50,701 DEBUG: 			View 1 : 0.702702702703
+2016-09-06 10:07:50,709 DEBUG: 			View 2 : 0.675675675676
+2016-09-06 10:07:50,716 DEBUG: 			View 3 : 0.654054054054
+2016-09-06 10:07:50,759 DEBUG: 			 Best view : 		View0
+2016-09-06 10:07:50,981 INFO: 	Start: 	 Classification
+2016-09-06 10:07:51,339 INFO: 	Done: 	 Fold number 3
+2016-09-06 10:07:51,340 INFO: 	Start:	 Fold number 4
+2016-09-06 10:07:51,369 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:07:51,376 DEBUG: 			View 0 : 0.483333333333
+2016-09-06 10:07:51,382 DEBUG: 			View 1 : 0.483333333333
+2016-09-06 10:07:51,389 DEBUG: 			View 2 : 0.483333333333
+2016-09-06 10:07:51,395 DEBUG: 			View 3 : 0.483333333333
+2016-09-06 10:07:51,396 WARNING: 		WARNING:	All bad for iteration 0
+2016-09-06 10:07:51,428 DEBUG: 			 Best view : 		View0
+2016-09-06 10:07:51,507 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:07:51,515 DEBUG: 			View 0 : 0.738888888889
+2016-09-06 10:07:51,522 DEBUG: 			View 1 : 0.744444444444
+2016-09-06 10:07:51,530 DEBUG: 			View 2 : 0.761111111111
+2016-09-06 10:07:51,537 DEBUG: 			View 3 : 0.711111111111
+2016-09-06 10:07:51,575 DEBUG: 			 Best view : 		View2
+2016-09-06 10:07:51,724 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:07:51,732 DEBUG: 			View 0 : 0.738888888889
+2016-09-06 10:07:51,739 DEBUG: 			View 1 : 0.744444444444
+2016-09-06 10:07:51,746 DEBUG: 			View 2 : 0.761111111111
+2016-09-06 10:07:51,754 DEBUG: 			View 3 : 0.711111111111
+2016-09-06 10:07:51,795 DEBUG: 			 Best view : 		View2
+2016-09-06 10:07:52,011 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:07:52,018 DEBUG: 			View 0 : 0.677777777778
+2016-09-06 10:07:52,026 DEBUG: 			View 1 : 0.711111111111
+2016-09-06 10:07:52,033 DEBUG: 			View 2 : 0.694444444444
+2016-09-06 10:07:52,040 DEBUG: 			View 3 : 0.661111111111
+2016-09-06 10:07:52,084 DEBUG: 			 Best view : 		View0
+2016-09-06 10:07:52,368 INFO: 	Start: 	 Classification
+2016-09-06 10:07:52,836 INFO: 	Done: 	 Fold number 4
+2016-09-06 10:07:52,837 INFO: 	Start:	 Fold number 5
+2016-09-06 10:07:52,867 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:07:52,874 DEBUG: 			View 0 : 0.53591160221
+2016-09-06 10:07:52,881 DEBUG: 			View 1 : 0.563535911602
+2016-09-06 10:07:52,888 DEBUG: 			View 2 : 0.552486187845
+2016-09-06 10:07:52,895 DEBUG: 			View 3 : 0.513812154696
+2016-09-06 10:07:52,929 DEBUG: 			 Best view : 		View3
+2016-09-06 10:07:53,013 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:07:53,021 DEBUG: 			View 0 : 0.690607734807
+2016-09-06 10:07:53,029 DEBUG: 			View 1 : 0.767955801105
+2016-09-06 10:07:53,036 DEBUG: 			View 2 : 0.696132596685
+2016-09-06 10:07:53,044 DEBUG: 			View 3 : 0.662983425414
+2016-09-06 10:07:53,084 DEBUG: 			 Best view : 		View1
+2016-09-06 10:07:53,237 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:07:53,244 DEBUG: 			View 0 : 0.690607734807
+2016-09-06 10:07:53,252 DEBUG: 			View 1 : 0.767955801105
+2016-09-06 10:07:53,259 DEBUG: 			View 2 : 0.696132596685
+2016-09-06 10:07:53,267 DEBUG: 			View 3 : 0.662983425414
+2016-09-06 10:07:53,311 DEBUG: 			 Best view : 		View1
+2016-09-06 10:07:53,532 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:07:53,539 DEBUG: 			View 0 : 0.624309392265
+2016-09-06 10:07:53,547 DEBUG: 			View 1 : 0.596685082873
+2016-09-06 10:07:53,554 DEBUG: 			View 2 : 0.723756906077
+2016-09-06 10:07:53,562 DEBUG: 			View 3 : 0.640883977901
+2016-09-06 10:07:53,606 DEBUG: 			 Best view : 		View2
+2016-09-06 10:07:53,901 INFO: 	Start: 	 Classification
+2016-09-06 10:07:54,384 INFO: 	Done: 	 Fold number 5
+2016-09-06 10:07:54,385 INFO: Done:	 Classification
+2016-09-06 10:07:54,385 INFO: Info:	 Time for Classification: 6[s]
+2016-09-06 10:07:54,385 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6e2ae2224a16dd049e4446d63b6405d70a387c83
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5fc348df1e7bb9c2247ada4335e38e97dc5054f4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..10de1910a53aee4e4eabc5cdfa2c04f8813c5175
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 32
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.619047619048
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ecebfbe89e65c68e8fd927a4e2320c5e3cb1073f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3476de3b859c640a3f5529b502e836cc94b2ef27
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..03ccc8e3c417ca714ae780c01bb055ba12b84419
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 32
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69ac2b1ba6cd9fdcad4efd5202dfd26c0f1f93e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 16, max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6743e8cca0afdeb2d257ec57bd6cbcea3871e1bc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9d769aa7607d81bf68ae335cf360638bf3b513c8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.514285714286
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6b7bdbacf5b89e95fbed66bbbea6949ae2106ccb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..97799939f6607020b193dd57c58c66d06c096d82
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b51560b4410c86f618871f07250cc5c018f95e1e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..94513afce48612fc0e66480c41da85f49c291e93
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a44df1d68f37d51362df04e1af77ed5e6d469f2b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 32
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.62380952381
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6133fdbfedadd020cda19c650640bbda60f48b34
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 16, max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..268dc6e313936df6e79bfa7dbf70a5cd721a2836
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.633333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.619047619048
+		- Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d2acb1806d89c70169a561ee253b0caa48a1f0d6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.528571428571
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..57b1b4cd42acf685d28c8f1e6dfabef4d5c4faf9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..622583f8131f042f7f9e6d9ec4f9eba5e86d0206
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..922eef966ef4b3ce6db161dc995c24b9044f9b22
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4c6b0f496a4077d09f6943b867607f921a2c405b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a96150395083266085a41d98b78ee3a5d068b123
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 32
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.580952380952
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6e0fa905be1eafa6afecde10e2ae6dac1fc18745
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 16, max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6352e1f5f81c126ab169137ad676f54b2bdb1940
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f9f92f1b306952b3a9bf23d18ec8d9f21af11678
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.433333333333
+accuracy_score on test : 0.377777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.433333333333
+		- Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cd43ab9430eb33e254e5d3f44a3eaca3aee30244
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..253cd1cb9be831390be2eed8653a42ca001a54db
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..34a64102eb6c091efd10e83d3c9e4023d835ad33
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 16, max_depth : 25
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..54bf6a98c46470824e8a2b1bfc5e7a514ccdf3db
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fb0c6265019d9187e0ff5ac54e9c484ee212abb2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 8318
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.490476190476
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..ccabbd2f06cc65414b5bf4edf84c1681ae807508
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1370 @@
+2016-09-06 10:08:34,156 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:08:34,156 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00014978125 Gbytes /!\ 
+2016-09-06 10:08:39,164 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:08:39,166 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:08:39,222 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,222 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,222 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:39,222 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:39,222 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:39,222 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:39,223 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,223 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,223 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,223 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:39,223 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,223 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,223 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:39,223 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,265 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:39,265 DEBUG: Start:	 Training
+2016-09-06 10:08:39,268 DEBUG: Info:	 Time for Training: 0.0466451644897[s]
+2016-09-06 10:08:39,268 DEBUG: Done:	 Training
+2016-09-06 10:08:39,268 DEBUG: Start:	 Predicting
+2016-09-06 10:08:39,271 DEBUG: Done:	 Predicting
+2016-09-06 10:08:39,271 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:39,272 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:39,272 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,273 INFO: Done:	 Result Analysis
+2016-09-06 10:08:39,287 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:39,287 DEBUG: Start:	 Training
+2016-09-06 10:08:39,292 DEBUG: Info:	 Time for Training: 0.0710310935974[s]
+2016-09-06 10:08:39,292 DEBUG: Done:	 Training
+2016-09-06 10:08:39,292 DEBUG: Start:	 Predicting
+2016-09-06 10:08:39,295 DEBUG: Done:	 Predicting
+2016-09-06 10:08:39,296 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:39,298 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:39,298 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,298 INFO: Done:	 Result Analysis
+2016-09-06 10:08:39,371 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,372 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:39,372 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:39,372 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,372 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:39,372 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:39,372 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,372 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,372 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,373 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:39,373 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,373 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:39,373 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,373 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,410 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:39,410 DEBUG: Start:	 Training
+2016-09-06 10:08:39,411 DEBUG: Info:	 Time for Training: 0.039687871933[s]
+2016-09-06 10:08:39,411 DEBUG: Done:	 Training
+2016-09-06 10:08:39,411 DEBUG: Start:	 Predicting
+2016-09-06 10:08:39,418 DEBUG: Done:	 Predicting
+2016-09-06 10:08:39,418 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:39,420 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:39,420 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,420 INFO: Done:	 Result Analysis
+2016-09-06 10:08:39,655 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:39,655 DEBUG: Start:	 Training
+2016-09-06 10:08:39,694 DEBUG: Info:	 Time for Training: 0.322620153427[s]
+2016-09-06 10:08:39,694 DEBUG: Done:	 Training
+2016-09-06 10:08:39,694 DEBUG: Start:	 Predicting
+2016-09-06 10:08:39,700 DEBUG: Done:	 Predicting
+2016-09-06 10:08:39,700 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:39,701 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:39,701 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 14, max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,702 INFO: Done:	 Result Analysis
+2016-09-06 10:08:39,821 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,822 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:39,822 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:39,822 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,822 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:39,822 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:39,823 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,823 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,823 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:39,823 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,823 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,823 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,823 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:39,823 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,869 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:39,869 DEBUG: Start:	 Training
+2016-09-06 10:08:39,870 DEBUG: Info:	 Time for Training: 0.0494568347931[s]
+2016-09-06 10:08:39,870 DEBUG: Done:	 Training
+2016-09-06 10:08:39,870 DEBUG: Start:	 Predicting
+2016-09-06 10:08:39,881 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:39,881 DEBUG: Start:	 Training
+2016-09-06 10:08:39,897 DEBUG: Done:	 Predicting
+2016-09-06 10:08:39,897 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:39,899 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:39,899 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,899 INFO: Done:	 Result Analysis
+2016-09-06 10:08:39,903 DEBUG: Info:	 Time for Training: 0.0822620391846[s]
+2016-09-06 10:08:39,904 DEBUG: Done:	 Training
+2016-09-06 10:08:39,904 DEBUG: Start:	 Predicting
+2016-09-06 10:08:39,907 DEBUG: Done:	 Predicting
+2016-09-06 10:08:39,907 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:39,908 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:39,909 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.52380952381
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:39,909 INFO: Done:	 Result Analysis
+2016-09-06 10:08:39,972 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,972 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:39,972 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:08:39,972 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:08:39,973 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:39,973 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:39,973 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,973 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:08:39,974 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,974 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:08:39,974 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:39,974 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:39,974 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:39,974 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,022 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:40,022 DEBUG: Start:	 Training
+2016-09-06 10:08:40,026 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:40,026 DEBUG: Start:	 Training
+2016-09-06 10:08:40,039 DEBUG: Info:	 Time for Training: 0.0676848888397[s]
+2016-09-06 10:08:40,039 DEBUG: Done:	 Training
+2016-09-06 10:08:40,039 DEBUG: Start:	 Predicting
+2016-09-06 10:08:40,043 DEBUG: Info:	 Time for Training: 0.0717689990997[s]
+2016-09-06 10:08:40,043 DEBUG: Done:	 Training
+2016-09-06 10:08:40,043 DEBUG: Start:	 Predicting
+2016-09-06 10:08:40,045 DEBUG: Done:	 Predicting
+2016-09-06 10:08:40,045 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:40,047 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:40,047 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,047 INFO: Done:	 Result Analysis
+2016-09-06 10:08:40,047 DEBUG: Done:	 Predicting
+2016-09-06 10:08:40,047 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:40,049 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:40,049 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,049 INFO: Done:	 Result Analysis
+2016-09-06 10:08:40,117 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,117 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,117 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:40,117 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:40,117 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:40,117 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:40,118 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,118 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,118 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,118 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,118 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:40,118 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:40,118 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,118 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,164 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:40,164 DEBUG: Start:	 Training
+2016-09-06 10:08:40,167 DEBUG: Info:	 Time for Training: 0.0504739284515[s]
+2016-09-06 10:08:40,167 DEBUG: Done:	 Training
+2016-09-06 10:08:40,167 DEBUG: Start:	 Predicting
+2016-09-06 10:08:40,170 DEBUG: Done:	 Predicting
+2016-09-06 10:08:40,170 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:40,171 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:40,171 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,171 INFO: Done:	 Result Analysis
+2016-09-06 10:08:40,175 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:40,175 DEBUG: Start:	 Training
+2016-09-06 10:08:40,180 DEBUG: Info:	 Time for Training: 0.0639250278473[s]
+2016-09-06 10:08:40,181 DEBUG: Done:	 Training
+2016-09-06 10:08:40,181 DEBUG: Start:	 Predicting
+2016-09-06 10:08:40,184 DEBUG: Done:	 Predicting
+2016-09-06 10:08:40,184 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:40,186 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:40,186 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,186 INFO: Done:	 Result Analysis
+2016-09-06 10:08:40,268 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,268 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,268 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:40,268 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:40,268 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:40,268 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:40,269 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,269 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,269 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,269 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,269 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:40,269 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:40,270 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,270 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,303 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:40,303 DEBUG: Start:	 Training
+2016-09-06 10:08:40,303 DEBUG: Info:	 Time for Training: 0.0363190174103[s]
+2016-09-06 10:08:40,303 DEBUG: Done:	 Training
+2016-09-06 10:08:40,304 DEBUG: Start:	 Predicting
+2016-09-06 10:08:40,312 DEBUG: Done:	 Predicting
+2016-09-06 10:08:40,312 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:40,313 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:40,313 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,314 INFO: Done:	 Result Analysis
+2016-09-06 10:08:40,540 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:40,540 DEBUG: Start:	 Training
+2016-09-06 10:08:40,580 DEBUG: Info:	 Time for Training: 0.312906980515[s]
+2016-09-06 10:08:40,580 DEBUG: Done:	 Training
+2016-09-06 10:08:40,580 DEBUG: Start:	 Predicting
+2016-09-06 10:08:40,586 DEBUG: Done:	 Predicting
+2016-09-06 10:08:40,586 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:40,587 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:40,587 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 14, max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,587 INFO: Done:	 Result Analysis
+2016-09-06 10:08:40,724 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,724 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,725 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:40,725 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:40,725 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:40,725 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:40,726 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,726 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,726 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,726 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,726 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:40,726 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:40,726 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,726 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,774 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:40,774 DEBUG: Start:	 Training
+2016-09-06 10:08:40,775 DEBUG: Info:	 Time for Training: 0.0510659217834[s]
+2016-09-06 10:08:40,775 DEBUG: Done:	 Training
+2016-09-06 10:08:40,775 DEBUG: Start:	 Predicting
+2016-09-06 10:08:40,779 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:40,779 DEBUG: Start:	 Training
+2016-09-06 10:08:40,801 DEBUG: Done:	 Predicting
+2016-09-06 10:08:40,801 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:40,803 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:40,804 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,804 INFO: Done:	 Result Analysis
+2016-09-06 10:08:40,810 DEBUG: Info:	 Time for Training: 0.0859551429749[s]
+2016-09-06 10:08:40,810 DEBUG: Done:	 Training
+2016-09-06 10:08:40,810 DEBUG: Start:	 Predicting
+2016-09-06 10:08:40,816 DEBUG: Done:	 Predicting
+2016-09-06 10:08:40,816 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:40,818 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:40,818 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:40,818 INFO: Done:	 Result Analysis
+2016-09-06 10:08:40,981 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,981 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:40,981 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:08:40,981 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:08:40,981 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:40,981 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:40,982 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,982 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:40,982 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,982 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:40,982 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:40,982 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:40,982 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:40,982 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,039 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:41,039 DEBUG: Start:	 Training
+2016-09-06 10:08:41,041 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:41,041 DEBUG: Start:	 Training
+2016-09-06 10:08:41,060 DEBUG: Info:	 Time for Training: 0.0792870521545[s]
+2016-09-06 10:08:41,060 DEBUG: Done:	 Training
+2016-09-06 10:08:41,060 DEBUG: Start:	 Predicting
+2016-09-06 10:08:41,062 DEBUG: Info:	 Time for Training: 0.0816400051117[s]
+2016-09-06 10:08:41,062 DEBUG: Done:	 Training
+2016-09-06 10:08:41,062 DEBUG: Start:	 Predicting
+2016-09-06 10:08:41,066 DEBUG: Done:	 Predicting
+2016-09-06 10:08:41,067 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:41,067 DEBUG: Done:	 Predicting
+2016-09-06 10:08:41,067 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:41,068 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:41,068 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,068 INFO: Done:	 Result Analysis
+2016-09-06 10:08:41,069 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:41,069 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,069 INFO: Done:	 Result Analysis
+2016-09-06 10:08:41,127 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,128 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:41,128 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:41,128 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,129 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,129 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:41,129 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,129 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,129 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:41,129 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:41,130 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,130 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,130 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:41,130 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,179 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:41,179 DEBUG: Start:	 Training
+2016-09-06 10:08:41,181 DEBUG: Info:	 Time for Training: 0.0534510612488[s]
+2016-09-06 10:08:41,182 DEBUG: Done:	 Training
+2016-09-06 10:08:41,182 DEBUG: Start:	 Predicting
+2016-09-06 10:08:41,186 DEBUG: Done:	 Predicting
+2016-09-06 10:08:41,186 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:41,188 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:41,188 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,188 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:41,188 DEBUG: Start:	 Training
+2016-09-06 10:08:41,188 INFO: Done:	 Result Analysis
+2016-09-06 10:08:41,193 DEBUG: Info:	 Time for Training: 0.0664410591125[s]
+2016-09-06 10:08:41,193 DEBUG: Done:	 Training
+2016-09-06 10:08:41,194 DEBUG: Start:	 Predicting
+2016-09-06 10:08:41,197 DEBUG: Done:	 Predicting
+2016-09-06 10:08:41,197 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:41,199 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:41,199 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,199 INFO: Done:	 Result Analysis
+2016-09-06 10:08:41,275 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,275 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,276 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:41,276 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:41,276 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:41,276 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:41,276 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,276 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,276 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,276 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,277 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:41,277 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:41,277 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,277 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,312 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:41,312 DEBUG: Start:	 Training
+2016-09-06 10:08:41,313 DEBUG: Info:	 Time for Training: 0.0382509231567[s]
+2016-09-06 10:08:41,313 DEBUG: Done:	 Training
+2016-09-06 10:08:41,313 DEBUG: Start:	 Predicting
+2016-09-06 10:08:41,320 DEBUG: Done:	 Predicting
+2016-09-06 10:08:41,321 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:41,322 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:41,322 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,322 INFO: Done:	 Result Analysis
+2016-09-06 10:08:41,539 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:41,539 DEBUG: Start:	 Training
+2016-09-06 10:08:41,581 DEBUG: Info:	 Time for Training: 0.306406974792[s]
+2016-09-06 10:08:41,581 DEBUG: Done:	 Training
+2016-09-06 10:08:41,581 DEBUG: Start:	 Predicting
+2016-09-06 10:08:41,587 DEBUG: Done:	 Predicting
+2016-09-06 10:08:41,587 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:41,589 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:41,589 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 14, max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,589 INFO: Done:	 Result Analysis
+2016-09-06 10:08:41,723 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,723 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:41,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:41,723 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:41,723 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:41,724 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,724 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,724 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,724 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,725 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:41,725 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:41,725 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,725 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,775 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:41,775 DEBUG: Start:	 Training
+2016-09-06 10:08:41,776 DEBUG: Info:	 Time for Training: 0.0535860061646[s]
+2016-09-06 10:08:41,776 DEBUG: Done:	 Training
+2016-09-06 10:08:41,776 DEBUG: Start:	 Predicting
+2016-09-06 10:08:41,780 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:41,780 DEBUG: Start:	 Training
+2016-09-06 10:08:41,790 DEBUG: Done:	 Predicting
+2016-09-06 10:08:41,790 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:41,793 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:41,793 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,794 INFO: Done:	 Result Analysis
+2016-09-06 10:08:41,807 DEBUG: Info:	 Time for Training: 0.0841271877289[s]
+2016-09-06 10:08:41,807 DEBUG: Done:	 Training
+2016-09-06 10:08:41,807 DEBUG: Start:	 Predicting
+2016-09-06 10:08:41,810 DEBUG: Done:	 Predicting
+2016-09-06 10:08:41,810 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:41,812 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:41,812 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,812 INFO: Done:	 Result Analysis
+2016-09-06 10:08:41,874 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,874 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:41,874 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:08:41,874 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:08:41,874 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:41,874 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:41,875 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,875 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:08:41,875 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,875 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:08:41,875 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:41,875 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:41,876 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,876 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:41,927 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:41,927 DEBUG: Start:	 Training
+2016-09-06 10:08:41,935 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:41,935 DEBUG: Start:	 Training
+2016-09-06 10:08:41,945 DEBUG: Info:	 Time for Training: 0.0712940692902[s]
+2016-09-06 10:08:41,945 DEBUG: Done:	 Training
+2016-09-06 10:08:41,945 DEBUG: Start:	 Predicting
+2016-09-06 10:08:41,951 DEBUG: Done:	 Predicting
+2016-09-06 10:08:41,951 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:41,952 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:41,952 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,953 INFO: Done:	 Result Analysis
+2016-09-06 10:08:41,953 DEBUG: Info:	 Time for Training: 0.079491853714[s]
+2016-09-06 10:08:41,953 DEBUG: Done:	 Training
+2016-09-06 10:08:41,953 DEBUG: Start:	 Predicting
+2016-09-06 10:08:41,957 DEBUG: Done:	 Predicting
+2016-09-06 10:08:41,957 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:41,959 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:41,959 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:41,959 INFO: Done:	 Result Analysis
+2016-09-06 10:08:42,026 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,026 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,026 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:42,026 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:42,027 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:42,027 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:42,027 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,028 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,028 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,028 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,028 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:42,028 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,028 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:42,028 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,066 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:42,066 DEBUG: Start:	 Training
+2016-09-06 10:08:42,067 DEBUG: Info:	 Time for Training: 0.0417790412903[s]
+2016-09-06 10:08:42,068 DEBUG: Done:	 Training
+2016-09-06 10:08:42,068 DEBUG: Start:	 Predicting
+2016-09-06 10:08:42,071 DEBUG: Done:	 Predicting
+2016-09-06 10:08:42,071 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:42,072 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:42,072 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,073 INFO: Done:	 Result Analysis
+2016-09-06 10:08:42,084 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:42,084 DEBUG: Start:	 Training
+2016-09-06 10:08:42,088 DEBUG: Info:	 Time for Training: 0.0621321201324[s]
+2016-09-06 10:08:42,088 DEBUG: Done:	 Training
+2016-09-06 10:08:42,088 DEBUG: Start:	 Predicting
+2016-09-06 10:08:42,091 DEBUG: Done:	 Predicting
+2016-09-06 10:08:42,091 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:42,093 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:42,093 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,093 INFO: Done:	 Result Analysis
+2016-09-06 10:08:42,174 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,174 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,174 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:42,174 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:42,174 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:42,174 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:42,175 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,175 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,175 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,175 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,175 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:42,175 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:42,175 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,175 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,208 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:42,208 DEBUG: Start:	 Training
+2016-09-06 10:08:42,209 DEBUG: Info:	 Time for Training: 0.0357630252838[s]
+2016-09-06 10:08:42,209 DEBUG: Done:	 Training
+2016-09-06 10:08:42,209 DEBUG: Start:	 Predicting
+2016-09-06 10:08:42,215 DEBUG: Done:	 Predicting
+2016-09-06 10:08:42,215 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:42,216 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:42,216 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.509523809524
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.509523809524
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,217 INFO: Done:	 Result Analysis
+2016-09-06 10:08:42,450 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:42,451 DEBUG: Start:	 Training
+2016-09-06 10:08:42,492 DEBUG: Info:	 Time for Training: 0.318718194962[s]
+2016-09-06 10:08:42,492 DEBUG: Done:	 Training
+2016-09-06 10:08:42,492 DEBUG: Start:	 Predicting
+2016-09-06 10:08:42,498 DEBUG: Done:	 Predicting
+2016-09-06 10:08:42,498 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:42,500 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:42,500 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.980952380952
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 14, max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.980952380952
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,500 INFO: Done:	 Result Analysis
+2016-09-06 10:08:42,623 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,624 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:42,624 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:42,625 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,625 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,625 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:42,625 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:42,625 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,626 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:42,626 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:42,627 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:08:42,627 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:08:42,627 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:42,627 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:42,673 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:42,674 DEBUG: Start:	 Training
+2016-09-06 10:08:42,674 DEBUG: Info:	 Time for Training: 0.0515320301056[s]
+2016-09-06 10:08:42,675 DEBUG: Done:	 Training
+2016-09-06 10:08:42,675 DEBUG: Start:	 Predicting
+2016-09-06 10:08:42,682 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:42,683 DEBUG: Start:	 Training
+2016-09-06 10:08:42,690 DEBUG: Done:	 Predicting
+2016-09-06 10:08:42,690 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:42,692 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:42,692 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,693 INFO: Done:	 Result Analysis
+2016-09-06 10:08:42,707 DEBUG: Info:	 Time for Training: 0.0825428962708[s]
+2016-09-06 10:08:42,707 DEBUG: Done:	 Training
+2016-09-06 10:08:42,707 DEBUG: Start:	 Predicting
+2016-09-06 10:08:42,710 DEBUG: Done:	 Predicting
+2016-09-06 10:08:42,710 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:42,711 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:42,712 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.509523809524
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.509523809524
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:42,712 INFO: Done:	 Result Analysis
+2016-09-06 10:08:42,917 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:08:42,918 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:08:42,919 INFO: Info:	 Shape of View0 :(300, 13)
+2016-09-06 10:08:42,919 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:08:42,920 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:08:42,920 INFO: Info:	 Shape of View1 :(300, 19)
+2016-09-06 10:08:42,921 INFO: Info:	 Shape of View0 :(300, 13)
+2016-09-06 10:08:42,921 INFO: Info:	 Shape of View2 :(300, 15)
+2016-09-06 10:08:42,921 INFO: Info:	 Shape of View1 :(300, 19)
+2016-09-06 10:08:42,922 INFO: Info:	 Shape of View3 :(300, 9)
+2016-09-06 10:08:42,922 INFO: Done:	 Read Database Files
+2016-09-06 10:08:42,922 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:08:42,922 INFO: Info:	 Shape of View2 :(300, 15)
+2016-09-06 10:08:42,923 INFO: Info:	 Shape of View3 :(300, 9)
+2016-09-06 10:08:42,923 INFO: Done:	 Read Database Files
+2016-09-06 10:08:42,924 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:08:42,931 INFO: Done:	 Determine validation split
+2016-09-06 10:08:42,931 INFO: Start:	 Determine 5 folds
+2016-09-06 10:08:42,932 INFO: Done:	 Determine validation split
+2016-09-06 10:08:42,932 INFO: Start:	 Determine 5 folds
+2016-09-06 10:08:42,944 INFO: Info:	 Length of Learning Sets: 168
+2016-09-06 10:08:42,945 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:08:42,945 INFO: Info:	 Length of Validation Set: 90
+2016-09-06 10:08:42,945 INFO: Done:	 Determine folds
+2016-09-06 10:08:42,945 INFO: Info:	 Length of Learning Sets: 168
+2016-09-06 10:08:42,945 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:08:42,945 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:08:42,945 INFO: Info:	 Length of Validation Set: 90
+2016-09-06 10:08:42,945 INFO: Start:	 Classification
+2016-09-06 10:08:42,945 INFO: Done:	 Determine folds
+2016-09-06 10:08:42,945 INFO: 	Start:	 Fold number 1
+2016-09-06 10:08:42,946 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:08:42,946 INFO: Start:	 Classification
+2016-09-06 10:08:42,946 INFO: 	Start:	 Fold number 1
+2016-09-06 10:08:43,005 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:08:43,014 DEBUG: 			View 0 : 0.463687150838
+2016-09-06 10:08:43,022 DEBUG: 			View 1 : 0.530726256983
+2016-09-06 10:08:43,029 DEBUG: 			View 2 : 0.541899441341
+2016-09-06 10:08:43,037 DEBUG: 			View 3 : 0.513966480447
+2016-09-06 10:08:43,071 DEBUG: 			 Best view : 		View3
+2016-09-06 10:08:43,154 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:08:43,161 DEBUG: 			View 0 : 0.653631284916
+2016-09-06 10:08:43,170 DEBUG: 			View 1 : 0.720670391061
+2016-09-06 10:08:43,178 DEBUG: 			View 2 : 0.720670391061
+2016-09-06 10:08:43,186 DEBUG: 			View 3 : 0.698324022346
+2016-09-06 10:08:43,226 DEBUG: 			 Best view : 		View1
+2016-09-06 10:08:43,378 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:08:43,394 DEBUG: 			View 0 : 0.653631284916
+2016-09-06 10:08:43,405 DEBUG: 			View 1 : 0.720670391061
+2016-09-06 10:08:43,413 DEBUG: 			View 2 : 0.720670391061
+2016-09-06 10:08:43,420 DEBUG: 			View 3 : 0.698324022346
+2016-09-06 10:08:43,462 DEBUG: 			 Best view : 		View1
+2016-09-06 10:08:43,721 INFO: 	Start: 	 Classification
+2016-09-06 10:08:44,142 INFO: 	Done: 	 Fold number 1
+2016-09-06 10:08:44,143 INFO: 	Start:	 Fold number 2
+2016-09-06 10:08:44,175 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:08:44,183 DEBUG: 			View 0 : 0.530386740331
+2016-09-06 10:08:44,191 DEBUG: 			View 1 : 0.591160220994
+2016-09-06 10:08:44,199 DEBUG: 			View 2 : 0.53591160221
+2016-09-06 10:08:44,206 DEBUG: 			View 3 : 0.491712707182
+2016-09-06 10:08:44,241 DEBUG: 			 Best view : 		View1
+2016-09-06 10:08:44,326 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:08:44,334 DEBUG: 			View 0 : 0.718232044199
+2016-09-06 10:08:44,342 DEBUG: 			View 1 : 0.696132596685
+2016-09-06 10:08:44,350 DEBUG: 			View 2 : 0.674033149171
+2016-09-06 10:08:44,358 DEBUG: 			View 3 : 0.635359116022
+2016-09-06 10:08:44,410 DEBUG: 			 Best view : 		View0
+2016-09-06 10:08:44,581 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:08:44,589 DEBUG: 			View 0 : 0.718232044199
+2016-09-06 10:08:44,596 DEBUG: 			View 1 : 0.696132596685
+2016-09-06 10:08:44,604 DEBUG: 			View 2 : 0.674033149171
+2016-09-06 10:08:44,611 DEBUG: 			View 3 : 0.635359116022
+2016-09-06 10:08:44,652 DEBUG: 			 Best view : 		View0
+2016-09-06 10:08:44,871 INFO: 	Start: 	 Classification
+2016-09-06 10:08:45,230 INFO: 	Done: 	 Fold number 2
+2016-09-06 10:08:45,230 INFO: 	Start:	 Fold number 3
+2016-09-06 10:08:45,259 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:08:45,267 DEBUG: 			View 0 : 0.505617977528
+2016-09-06 10:08:45,274 DEBUG: 			View 1 : 0.522471910112
+2016-09-06 10:08:45,281 DEBUG: 			View 2 : 0.516853932584
+2016-09-06 10:08:45,288 DEBUG: 			View 3 : 0.52808988764
+2016-09-06 10:08:45,320 DEBUG: 			 Best view : 		View1
+2016-09-06 10:08:45,399 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:08:45,406 DEBUG: 			View 0 : 0.629213483146
+2016-09-06 10:08:45,414 DEBUG: 			View 1 : 0.724719101124
+2016-09-06 10:08:45,421 DEBUG: 			View 2 : 0.691011235955
+2016-09-06 10:08:45,429 DEBUG: 			View 3 : 0.629213483146
+2016-09-06 10:08:45,467 DEBUG: 			 Best view : 		View1
+2016-09-06 10:08:45,616 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:08:45,624 DEBUG: 			View 0 : 0.629213483146
+2016-09-06 10:08:45,631 DEBUG: 			View 1 : 0.724719101124
+2016-09-06 10:08:45,639 DEBUG: 			View 2 : 0.691011235955
+2016-09-06 10:08:45,646 DEBUG: 			View 3 : 0.629213483146
+2016-09-06 10:08:45,686 DEBUG: 			 Best view : 		View1
+2016-09-06 10:08:45,910 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:08:45,918 DEBUG: 			View 0 : 0.623595505618
+2016-09-06 10:08:45,926 DEBUG: 			View 1 : 0.662921348315
+2016-09-06 10:08:45,934 DEBUG: 			View 2 : 0.651685393258
+2016-09-06 10:08:45,942 DEBUG: 			View 3 : 0.623595505618
+2016-09-06 10:08:45,986 DEBUG: 			 Best view : 		View0
+2016-09-06 10:08:46,283 INFO: 	Start: 	 Classification
+2016-09-06 10:08:46,944 INFO: 	Done: 	 Fold number 3
+2016-09-06 10:08:46,944 INFO: 	Start:	 Fold number 4
+2016-09-06 10:08:46,983 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:08:46,993 DEBUG: 			View 0 : 0.56043956044
+2016-09-06 10:08:47,001 DEBUG: 			View 1 : 0.521978021978
+2016-09-06 10:08:47,010 DEBUG: 			View 2 : 0.554945054945
+2016-09-06 10:08:47,018 DEBUG: 			View 3 : 0.521978021978
+2016-09-06 10:08:47,055 DEBUG: 			 Best view : 		View1
+2016-09-06 10:08:47,142 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:08:47,150 DEBUG: 			View 0 : 0.67032967033
+2016-09-06 10:08:47,158 DEBUG: 			View 1 : 0.736263736264
+2016-09-06 10:08:47,167 DEBUG: 			View 2 : 0.681318681319
+2016-09-06 10:08:47,175 DEBUG: 			View 3 : 0.714285714286
+2016-09-06 10:08:47,218 DEBUG: 			 Best view : 		View1
+2016-09-06 10:08:47,372 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:08:47,380 DEBUG: 			View 0 : 0.67032967033
+2016-09-06 10:08:47,388 DEBUG: 			View 1 : 0.736263736264
+2016-09-06 10:08:47,395 DEBUG: 			View 2 : 0.681318681319
+2016-09-06 10:08:47,402 DEBUG: 			View 3 : 0.714285714286
+2016-09-06 10:08:47,445 DEBUG: 			 Best view : 		View1
+2016-09-06 10:08:47,666 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:08:47,674 DEBUG: 			View 0 : 0.549450549451
+2016-09-06 10:08:47,682 DEBUG: 			View 1 : 0.763736263736
+2016-09-06 10:08:47,690 DEBUG: 			View 2 : 0.598901098901
+2016-09-06 10:08:47,697 DEBUG: 			View 3 : 0.637362637363
+2016-09-06 10:08:47,741 DEBUG: 			 Best view : 		View1
+2016-09-06 10:08:48,048 INFO: 	Start: 	 Classification
+2016-09-06 10:08:48,536 INFO: 	Done: 	 Fold number 4
+2016-09-06 10:08:48,536 INFO: 	Start:	 Fold number 5
+2016-09-06 10:08:48,569 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:08:48,577 DEBUG: 			View 0 : 0.491712707182
+2016-09-06 10:08:48,584 DEBUG: 			View 1 : 0.491712707182
+2016-09-06 10:08:48,594 DEBUG: 			View 2 : 0.491712707182
+2016-09-06 10:08:48,606 DEBUG: 			View 3 : 0.491712707182
+2016-09-06 10:08:48,606 WARNING: 		WARNING:	All bad for iteration 0
+2016-09-06 10:08:48,657 DEBUG: 			 Best view : 		View0
+2016-09-06 10:08:48,759 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:08:48,767 DEBUG: 			View 0 : 0.651933701657
+2016-09-06 10:08:48,777 DEBUG: 			View 1 : 0.646408839779
+2016-09-06 10:08:48,789 DEBUG: 			View 2 : 0.662983425414
+2016-09-06 10:08:48,803 DEBUG: 			View 3 : 0.591160220994
+2016-09-06 10:08:48,867 DEBUG: 			 Best view : 		View2
+2016-09-06 10:08:49,051 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:08:49,059 DEBUG: 			View 0 : 0.651933701657
+2016-09-06 10:08:49,068 DEBUG: 			View 1 : 0.646408839779
+2016-09-06 10:08:49,077 DEBUG: 			View 2 : 0.662983425414
+2016-09-06 10:08:49,085 DEBUG: 			View 3 : 0.591160220994
+2016-09-06 10:08:49,135 DEBUG: 			 Best view : 		View2
+2016-09-06 10:08:49,386 INFO: 	Start: 	 Classification
+2016-09-06 10:08:49,796 INFO: 	Done: 	 Fold number 5
+2016-09-06 10:08:49,796 INFO: Done:	 Classification
+2016-09-06 10:08:49,796 INFO: Info:	 Time for Classification: 6[s]
+2016-09-06 10:08:49,796 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ddea2b4fd39bb6c4c2ed80705d636b25beadb050
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e1d148804213849085da1aad85b70d19bf641ce0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..87eef941fba29b1e81351b75aca71e8b7f56c161
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..57e7471b5e7e2aae0d180ea8b3846ca91aa80232
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.97619047619
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 14, max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.97619047619
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cbd7b4f18687c43ea0be068574094b718ecce10f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b91d1651b9be39d33df68268f06916acc1a1c4fa
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.52380952381
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d413cf8788eac148163d882e40a015d2ffebd79c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c7edd45ac0c7f27aa769f2b69822dcf8ffc91f9f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d0be9dd2d9652aa66eba0f8d4bf645b7a06a335d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..20848fbcef792a8f17937d0e92496645b82a553f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 14, max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0da527101bbbf8d9166eebb0292bd0caf9a415ab
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d1c231bdd58b9af485d57d92f22b0a4c6b9d218a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d1835abba7246701b78e9d33af4f9a38922da80
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3fd73c43b060e332169de31ecc2dab0adec0d7c2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..45bc125e72aaaf1f3981e925df55124e7ac96a84
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e933492b16bc199a5b6fd23e87752c75e57f360e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..30c528bc06c44cbd5c61ce2c679a9c24515e989f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b47c31af98d014c1fc237c271700817666145c83
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 14, max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7e8ec93402628b196bbcf64625e7344d5b008d41
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5ea49c27fc2bbb6252f777a1cd8aa779e86a2762
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ac93c75fdce8c35872239a20aec519bdb8b1cfcd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..47e6566c7f7e441a25d33f0d9b72a57613494038
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..77221a3d959b55d9ff0b4531881776d371bb87ee
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e0f6b680f800db194027d6c94788cf62282a9bfe
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..917a2d1cbb0ac34c5491ca033dde9fad7b64eadf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.509523809524
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.509523809524
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..84b2e632565bc27430a792444f0a2d46da20265d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.980952380952
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 14, max_depth : 28
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.980952380952
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0fabbd85581ca3c41f967ff2673e1965fd6ffe67
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ba1f9386cc8241bec5d15a96e570be70dbe559c0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.509523809524
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7580
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.509523809524
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..47a28cd4aca7bb1731207a34e8af2fdbee230354
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1363 @@
+2016-09-06 10:08:53,165 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:08:53,165 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.0001193125 Gbytes /!\ 
+2016-09-06 10:08:58,177 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:08:58,179 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:08:58,234 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,234 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,234 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:58,234 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:58,234 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:58,234 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:58,235 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,235 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,235 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,235 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,235 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:58,235 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:58,235 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,235 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,274 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:58,274 DEBUG: Start:	 Training
+2016-09-06 10:08:58,276 DEBUG: Info:	 Time for Training: 0.0428988933563[s]
+2016-09-06 10:08:58,276 DEBUG: Done:	 Training
+2016-09-06 10:08:58,276 DEBUG: Start:	 Predicting
+2016-09-06 10:08:58,279 DEBUG: Done:	 Predicting
+2016-09-06 10:08:58,279 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:58,280 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:58,280 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,281 INFO: Done:	 Result Analysis
+2016-09-06 10:08:58,288 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:58,288 DEBUG: Start:	 Training
+2016-09-06 10:08:58,293 DEBUG: Info:	 Time for Training: 0.0592339038849[s]
+2016-09-06 10:08:58,293 DEBUG: Done:	 Training
+2016-09-06 10:08:58,293 DEBUG: Start:	 Predicting
+2016-09-06 10:08:58,295 DEBUG: Done:	 Predicting
+2016-09-06 10:08:58,296 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:58,298 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:58,298 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,298 INFO: Done:	 Result Analysis
+2016-09-06 10:08:58,378 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,378 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,379 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:58,379 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:58,379 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:58,379 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:58,380 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,380 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,380 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,380 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,380 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:58,380 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:58,380 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,380 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,418 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:58,418 DEBUG: Start:	 Training
+2016-09-06 10:08:58,419 DEBUG: Info:	 Time for Training: 0.0408041477203[s]
+2016-09-06 10:08:58,419 DEBUG: Done:	 Training
+2016-09-06 10:08:58,419 DEBUG: Start:	 Predicting
+2016-09-06 10:08:58,426 DEBUG: Done:	 Predicting
+2016-09-06 10:08:58,426 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:58,428 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:58,428 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 35
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,428 INFO: Done:	 Result Analysis
+2016-09-06 10:08:58,469 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:58,469 DEBUG: Start:	 Training
+2016-09-06 10:08:58,478 DEBUG: Info:	 Time for Training: 0.0997009277344[s]
+2016-09-06 10:08:58,478 DEBUG: Done:	 Training
+2016-09-06 10:08:58,478 DEBUG: Start:	 Predicting
+2016-09-06 10:08:58,481 DEBUG: Done:	 Predicting
+2016-09-06 10:08:58,481 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:58,483 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:58,483 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.890476190476
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,483 INFO: Done:	 Result Analysis
+2016-09-06 10:08:58,633 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,633 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,634 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:58,634 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:58,634 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:58,634 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:58,635 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,635 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,635 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,635 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,635 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:58,635 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:58,635 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,635 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,686 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:58,686 DEBUG: Start:	 Training
+2016-09-06 10:08:58,687 DEBUG: Info:	 Time for Training: 0.0547561645508[s]
+2016-09-06 10:08:58,687 DEBUG: Done:	 Training
+2016-09-06 10:08:58,687 DEBUG: Start:	 Predicting
+2016-09-06 10:08:58,692 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:58,692 DEBUG: Start:	 Training
+2016-09-06 10:08:58,713 DEBUG: Info:	 Time for Training: 0.0805191993713[s]
+2016-09-06 10:08:58,713 DEBUG: Done:	 Training
+2016-09-06 10:08:58,713 DEBUG: Done:	 Predicting
+2016-09-06 10:08:58,713 DEBUG: Start:	 Predicting
+2016-09-06 10:08:58,713 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:58,714 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:58,715 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.661904761905
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.661904761905
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,715 INFO: Done:	 Result Analysis
+2016-09-06 10:08:58,717 DEBUG: Done:	 Predicting
+2016-09-06 10:08:58,717 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:58,718 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:58,719 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,719 INFO: Done:	 Result Analysis
+2016-09-06 10:08:58,777 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,777 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,777 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:08:58,777 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:58,777 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:08:58,777 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:58,778 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,778 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:08:58,778 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,778 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:08:58,778 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:58,778 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:58,778 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,778 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,829 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:58,829 DEBUG: Start:	 Training
+2016-09-06 10:08:58,838 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:58,838 DEBUG: Start:	 Training
+2016-09-06 10:08:58,847 DEBUG: Info:	 Time for Training: 0.0710601806641[s]
+2016-09-06 10:08:58,847 DEBUG: Done:	 Training
+2016-09-06 10:08:58,847 DEBUG: Start:	 Predicting
+2016-09-06 10:08:58,854 DEBUG: Done:	 Predicting
+2016-09-06 10:08:58,854 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:58,855 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:58,855 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,856 INFO: Done:	 Result Analysis
+2016-09-06 10:08:58,861 DEBUG: Info:	 Time for Training: 0.0853440761566[s]
+2016-09-06 10:08:58,861 DEBUG: Done:	 Training
+2016-09-06 10:08:58,862 DEBUG: Start:	 Predicting
+2016-09-06 10:08:58,866 DEBUG: Done:	 Predicting
+2016-09-06 10:08:58,866 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:58,867 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:58,867 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,867 INFO: Done:	 Result Analysis
+2016-09-06 10:08:58,923 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,923 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:58,923 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:58,924 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:58,923 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:58,924 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:58,924 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:58,924 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:58,924 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,924 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:58,925 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:58,925 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:58,925 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:58,925 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:58,960 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:58,961 DEBUG: Start:	 Training
+2016-09-06 10:08:58,963 DEBUG: Info:	 Time for Training: 0.0409200191498[s]
+2016-09-06 10:08:58,963 DEBUG: Done:	 Training
+2016-09-06 10:08:58,963 DEBUG: Start:	 Predicting
+2016-09-06 10:08:58,966 DEBUG: Done:	 Predicting
+2016-09-06 10:08:58,966 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:58,967 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:58,967 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,968 INFO: Done:	 Result Analysis
+2016-09-06 10:08:58,981 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:58,981 DEBUG: Start:	 Training
+2016-09-06 10:08:58,986 DEBUG: Info:	 Time for Training: 0.0627229213715[s]
+2016-09-06 10:08:58,986 DEBUG: Done:	 Training
+2016-09-06 10:08:58,986 DEBUG: Start:	 Predicting
+2016-09-06 10:08:58,989 DEBUG: Done:	 Predicting
+2016-09-06 10:08:58,989 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:58,992 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:58,992 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:58,992 INFO: Done:	 Result Analysis
+2016-09-06 10:08:59,074 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,074 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,075 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:59,075 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:59,075 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:59,075 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:59,075 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,075 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,076 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,076 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,076 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:59,076 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:59,076 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,076 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,108 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:59,108 DEBUG: Start:	 Training
+2016-09-06 10:08:59,109 DEBUG: Info:	 Time for Training: 0.0350739955902[s]
+2016-09-06 10:08:59,109 DEBUG: Done:	 Training
+2016-09-06 10:08:59,109 DEBUG: Start:	 Predicting
+2016-09-06 10:08:59,116 DEBUG: Done:	 Predicting
+2016-09-06 10:08:59,116 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:59,117 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:59,117 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 35
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.561904761905
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,117 INFO: Done:	 Result Analysis
+2016-09-06 10:08:59,170 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:59,170 DEBUG: Start:	 Training
+2016-09-06 10:08:59,178 DEBUG: Info:	 Time for Training: 0.10413312912[s]
+2016-09-06 10:08:59,178 DEBUG: Done:	 Training
+2016-09-06 10:08:59,178 DEBUG: Start:	 Predicting
+2016-09-06 10:08:59,182 DEBUG: Done:	 Predicting
+2016-09-06 10:08:59,182 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:59,184 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:59,184 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.9
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.9
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,184 INFO: Done:	 Result Analysis
+2016-09-06 10:08:59,324 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,324 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,324 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:08:59,324 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:08:59,325 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:59,325 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:59,326 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,326 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,326 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,326 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,326 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:59,326 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:59,326 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,326 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,396 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:59,396 DEBUG: Start:	 Training
+2016-09-06 10:08:59,397 DEBUG: Info:	 Time for Training: 0.074168920517[s]
+2016-09-06 10:08:59,397 DEBUG: Done:	 Training
+2016-09-06 10:08:59,398 DEBUG: Start:	 Predicting
+2016-09-06 10:08:59,403 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:59,403 DEBUG: Start:	 Training
+2016-09-06 10:08:59,421 DEBUG: Done:	 Predicting
+2016-09-06 10:08:59,421 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:59,424 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:59,424 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,424 INFO: Done:	 Result Analysis
+2016-09-06 10:08:59,431 DEBUG: Info:	 Time for Training: 0.107607841492[s]
+2016-09-06 10:08:59,431 DEBUG: Done:	 Training
+2016-09-06 10:08:59,431 DEBUG: Start:	 Predicting
+2016-09-06 10:08:59,434 DEBUG: Done:	 Predicting
+2016-09-06 10:08:59,435 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:59,436 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:59,436 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,436 INFO: Done:	 Result Analysis
+2016-09-06 10:08:59,571 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,571 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,571 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:08:59,571 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:08:59,571 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:59,571 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:59,571 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,571 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:08:59,572 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,572 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:08:59,572 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:59,572 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:59,572 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,572 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,620 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:59,620 DEBUG: Start:	 Training
+2016-09-06 10:08:59,624 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:59,624 DEBUG: Start:	 Training
+2016-09-06 10:08:59,637 DEBUG: Info:	 Time for Training: 0.0667371749878[s]
+2016-09-06 10:08:59,637 DEBUG: Done:	 Training
+2016-09-06 10:08:59,637 DEBUG: Start:	 Predicting
+2016-09-06 10:08:59,643 DEBUG: Done:	 Predicting
+2016-09-06 10:08:59,643 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:59,643 DEBUG: Info:	 Time for Training: 0.0726878643036[s]
+2016-09-06 10:08:59,643 DEBUG: Done:	 Training
+2016-09-06 10:08:59,643 DEBUG: Start:	 Predicting
+2016-09-06 10:08:59,644 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:59,644 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,645 INFO: Done:	 Result Analysis
+2016-09-06 10:08:59,647 DEBUG: Done:	 Predicting
+2016-09-06 10:08:59,647 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:59,649 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:59,649 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,649 INFO: Done:	 Result Analysis
+2016-09-06 10:08:59,723 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:08:59,723 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:59,724 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,724 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:08:59,724 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:59,724 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:08:59,724 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:08:59,724 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:59,725 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:08:59,725 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,725 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:08:59,725 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:59,725 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,756 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:59,756 DEBUG: Start:	 Training
+2016-09-06 10:08:59,757 DEBUG: Info:	 Time for Training: 0.0341780185699[s]
+2016-09-06 10:08:59,757 DEBUG: Done:	 Training
+2016-09-06 10:08:59,757 DEBUG: Start:	 Predicting
+2016-09-06 10:08:59,760 DEBUG: Done:	 Predicting
+2016-09-06 10:08:59,760 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:59,761 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:59,761 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.344444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.344444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,762 INFO: Done:	 Result Analysis
+2016-09-06 10:08:59,771 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:59,771 DEBUG: Start:	 Training
+2016-09-06 10:08:59,774 DEBUG: Info:	 Time for Training: 0.0514109134674[s]
+2016-09-06 10:08:59,774 DEBUG: Done:	 Training
+2016-09-06 10:08:59,774 DEBUG: Start:	 Predicting
+2016-09-06 10:08:59,777 DEBUG: Done:	 Predicting
+2016-09-06 10:08:59,777 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:59,779 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:59,779 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.366666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.366666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,779 INFO: Done:	 Result Analysis
+2016-09-06 10:08:59,875 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,875 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:08:59,876 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:08:59,876 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:08:59,877 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:59,877 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:08:59,878 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:08:59,878 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:08:59,878 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:08:59,878 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:08:59,878 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:59,878 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:08:59,878 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,878 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:08:59,909 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:59,910 DEBUG: Start:	 Training
+2016-09-06 10:08:59,910 DEBUG: Info:	 Time for Training: 0.0356111526489[s]
+2016-09-06 10:08:59,910 DEBUG: Done:	 Training
+2016-09-06 10:08:59,910 DEBUG: Start:	 Predicting
+2016-09-06 10:08:59,916 DEBUG: Done:	 Predicting
+2016-09-06 10:08:59,916 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:59,918 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:59,918 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 35
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,918 INFO: Done:	 Result Analysis
+2016-09-06 10:08:59,962 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:08:59,962 DEBUG: Start:	 Training
+2016-09-06 10:08:59,971 DEBUG: Info:	 Time for Training: 0.0960199832916[s]
+2016-09-06 10:08:59,971 DEBUG: Done:	 Training
+2016-09-06 10:08:59,971 DEBUG: Start:	 Predicting
+2016-09-06 10:08:59,974 DEBUG: Done:	 Predicting
+2016-09-06 10:08:59,974 DEBUG: Start:	 Getting Results
+2016-09-06 10:08:59,975 DEBUG: Done:	 Getting Results
+2016-09-06 10:08:59,976 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.895238095238
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.895238095238
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:08:59,976 INFO: Done:	 Result Analysis
+2016-09-06 10:09:00,126 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,126 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,127 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:09:00,127 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:09:00,127 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:09:00,127 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:09:00,128 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,128 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,128 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,128 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,128 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:09:00,128 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:09:00,128 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,128 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,198 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:09:00,198 DEBUG: Start:	 Training
+2016-09-06 10:09:00,199 DEBUG: Info:	 Time for Training: 0.073979139328[s]
+2016-09-06 10:09:00,199 DEBUG: Done:	 Training
+2016-09-06 10:09:00,199 DEBUG: Start:	 Predicting
+2016-09-06 10:09:00,201 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:09:00,201 DEBUG: Start:	 Training
+2016-09-06 10:09:00,217 DEBUG: Done:	 Predicting
+2016-09-06 10:09:00,217 DEBUG: Start:	 Getting Results
+2016-09-06 10:09:00,217 DEBUG: Info:	 Time for Training: 0.09197306633[s]
+2016-09-06 10:09:00,217 DEBUG: Done:	 Training
+2016-09-06 10:09:00,217 DEBUG: Start:	 Predicting
+2016-09-06 10:09:00,219 DEBUG: Done:	 Getting Results
+2016-09-06 10:09:00,219 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,219 INFO: Done:	 Result Analysis
+2016-09-06 10:09:00,220 DEBUG: Done:	 Predicting
+2016-09-06 10:09:00,220 DEBUG: Start:	 Getting Results
+2016-09-06 10:09:00,221 DEBUG: Done:	 Getting Results
+2016-09-06 10:09:00,222 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,222 INFO: Done:	 Result Analysis
+2016-09-06 10:09:00,375 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,375 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,376 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:09:00,376 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:09:00,376 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:09:00,376 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:09:00,377 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,377 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,377 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,377 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,378 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:09:00,378 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:09:00,378 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,378 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,452 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:09:00,452 DEBUG: Start:	 Training
+2016-09-06 10:09:00,454 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:09:00,454 DEBUG: Start:	 Training
+2016-09-06 10:09:00,472 DEBUG: Info:	 Time for Training: 0.0980281829834[s]
+2016-09-06 10:09:00,472 DEBUG: Done:	 Training
+2016-09-06 10:09:00,472 DEBUG: Start:	 Predicting
+2016-09-06 10:09:00,474 DEBUG: Info:	 Time for Training: 0.100123167038[s]
+2016-09-06 10:09:00,474 DEBUG: Done:	 Training
+2016-09-06 10:09:00,474 DEBUG: Start:	 Predicting
+2016-09-06 10:09:00,475 DEBUG: Done:	 Predicting
+2016-09-06 10:09:00,475 DEBUG: Start:	 Getting Results
+2016-09-06 10:09:00,477 DEBUG: Done:	 Getting Results
+2016-09-06 10:09:00,477 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,477 INFO: Done:	 Result Analysis
+2016-09-06 10:09:00,479 DEBUG: Done:	 Predicting
+2016-09-06 10:09:00,479 DEBUG: Start:	 Getting Results
+2016-09-06 10:09:00,481 DEBUG: Done:	 Getting Results
+2016-09-06 10:09:00,481 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,481 INFO: Done:	 Result Analysis
+2016-09-06 10:09:00,627 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,627 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,627 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:09:00,627 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:09:00,627 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:09:00,627 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:09:00,628 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,628 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,629 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,629 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,629 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:09:00,629 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:09:00,629 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,629 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,678 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:09:00,679 DEBUG: Start:	 Training
+2016-09-06 10:09:00,680 DEBUG: Info:	 Time for Training: 0.054713010788[s]
+2016-09-06 10:09:00,681 DEBUG: Done:	 Training
+2016-09-06 10:09:00,681 DEBUG: Start:	 Predicting
+2016-09-06 10:09:00,684 DEBUG: Done:	 Predicting
+2016-09-06 10:09:00,684 DEBUG: Start:	 Getting Results
+2016-09-06 10:09:00,687 DEBUG: Done:	 Getting Results
+2016-09-06 10:09:00,687 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,687 INFO: Done:	 Result Analysis
+2016-09-06 10:09:00,696 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:09:00,696 DEBUG: Start:	 Training
+2016-09-06 10:09:00,700 DEBUG: Info:	 Time for Training: 0.0740790367126[s]
+2016-09-06 10:09:00,700 DEBUG: Done:	 Training
+2016-09-06 10:09:00,700 DEBUG: Start:	 Predicting
+2016-09-06 10:09:00,703 DEBUG: Done:	 Predicting
+2016-09-06 10:09:00,703 DEBUG: Start:	 Getting Results
+2016-09-06 10:09:00,705 DEBUG: Done:	 Getting Results
+2016-09-06 10:09:00,705 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,705 INFO: Done:	 Result Analysis
+2016-09-06 10:09:00,775 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,775 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:00,775 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:09:00,775 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:09:00,775 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:09:00,775 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:09:00,776 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,776 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:00,776 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,776 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:00,776 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:09:00,776 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:09:00,777 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,777 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:09:00,824 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:09:00,824 DEBUG: Start:	 Training
+2016-09-06 10:09:00,825 DEBUG: Info:	 Time for Training: 0.0511150360107[s]
+2016-09-06 10:09:00,825 DEBUG: Done:	 Training
+2016-09-06 10:09:00,825 DEBUG: Start:	 Predicting
+2016-09-06 10:09:00,834 DEBUG: Done:	 Predicting
+2016-09-06 10:09:00,834 DEBUG: Start:	 Getting Results
+2016-09-06 10:09:00,836 DEBUG: Done:	 Getting Results
+2016-09-06 10:09:00,836 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 35
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.561904761905
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,836 INFO: Done:	 Result Analysis
+2016-09-06 10:09:00,882 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:09:00,882 DEBUG: Start:	 Training
+2016-09-06 10:09:00,891 DEBUG: Info:	 Time for Training: 0.116482019424[s]
+2016-09-06 10:09:00,891 DEBUG: Done:	 Training
+2016-09-06 10:09:00,891 DEBUG: Start:	 Predicting
+2016-09-06 10:09:00,894 DEBUG: Done:	 Predicting
+2016-09-06 10:09:00,894 DEBUG: Start:	 Getting Results
+2016-09-06 10:09:00,895 DEBUG: Done:	 Getting Results
+2016-09-06 10:09:00,895 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.355555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.890476190476
+		- Score on test : 0.355555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:00,896 INFO: Done:	 Result Analysis
+2016-09-06 10:09:01,024 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:01,024 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:09:01,025 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:09:01,025 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:09:01,025 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:09:01,025 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:09:01,026 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:01,026 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:09:01,026 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:01,026 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:09:01,026 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:09:01,026 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:09:01,026 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:09:01,026 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:09:01,095 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:09:01,095 DEBUG: Start:	 Training
+2016-09-06 10:09:01,096 DEBUG: Info:	 Time for Training: 0.072772026062[s]
+2016-09-06 10:09:01,096 DEBUG: Done:	 Training
+2016-09-06 10:09:01,096 DEBUG: Start:	 Predicting
+2016-09-06 10:09:01,098 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:09:01,098 DEBUG: Start:	 Training
+2016-09-06 10:09:01,109 DEBUG: Done:	 Predicting
+2016-09-06 10:09:01,110 DEBUG: Start:	 Getting Results
+2016-09-06 10:09:01,112 DEBUG: Done:	 Getting Results
+2016-09-06 10:09:01,112 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:01,112 INFO: Done:	 Result Analysis
+2016-09-06 10:09:01,118 DEBUG: Info:	 Time for Training: 0.0949590206146[s]
+2016-09-06 10:09:01,119 DEBUG: Done:	 Training
+2016-09-06 10:09:01,119 DEBUG: Start:	 Predicting
+2016-09-06 10:09:01,123 DEBUG: Done:	 Predicting
+2016-09-06 10:09:01,123 DEBUG: Start:	 Getting Results
+2016-09-06 10:09:01,124 DEBUG: Done:	 Getting Results
+2016-09-06 10:09:01,125 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.485714285714
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:09:01,125 INFO: Done:	 Result Analysis
+2016-09-06 10:09:01,423 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:09:01,424 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:09:01,425 INFO: Info:	 Shape of View0 :(300, 19)
+2016-09-06 10:09:01,425 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:09:01,425 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:09:01,426 INFO: Info:	 Shape of View1 :(300, 14)
+2016-09-06 10:09:01,426 INFO: Info:	 Shape of View0 :(300, 19)
+2016-09-06 10:09:01,426 INFO: Info:	 Shape of View2 :(300, 5)
+2016-09-06 10:09:01,427 INFO: Info:	 Shape of View1 :(300, 14)
+2016-09-06 10:09:01,427 INFO: Info:	 Shape of View3 :(300, 5)
+2016-09-06 10:09:01,427 INFO: Done:	 Read Database Files
+2016-09-06 10:09:01,428 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:09:01,428 INFO: Info:	 Shape of View2 :(300, 5)
+2016-09-06 10:09:01,429 INFO: Info:	 Shape of View3 :(300, 5)
+2016-09-06 10:09:01,429 INFO: Done:	 Read Database Files
+2016-09-06 10:09:01,429 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:09:01,436 INFO: Done:	 Determine validation split
+2016-09-06 10:09:01,436 INFO: Start:	 Determine 5 folds
+2016-09-06 10:09:01,436 INFO: Done:	 Determine validation split
+2016-09-06 10:09:01,437 INFO: Start:	 Determine 5 folds
+2016-09-06 10:09:01,450 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:09:01,450 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:09:01,450 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:09:01,450 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:09:01,450 INFO: Done:	 Determine folds
+2016-09-06 10:09:01,450 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:09:01,450 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:09:01,450 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:09:01,450 INFO: Start:	 Classification
+2016-09-06 10:09:01,450 INFO: Done:	 Determine folds
+2016-09-06 10:09:01,451 INFO: 	Start:	 Fold number 1
+2016-09-06 10:09:01,451 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:09:01,451 INFO: Start:	 Classification
+2016-09-06 10:09:01,451 INFO: 	Start:	 Fold number 1
+2016-09-06 10:09:01,514 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:09:01,528 DEBUG: 			View 0 : 0.433333333333
+2016-09-06 10:09:01,541 DEBUG: 			View 1 : 0.577777777778
+2016-09-06 10:09:01,554 DEBUG: 			View 2 : 0.444444444444
+2016-09-06 10:09:01,566 DEBUG: 			View 3 : 0.433333333333
+2016-09-06 10:09:01,621 DEBUG: 			 Best view : 		View0
+2016-09-06 10:09:01,751 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:09:01,765 DEBUG: 			View 0 : 0.75
+2016-09-06 10:09:01,779 DEBUG: 			View 1 : 0.622222222222
+2016-09-06 10:09:01,792 DEBUG: 			View 2 : 0.711111111111
+2016-09-06 10:09:01,805 DEBUG: 			View 3 : 0.683333333333
+2016-09-06 10:09:01,851 DEBUG: 			 Best view : 		View0
+2016-09-06 10:09:02,004 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:09:02,013 DEBUG: 			View 0 : 0.75
+2016-09-06 10:09:02,022 DEBUG: 			View 1 : 0.622222222222
+2016-09-06 10:09:02,030 DEBUG: 			View 2 : 0.711111111111
+2016-09-06 10:09:02,037 DEBUG: 			View 3 : 0.683333333333
+2016-09-06 10:09:02,077 DEBUG: 			 Best view : 		View0
+2016-09-06 10:09:02,295 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:09:02,303 DEBUG: 			View 0 : 0.738888888889
+2016-09-06 10:09:02,310 DEBUG: 			View 1 : 0.666666666667
+2016-09-06 10:09:02,317 DEBUG: 			View 2 : 0.677777777778
+2016-09-06 10:09:02,324 DEBUG: 			View 3 : 0.666666666667
+2016-09-06 10:09:02,367 DEBUG: 			 Best view : 		View0
+2016-09-06 10:09:02,653 INFO: 	Start: 	 Classification
+2016-09-06 10:09:03,124 INFO: 	Done: 	 Fold number 1
+2016-09-06 10:09:03,124 INFO: 	Start:	 Fold number 2
+2016-09-06 10:09:03,155 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:09:03,162 DEBUG: 			View 0 : 0.472826086957
+2016-09-06 10:09:03,169 DEBUG: 			View 1 : 0.559782608696
+2016-09-06 10:09:03,176 DEBUG: 			View 2 : 0.527173913043
+2016-09-06 10:09:03,183 DEBUG: 			View 3 : 0.570652173913
+2016-09-06 10:09:03,216 DEBUG: 			 Best view : 		View2
+2016-09-06 10:09:03,297 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:09:03,305 DEBUG: 			View 0 : 0.695652173913
+2016-09-06 10:09:03,313 DEBUG: 			View 1 : 0.625
+2016-09-06 10:09:03,320 DEBUG: 			View 2 : 0.657608695652
+2016-09-06 10:09:03,327 DEBUG: 			View 3 : 0.657608695652
+2016-09-06 10:09:03,366 DEBUG: 			 Best view : 		View0
+2016-09-06 10:09:03,518 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:09:03,526 DEBUG: 			View 0 : 0.695652173913
+2016-09-06 10:09:03,533 DEBUG: 			View 1 : 0.625
+2016-09-06 10:09:03,540 DEBUG: 			View 2 : 0.657608695652
+2016-09-06 10:09:03,548 DEBUG: 			View 3 : 0.657608695652
+2016-09-06 10:09:03,589 DEBUG: 			 Best view : 		View0
+2016-09-06 10:09:03,812 INFO: 	Start: 	 Classification
+2016-09-06 10:09:04,173 INFO: 	Done: 	 Fold number 2
+2016-09-06 10:09:04,173 INFO: 	Start:	 Fold number 3
+2016-09-06 10:09:04,204 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:09:04,211 DEBUG: 			View 0 : 0.513812154696
+2016-09-06 10:09:04,218 DEBUG: 			View 1 : 0.480662983425
+2016-09-06 10:09:04,225 DEBUG: 			View 2 : 0.441988950276
+2016-09-06 10:09:04,232 DEBUG: 			View 3 : 0.453038674033
+2016-09-06 10:09:04,264 DEBUG: 			 Best view : 		View0
+2016-09-06 10:09:04,345 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:09:04,353 DEBUG: 			View 0 : 0.685082872928
+2016-09-06 10:09:04,360 DEBUG: 			View 1 : 0.662983425414
+2016-09-06 10:09:04,367 DEBUG: 			View 2 : 0.685082872928
+2016-09-06 10:09:04,374 DEBUG: 			View 3 : 0.707182320442
+2016-09-06 10:09:04,413 DEBUG: 			 Best view : 		View3
+2016-09-06 10:09:04,562 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:09:04,569 DEBUG: 			View 0 : 0.685082872928
+2016-09-06 10:09:04,577 DEBUG: 			View 1 : 0.662983425414
+2016-09-06 10:09:04,584 DEBUG: 			View 2 : 0.685082872928
+2016-09-06 10:09:04,591 DEBUG: 			View 3 : 0.707182320442
+2016-09-06 10:09:04,631 DEBUG: 			 Best view : 		View3
+2016-09-06 10:09:04,849 INFO: 	Start: 	 Classification
+2016-09-06 10:09:05,203 INFO: 	Done: 	 Fold number 3
+2016-09-06 10:09:05,203 INFO: 	Start:	 Fold number 4
+2016-09-06 10:09:05,235 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:09:05,242 DEBUG: 			View 0 : 0.491891891892
+2016-09-06 10:09:05,249 DEBUG: 			View 1 : 0.47027027027
+2016-09-06 10:09:05,256 DEBUG: 			View 2 : 0.491891891892
+2016-09-06 10:09:05,263 DEBUG: 			View 3 : 0.513513513514
+2016-09-06 10:09:05,296 DEBUG: 			 Best view : 		View1
+2016-09-06 10:09:05,378 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:09:05,385 DEBUG: 			View 0 : 0.675675675676
+2016-09-06 10:09:05,393 DEBUG: 			View 1 : 0.686486486486
+2016-09-06 10:09:05,400 DEBUG: 			View 2 : 0.681081081081
+2016-09-06 10:09:05,407 DEBUG: 			View 3 : 0.686486486486
+2016-09-06 10:09:05,447 DEBUG: 			 Best view : 		View1
+2016-09-06 10:09:05,600 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:09:05,608 DEBUG: 			View 0 : 0.675675675676
+2016-09-06 10:09:05,615 DEBUG: 			View 1 : 0.686486486486
+2016-09-06 10:09:05,622 DEBUG: 			View 2 : 0.681081081081
+2016-09-06 10:09:05,629 DEBUG: 			View 3 : 0.686486486486
+2016-09-06 10:09:05,671 DEBUG: 			 Best view : 		View1
+2016-09-06 10:09:05,894 INFO: 	Start: 	 Classification
+2016-09-06 10:09:06,257 INFO: 	Done: 	 Fold number 4
+2016-09-06 10:09:06,257 INFO: 	Start:	 Fold number 5
+2016-09-06 10:09:06,287 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:09:06,295 DEBUG: 			View 0 : 0.538888888889
+2016-09-06 10:09:06,302 DEBUG: 			View 1 : 0.461111111111
+2016-09-06 10:09:06,309 DEBUG: 			View 2 : 0.511111111111
+2016-09-06 10:09:06,316 DEBUG: 			View 3 : 0.561111111111
+2016-09-06 10:09:06,348 DEBUG: 			 Best view : 		View0
+2016-09-06 10:09:06,428 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:09:06,435 DEBUG: 			View 0 : 0.722222222222
+2016-09-06 10:09:06,443 DEBUG: 			View 1 : 0.638888888889
+2016-09-06 10:09:06,450 DEBUG: 			View 2 : 0.644444444444
+2016-09-06 10:09:06,457 DEBUG: 			View 3 : 0.611111111111
+2016-09-06 10:09:06,496 DEBUG: 			 Best view : 		View0
+2016-09-06 10:09:06,646 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:09:06,654 DEBUG: 			View 0 : 0.722222222222
+2016-09-06 10:09:06,662 DEBUG: 			View 1 : 0.638888888889
+2016-09-06 10:09:06,669 DEBUG: 			View 2 : 0.644444444444
+2016-09-06 10:09:06,676 DEBUG: 			View 3 : 0.572222222222
+2016-09-06 10:09:06,717 DEBUG: 			 Best view : 		View0
+2016-09-06 10:09:06,935 INFO: 	Start: 	 Classification
+2016-09-06 10:09:07,291 INFO: 	Done: 	 Fold number 5
+2016-09-06 10:09:07,291 INFO: Done:	 Classification
+2016-09-06 10:09:07,291 INFO: Info:	 Time for Classification: 5[s]
+2016-09-06 10:09:07,291 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5340a4ef639fe2995cf7705c0bf13deb6ab05e8e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..401659eecc6936ba15b4ca39f423576b42d93da6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2b148b8aad4023703cabd1448ac4cf01ddff3e3d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 35
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4370daf7770a9f32ca076844c5b91efdf55d95d8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.890476190476
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0f779050eebcc976235b4a74528946db0de884c5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.661904761905
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.661904761905
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bbd4f050e2e82ae726b176ef3492cb13f291679b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ea7ee5a0f2fea10f2251cf4f5711561b6b76c594
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..238e7f55e34780d1fa64e10dfb522beb89dab71a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a919948fddde516c41a22bfff839e855ebd97954
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.366666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.366666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..53b3252bcec8b29327bce446534f0943bba7c4f2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.344444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.344444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..71631804fdd66539c6a272b5e240ce601ee90301
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 35
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5ffc954ff50d55ecf9782cbc87c84301216342d5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.895238095238
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.895238095238
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4a7d3a5e0bd2bfb31c94c971e62e0132c392a503
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..848051bce5bd883118230d62c21e8aa6752b65b9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f77cf58d354c088c8b04133fdb11208c6dde3730
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2282cfc96c6a5e1797718634aaf009f744935826
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7e19a21ff2758f9872633375f16819a3bff5fa94
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0cc72c434373182c1701b24f83794b048379580f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ad6d2c4fcbbfbbacf012c70de103999c16d45682
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 35
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.561904761905
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..02ce1d849a422da19ca504d71d46b408bd4b85db
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.355555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 27
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.890476190476
+		- Score on test : 0.355555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..013f27903cb4bfc07ba1c9b9687821054bc84f44
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2c1b8cde16e70359a02061a983f424c51a9af7de
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..52bd8c30fa9e7afb48ecea23c87f86e96e960b0e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..291885b02da0a103e9096dea90eb4cba10351cdb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..722f6d1c680c217c27a42f057ee8f87bf1d7f286
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..36d5aa61f82bfa35c91c4ee0c386352a79cc9fd3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 830
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.485714285714
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..17ec1f4723b259b9197d17aa811170c3d9005928
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1375 @@
+2016-09-06 10:13:30,568 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:13:30,568 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000133375 Gbytes /!\ 
+2016-09-06 10:13:35,578 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:13:35,581 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:13:35,639 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:35,640 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:35,640 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:13:35,640 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:13:35,640 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:35,640 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:35,641 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:35,641 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:35,641 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:35,641 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:35,641 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:35,641 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:35,641 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:35,641 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:35,678 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:35,678 DEBUG: Start:	 Training
+2016-09-06 10:13:35,680 DEBUG: Info:	 Time for Training: 0.0411851406097[s]
+2016-09-06 10:13:35,680 DEBUG: Done:	 Training
+2016-09-06 10:13:35,680 DEBUG: Start:	 Predicting
+2016-09-06 10:13:35,683 DEBUG: Done:	 Predicting
+2016-09-06 10:13:35,683 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:35,684 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:35,684 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:35,685 INFO: Done:	 Result Analysis
+2016-09-06 10:13:35,692 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:35,692 DEBUG: Start:	 Training
+2016-09-06 10:13:35,696 DEBUG: Info:	 Time for Training: 0.0574531555176[s]
+2016-09-06 10:13:35,696 DEBUG: Done:	 Training
+2016-09-06 10:13:35,696 DEBUG: Start:	 Predicting
+2016-09-06 10:13:35,699 DEBUG: Done:	 Predicting
+2016-09-06 10:13:35,700 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:35,701 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:35,701 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:35,702 INFO: Done:	 Result Analysis
+2016-09-06 10:13:35,786 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:35,786 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:35,786 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:13:35,786 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:35,786 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:13:35,786 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:35,787 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:35,787 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:35,787 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:35,787 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:35,787 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:35,788 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:35,788 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:35,788 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:35,820 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:35,820 DEBUG: Start:	 Training
+2016-09-06 10:13:35,821 DEBUG: Info:	 Time for Training: 0.0358920097351[s]
+2016-09-06 10:13:35,821 DEBUG: Done:	 Training
+2016-09-06 10:13:35,821 DEBUG: Start:	 Predicting
+2016-09-06 10:13:35,828 DEBUG: Done:	 Predicting
+2016-09-06 10:13:35,829 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:35,831 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:35,831 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 48
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:35,831 INFO: Done:	 Result Analysis
+2016-09-06 10:13:35,871 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:35,871 DEBUG: Start:	 Training
+2016-09-06 10:13:35,880 DEBUG: Info:	 Time for Training: 0.094918012619[s]
+2016-09-06 10:13:35,880 DEBUG: Done:	 Training
+2016-09-06 10:13:35,880 DEBUG: Start:	 Predicting
+2016-09-06 10:13:35,883 DEBUG: Done:	 Predicting
+2016-09-06 10:13:35,883 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:35,885 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:35,885 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.9
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.9
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:35,885 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,032 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,032 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,032 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:13:36,032 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:13:36,033 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,033 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,033 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:36,033 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:36,033 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:36,033 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:36,033 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,033 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,034 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,034 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,079 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,079 DEBUG: Start:	 Training
+2016-09-06 10:13:36,080 DEBUG: Info:	 Time for Training: 0.0481998920441[s]
+2016-09-06 10:13:36,080 DEBUG: Done:	 Training
+2016-09-06 10:13:36,080 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,084 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,084 DEBUG: Start:	 Training
+2016-09-06 10:13:36,102 DEBUG: Info:	 Time for Training: 0.070839881897[s]
+2016-09-06 10:13:36,103 DEBUG: Done:	 Training
+2016-09-06 10:13:36,103 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,106 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,106 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,108 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,108 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.490476190476
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,108 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,110 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,111 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,114 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,114 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,115 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,177 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,177 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,178 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:13:36,178 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:13:36,178 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,178 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,178 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:36,178 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:13:36,179 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:36,179 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:13:36,179 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,179 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,179 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,179 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,233 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,233 DEBUG: Start:	 Training
+2016-09-06 10:13:36,235 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,236 DEBUG: Start:	 Training
+2016-09-06 10:13:36,250 DEBUG: Info:	 Time for Training: 0.0732500553131[s]
+2016-09-06 10:13:36,250 DEBUG: Done:	 Training
+2016-09-06 10:13:36,250 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,254 DEBUG: Info:	 Time for Training: 0.0775668621063[s]
+2016-09-06 10:13:36,254 DEBUG: Done:	 Training
+2016-09-06 10:13:36,255 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,256 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,257 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,258 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,258 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,259 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,259 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,259 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,260 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,260 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.990476190476
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,261 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,328 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,328 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:13:36,329 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,330 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,330 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,330 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,330 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,330 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,331 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:13:36,331 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,331 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,332 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,332 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,332 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,372 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,372 DEBUG: Start:	 Training
+2016-09-06 10:13:36,374 DEBUG: Info:	 Time for Training: 0.0452740192413[s]
+2016-09-06 10:13:36,375 DEBUG: Done:	 Training
+2016-09-06 10:13:36,375 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,378 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,378 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,380 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,380 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,381 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,383 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,383 DEBUG: Start:	 Training
+2016-09-06 10:13:36,387 DEBUG: Info:	 Time for Training: 0.0596179962158[s]
+2016-09-06 10:13:36,387 DEBUG: Done:	 Training
+2016-09-06 10:13:36,387 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,390 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,390 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,392 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,392 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,392 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,471 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,471 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,471 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:13:36,471 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:13:36,471 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,471 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,472 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,472 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,472 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,472 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,472 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,472 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,472 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,472 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,505 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,505 DEBUG: Start:	 Training
+2016-09-06 10:13:36,506 DEBUG: Info:	 Time for Training: 0.0356941223145[s]
+2016-09-06 10:13:36,506 DEBUG: Done:	 Training
+2016-09-06 10:13:36,506 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,513 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,513 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,515 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,515 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 48
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,515 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,557 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,557 DEBUG: Start:	 Training
+2016-09-06 10:13:36,565 DEBUG: Info:	 Time for Training: 0.0949969291687[s]
+2016-09-06 10:13:36,565 DEBUG: Done:	 Training
+2016-09-06 10:13:36,566 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,569 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,569 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,570 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,571 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.904761904762
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.904761904762
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,571 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,718 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,718 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,718 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:13:36,718 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:13:36,718 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,718 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,719 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,719 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,719 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,719 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,719 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,719 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,719 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,719 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,771 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,771 DEBUG: Start:	 Training
+2016-09-06 10:13:36,779 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,779 DEBUG: Start:	 Training
+2016-09-06 10:13:36,780 DEBUG: Info:	 Time for Training: 0.063157081604[s]
+2016-09-06 10:13:36,780 DEBUG: Done:	 Training
+2016-09-06 10:13:36,781 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,792 DEBUG: Info:	 Time for Training: 0.0748438835144[s]
+2016-09-06 10:13:36,792 DEBUG: Done:	 Training
+2016-09-06 10:13:36,792 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,796 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,796 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,797 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,797 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.466666666667
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,797 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,801 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,801 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,804 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,804 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,804 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,869 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,870 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:13:36,870 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,870 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:36,870 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:13:36,870 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:36,871 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,871 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,871 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,871 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,872 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:13:36,872 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:13:36,872 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:36,872 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:36,918 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,919 DEBUG: Start:	 Training
+2016-09-06 10:13:36,930 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:36,930 DEBUG: Start:	 Training
+2016-09-06 10:13:36,935 DEBUG: Info:	 Time for Training: 0.0670058727264[s]
+2016-09-06 10:13:36,936 DEBUG: Done:	 Training
+2016-09-06 10:13:36,936 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,941 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,941 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,943 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,943 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,943 INFO: Done:	 Result Analysis
+2016-09-06 10:13:36,947 DEBUG: Info:	 Time for Training: 0.0786368846893[s]
+2016-09-06 10:13:36,947 DEBUG: Done:	 Training
+2016-09-06 10:13:36,947 DEBUG: Start:	 Predicting
+2016-09-06 10:13:36,952 DEBUG: Done:	 Predicting
+2016-09-06 10:13:36,952 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:36,954 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:36,954 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:36,954 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,012 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,012 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,013 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:13:37,013 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:13:37,013 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,013 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,013 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,013 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,014 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,014 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,014 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,014 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,014 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,014 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,046 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,046 DEBUG: Start:	 Training
+2016-09-06 10:13:37,048 DEBUG: Info:	 Time for Training: 0.035749912262[s]
+2016-09-06 10:13:37,048 DEBUG: Done:	 Training
+2016-09-06 10:13:37,048 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,051 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,051 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,052 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,052 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,052 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,061 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,061 DEBUG: Start:	 Training
+2016-09-06 10:13:37,064 DEBUG: Info:	 Time for Training: 0.0523209571838[s]
+2016-09-06 10:13:37,064 DEBUG: Done:	 Training
+2016-09-06 10:13:37,065 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,067 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,068 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,069 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,070 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,078 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,160 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,160 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,161 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:13:37,161 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:13:37,162 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,162 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,163 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,163 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,163 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,163 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,163 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,163 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,163 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,163 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,195 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,195 DEBUG: Start:	 Training
+2016-09-06 10:13:37,196 DEBUG: Info:	 Time for Training: 0.0357730388641[s]
+2016-09-06 10:13:37,196 DEBUG: Done:	 Training
+2016-09-06 10:13:37,196 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,202 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,202 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,204 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,204 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 48
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,204 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,251 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,252 DEBUG: Start:	 Training
+2016-09-06 10:13:37,260 DEBUG: Info:	 Time for Training: 0.0996959209442[s]
+2016-09-06 10:13:37,260 DEBUG: Done:	 Training
+2016-09-06 10:13:37,260 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,263 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,263 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,265 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,265 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.895238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.895238095238
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,265 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,416 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,416 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,417 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:13:37,417 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:13:37,417 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,417 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,417 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,417 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,418 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,418 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,418 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,418 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,418 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,418 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,462 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,463 DEBUG: Start:	 Training
+2016-09-06 10:13:37,463 DEBUG: Info:	 Time for Training: 0.0475831031799[s]
+2016-09-06 10:13:37,463 DEBUG: Done:	 Training
+2016-09-06 10:13:37,463 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,467 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,468 DEBUG: Start:	 Training
+2016-09-06 10:13:37,476 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,477 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,478 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,478 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,479 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,492 DEBUG: Info:	 Time for Training: 0.0758819580078[s]
+2016-09-06 10:13:37,492 DEBUG: Done:	 Training
+2016-09-06 10:13:37,492 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,495 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,495 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,496 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,496 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,497 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,558 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,558 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,559 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:13:37,559 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:13:37,559 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,559 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,559 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,559 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:13:37,559 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,560 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:13:37,560 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,560 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,560 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,560 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,610 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,610 DEBUG: Start:	 Training
+2016-09-06 10:13:37,614 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,614 DEBUG: Start:	 Training
+2016-09-06 10:13:37,627 DEBUG: Info:	 Time for Training: 0.0695948600769[s]
+2016-09-06 10:13:37,627 DEBUG: Done:	 Training
+2016-09-06 10:13:37,628 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,632 DEBUG: Info:	 Time for Training: 0.0741398334503[s]
+2016-09-06 10:13:37,632 DEBUG: Done:	 Training
+2016-09-06 10:13:37,632 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,633 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,633 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,634 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,634 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,634 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,635 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,635 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,637 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,637 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.628571428571
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,637 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,708 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,708 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:13:37,708 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,708 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,709 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:13:37,709 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:37,709 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,709 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:37,709 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,709 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,710 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:37,710 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:37,710 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,710 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,749 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,749 DEBUG: Start:	 Training
+2016-09-06 10:13:37,751 DEBUG: Info:	 Time for Training: 0.0440349578857[s]
+2016-09-06 10:13:37,751 DEBUG: Done:	 Training
+2016-09-06 10:13:37,752 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,754 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,754 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,755 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,756 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,756 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,768 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,768 DEBUG: Start:	 Training
+2016-09-06 10:13:37,773 DEBUG: Info:	 Time for Training: 0.0648808479309[s]
+2016-09-06 10:13:37,773 DEBUG: Done:	 Training
+2016-09-06 10:13:37,773 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,776 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,776 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,778 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,778 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,778 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,859 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,859 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:37,860 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:13:37,860 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:13:37,860 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,860 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:37,861 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:37,861 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:37,862 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:37,862 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:37,862 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,862 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:37,862 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,862 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:37,914 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,915 DEBUG: Start:	 Training
+2016-09-06 10:13:37,915 DEBUG: Info:	 Time for Training: 0.0569260120392[s]
+2016-09-06 10:13:37,915 DEBUG: Done:	 Training
+2016-09-06 10:13:37,916 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,927 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,927 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,929 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,929 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 48
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,930 INFO: Done:	 Result Analysis
+2016-09-06 10:13:37,973 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:37,974 DEBUG: Start:	 Training
+2016-09-06 10:13:37,982 DEBUG: Info:	 Time for Training: 0.123476028442[s]
+2016-09-06 10:13:37,982 DEBUG: Done:	 Training
+2016-09-06 10:13:37,982 DEBUG: Start:	 Predicting
+2016-09-06 10:13:37,985 DEBUG: Done:	 Predicting
+2016-09-06 10:13:37,985 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:37,987 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:37,987 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.890476190476
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:37,987 INFO: Done:	 Result Analysis
+2016-09-06 10:13:38,103 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:38,103 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:13:38,104 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:13:38,104 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:13:38,104 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:38,104 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:13:38,105 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:38,105 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:13:38,105 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:38,105 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:13:38,105 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:38,105 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:13:38,105 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:38,105 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:13:38,152 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:38,152 DEBUG: Start:	 Training
+2016-09-06 10:13:38,153 DEBUG: Info:	 Time for Training: 0.0500018596649[s]
+2016-09-06 10:13:38,153 DEBUG: Done:	 Training
+2016-09-06 10:13:38,153 DEBUG: Start:	 Predicting
+2016-09-06 10:13:38,160 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:13:38,160 DEBUG: Start:	 Training
+2016-09-06 10:13:38,170 DEBUG: Done:	 Predicting
+2016-09-06 10:13:38,170 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:38,172 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:38,172 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:38,172 INFO: Done:	 Result Analysis
+2016-09-06 10:13:38,182 DEBUG: Info:	 Time for Training: 0.0787019729614[s]
+2016-09-06 10:13:38,182 DEBUG: Done:	 Training
+2016-09-06 10:13:38,182 DEBUG: Start:	 Predicting
+2016-09-06 10:13:38,185 DEBUG: Done:	 Predicting
+2016-09-06 10:13:38,186 DEBUG: Start:	 Getting Results
+2016-09-06 10:13:38,187 DEBUG: Done:	 Getting Results
+2016-09-06 10:13:38,187 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.52380952381
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:13:38,187 INFO: Done:	 Result Analysis
+2016-09-06 10:13:38,404 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:13:38,404 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:13:38,405 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:13:38,405 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:13:38,405 INFO: Info:	 Shape of View0 :(300, 10)
+2016-09-06 10:13:38,406 INFO: Info:	 Shape of View0 :(300, 10)
+2016-09-06 10:13:38,406 INFO: Info:	 Shape of View1 :(300, 13)
+2016-09-06 10:13:38,407 INFO: Info:	 Shape of View1 :(300, 13)
+2016-09-06 10:13:38,407 INFO: Info:	 Shape of View2 :(300, 7)
+2016-09-06 10:13:38,407 INFO: Info:	 Shape of View2 :(300, 7)
+2016-09-06 10:13:38,407 INFO: Info:	 Shape of View3 :(300, 19)
+2016-09-06 10:13:38,407 INFO: Done:	 Read Database Files
+2016-09-06 10:13:38,408 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:13:38,408 INFO: Info:	 Shape of View3 :(300, 19)
+2016-09-06 10:13:38,408 INFO: Done:	 Read Database Files
+2016-09-06 10:13:38,408 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:13:38,412 INFO: Done:	 Determine validation split
+2016-09-06 10:13:38,412 INFO: Start:	 Determine 5 folds
+2016-09-06 10:13:38,412 INFO: Done:	 Determine validation split
+2016-09-06 10:13:38,413 INFO: Start:	 Determine 5 folds
+2016-09-06 10:13:38,422 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:13:38,422 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:13:38,422 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:13:38,422 INFO: Done:	 Determine folds
+2016-09-06 10:13:38,423 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:13:38,423 INFO: Start:	 Classification
+2016-09-06 10:13:38,423 INFO: 	Start:	 Fold number 1
+2016-09-06 10:13:38,425 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:13:38,425 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:13:38,425 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:13:38,425 INFO: Done:	 Determine folds
+2016-09-06 10:13:38,425 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:13:38,425 INFO: Start:	 Classification
+2016-09-06 10:13:38,426 INFO: 	Start:	 Fold number 1
+2016-09-06 10:13:38,463 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:13:38,473 DEBUG: 			View 0 : 0.535135135135
+2016-09-06 10:13:38,481 DEBUG: 			View 1 : 0.524324324324
+2016-09-06 10:13:38,491 DEBUG: 			View 2 : 0.437837837838
+2016-09-06 10:13:38,500 DEBUG: 			View 3 : 0.535135135135
+2016-09-06 10:13:38,541 DEBUG: 			 Best view : 		View1
+2016-09-06 10:13:38,635 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:13:38,645 DEBUG: 			View 0 : 0.756756756757
+2016-09-06 10:13:38,652 DEBUG: 			View 1 : 0.735135135135
+2016-09-06 10:13:38,662 DEBUG: 			View 2 : 0.697297297297
+2016-09-06 10:13:38,669 DEBUG: 			View 3 : 0.697297297297
+2016-09-06 10:13:38,719 DEBUG: 			 Best view : 		View0
+2016-09-06 10:13:38,877 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:13:38,885 DEBUG: 			View 0 : 0.756756756757
+2016-09-06 10:13:38,892 DEBUG: 			View 1 : 0.735135135135
+2016-09-06 10:13:38,899 DEBUG: 			View 2 : 0.697297297297
+2016-09-06 10:13:38,907 DEBUG: 			View 3 : 0.697297297297
+2016-09-06 10:13:38,949 DEBUG: 			 Best view : 		View0
+2016-09-06 10:13:39,172 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:13:39,179 DEBUG: 			View 0 : 0.702702702703
+2016-09-06 10:13:39,187 DEBUG: 			View 1 : 0.67027027027
+2016-09-06 10:13:39,194 DEBUG: 			View 2 : 0.708108108108
+2016-09-06 10:13:39,202 DEBUG: 			View 3 : 0.67027027027
+2016-09-06 10:13:39,246 DEBUG: 			 Best view : 		View0
+2016-09-06 10:13:39,538 INFO: 	Start: 	 Classification
+2016-09-06 10:13:40,019 INFO: 	Done: 	 Fold number 1
+2016-09-06 10:13:40,019 INFO: 	Start:	 Fold number 2
+2016-09-06 10:13:40,049 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:13:40,056 DEBUG: 			View 0 : 0.522222222222
+2016-09-06 10:13:40,063 DEBUG: 			View 1 : 0.516666666667
+2016-09-06 10:13:40,069 DEBUG: 			View 2 : 0.477777777778
+2016-09-06 10:13:40,077 DEBUG: 			View 3 : 0.516666666667
+2016-09-06 10:13:40,109 DEBUG: 			 Best view : 		View0
+2016-09-06 10:13:40,187 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:13:40,195 DEBUG: 			View 0 : 0.622222222222
+2016-09-06 10:13:40,202 DEBUG: 			View 1 : 0.672222222222
+2016-09-06 10:13:40,209 DEBUG: 			View 2 : 0.677777777778
+2016-09-06 10:13:40,217 DEBUG: 			View 3 : 0.672222222222
+2016-09-06 10:13:40,256 DEBUG: 			 Best view : 		View2
+2016-09-06 10:13:40,404 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:13:40,411 DEBUG: 			View 0 : 0.622222222222
+2016-09-06 10:13:40,419 DEBUG: 			View 1 : 0.672222222222
+2016-09-06 10:13:40,426 DEBUG: 			View 2 : 0.677777777778
+2016-09-06 10:13:40,433 DEBUG: 			View 3 : 0.672222222222
+2016-09-06 10:13:40,475 DEBUG: 			 Best view : 		View2
+2016-09-06 10:13:40,691 INFO: 	Start: 	 Classification
+2016-09-06 10:13:41,043 INFO: 	Done: 	 Fold number 2
+2016-09-06 10:13:41,043 INFO: 	Start:	 Fold number 3
+2016-09-06 10:13:41,073 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:13:41,079 DEBUG: 			View 0 : 0.544444444444
+2016-09-06 10:13:41,087 DEBUG: 			View 1 : 0.522222222222
+2016-09-06 10:13:41,093 DEBUG: 			View 2 : 0.466666666667
+2016-09-06 10:13:41,101 DEBUG: 			View 3 : 0.511111111111
+2016-09-06 10:13:41,133 DEBUG: 			 Best view : 		View1
+2016-09-06 10:13:41,212 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:13:41,220 DEBUG: 			View 0 : 0.688888888889
+2016-09-06 10:13:41,227 DEBUG: 			View 1 : 0.627777777778
+2016-09-06 10:13:41,234 DEBUG: 			View 2 : 0.716666666667
+2016-09-06 10:13:41,242 DEBUG: 			View 3 : 0.733333333333
+2016-09-06 10:13:41,280 DEBUG: 			 Best view : 		View3
+2016-09-06 10:13:41,428 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:13:41,435 DEBUG: 			View 0 : 0.688888888889
+2016-09-06 10:13:41,443 DEBUG: 			View 1 : 0.627777777778
+2016-09-06 10:13:41,450 DEBUG: 			View 2 : 0.716666666667
+2016-09-06 10:13:41,457 DEBUG: 			View 3 : 0.733333333333
+2016-09-06 10:13:41,498 DEBUG: 			 Best view : 		View3
+2016-09-06 10:13:41,716 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:13:41,723 DEBUG: 			View 0 : 0.666666666667
+2016-09-06 10:13:41,730 DEBUG: 			View 1 : 0.611111111111
+2016-09-06 10:13:41,737 DEBUG: 			View 2 : 0.705555555556
+2016-09-06 10:13:41,745 DEBUG: 			View 3 : 0.688888888889
+2016-09-06 10:13:41,789 DEBUG: 			 Best view : 		View2
+2016-09-06 10:13:42,070 INFO: 	Start: 	 Classification
+2016-09-06 10:13:42,538 INFO: 	Done: 	 Fold number 3
+2016-09-06 10:13:42,538 INFO: 	Start:	 Fold number 4
+2016-09-06 10:13:42,568 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:13:42,575 DEBUG: 			View 0 : 0.527472527473
+2016-09-06 10:13:42,582 DEBUG: 			View 1 : 0.582417582418
+2016-09-06 10:13:42,588 DEBUG: 			View 2 : 0.516483516484
+2016-09-06 10:13:42,596 DEBUG: 			View 3 : 0.516483516484
+2016-09-06 10:13:42,628 DEBUG: 			 Best view : 		View0
+2016-09-06 10:13:42,708 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:13:42,716 DEBUG: 			View 0 : 0.697802197802
+2016-09-06 10:13:42,723 DEBUG: 			View 1 : 0.675824175824
+2016-09-06 10:13:42,730 DEBUG: 			View 2 : 0.675824175824
+2016-09-06 10:13:42,738 DEBUG: 			View 3 : 0.686813186813
+2016-09-06 10:13:42,776 DEBUG: 			 Best view : 		View0
+2016-09-06 10:13:42,926 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:13:42,933 DEBUG: 			View 0 : 0.697802197802
+2016-09-06 10:13:42,940 DEBUG: 			View 1 : 0.675824175824
+2016-09-06 10:13:42,947 DEBUG: 			View 2 : 0.675824175824
+2016-09-06 10:13:42,955 DEBUG: 			View 3 : 0.686813186813
+2016-09-06 10:13:42,996 DEBUG: 			 Best view : 		View0
+2016-09-06 10:13:43,215 INFO: 	Start: 	 Classification
+2016-09-06 10:13:43,569 INFO: 	Done: 	 Fold number 4
+2016-09-06 10:13:43,570 INFO: 	Start:	 Fold number 5
+2016-09-06 10:13:43,600 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:13:43,607 DEBUG: 			View 0 : 0.497297297297
+2016-09-06 10:13:43,615 DEBUG: 			View 1 : 0.486486486486
+2016-09-06 10:13:43,621 DEBUG: 			View 2 : 0.491891891892
+2016-09-06 10:13:43,629 DEBUG: 			View 3 : 0.502702702703
+2016-09-06 10:13:43,662 DEBUG: 			 Best view : 		View3
+2016-09-06 10:13:43,743 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:13:43,751 DEBUG: 			View 0 : 0.702702702703
+2016-09-06 10:13:43,758 DEBUG: 			View 1 : 0.702702702703
+2016-09-06 10:13:43,765 DEBUG: 			View 2 : 0.605405405405
+2016-09-06 10:13:43,773 DEBUG: 			View 3 : 0.718918918919
+2016-09-06 10:13:43,812 DEBUG: 			 Best view : 		View3
+2016-09-06 10:13:43,965 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:13:43,972 DEBUG: 			View 0 : 0.702702702703
+2016-09-06 10:13:43,980 DEBUG: 			View 1 : 0.702702702703
+2016-09-06 10:13:43,987 DEBUG: 			View 2 : 0.605405405405
+2016-09-06 10:13:43,995 DEBUG: 			View 3 : 0.718918918919
+2016-09-06 10:13:44,037 DEBUG: 			 Best view : 		View3
+2016-09-06 10:13:44,260 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:13:44,268 DEBUG: 			View 0 : 0.724324324324
+2016-09-06 10:13:44,275 DEBUG: 			View 1 : 0.627027027027
+2016-09-06 10:13:44,283 DEBUG: 			View 2 : 0.681081081081
+2016-09-06 10:13:44,290 DEBUG: 			View 3 : 0.648648648649
+2016-09-06 10:13:44,335 DEBUG: 			 Best view : 		View0
+2016-09-06 10:13:44,628 INFO: 	Start: 	 Classification
+2016-09-06 10:13:45,105 INFO: 	Done: 	 Fold number 5
+2016-09-06 10:13:45,106 INFO: Done:	 Classification
+2016-09-06 10:13:45,106 INFO: Info:	 Time for Classification: 6[s]
+2016-09-06 10:13:45,106 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d594434166a146d3f494ca2c20fab70800090ff2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..41319f9a9f3ebffe8d077fd30a358df14240f387
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ffd9cc63fc5b1ad1c7daf34cb4d7e8e8cd756643
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 48
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..61f7d8649463de1c263f3b9d3ab07a2cac99614d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.9
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.9
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5e68fb883ac17dfd5d6210e4375e439e31615a56
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..73356fd51695a723319d40daeac7d2afc5ae6419
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1a9bd291268ae15c9a9d1ba00ec0f9484459229c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 48
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7ece7302181bbc22ada0c2dc3571a028bfff24ec
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.904761904762
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.904761904762
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..23e4c20cb46a53fea41beacfabcfa51a0c4ea031
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a6ee90732ab60d714cd21319c61f07ccabd6a223
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.466666666667
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..736ada899fb022ef8ba2503b09a6723398d5c9b4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..749dc0afe3142c94dc5a247c6c0a6b04cd19c6e6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..05b31d532cb9d42d7f108cba47b4846bd33a83d2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0e7c951d0a5f47fee7f6b6922f86ef8a17af692d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..58caffdda4255d1341b8e1260d1601d02eb45c2f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 48
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fffde9a8b265f2315fc6bed616f802872619a798
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 3, max_depth : 16
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.890476190476
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..35e2c637bc2fbd3b09799e55301b194a1b4095b4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fc1d9bb0429aaf686a8f5f1ede033cbc09251ab2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3b5c3c54184735dc3bf8b27aaa6240a75cc3ca02
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.628571428571
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..785b35119368d7ad397bf94b6366cd41a012941e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5be944065b4ac4026e6568b017952bc45473e35e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7668376b7f21ff7c34aeba050aee3bde3f4c5468
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 9584
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.52380952381
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..5867a8bf1b4cfc28d3fca0c46d6369be80d05aef
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1381 @@
+2016-09-06 10:23:43,264 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:23:43,264 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 9.821875e-05 Gbytes /!\ 
+2016-09-06 10:23:48,275 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:23:48,277 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:23:48,332 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:48,332 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:48,332 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:23:48,332 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:23:48,332 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:48,332 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:48,333 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:48,333 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:48,333 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:48,333 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:48,333 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:48,333 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:48,333 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:48,333 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:48,366 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:48,367 DEBUG: Start:	 Training
+2016-09-06 10:23:48,368 DEBUG: Info:	 Time for Training: 0.0366399288177[s]
+2016-09-06 10:23:48,368 DEBUG: Done:	 Training
+2016-09-06 10:23:48,368 DEBUG: Start:	 Predicting
+2016-09-06 10:23:48,371 DEBUG: Done:	 Predicting
+2016-09-06 10:23:48,371 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:48,372 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:48,372 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.919047619048
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.919047619048
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:48,372 INFO: Done:	 Result Analysis
+2016-09-06 10:23:48,382 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:48,382 DEBUG: Start:	 Training
+2016-09-06 10:23:48,385 DEBUG: Info:	 Time for Training: 0.0539109706879[s]
+2016-09-06 10:23:48,385 DEBUG: Done:	 Training
+2016-09-06 10:23:48,385 DEBUG: Start:	 Predicting
+2016-09-06 10:23:48,388 DEBUG: Done:	 Predicting
+2016-09-06 10:23:48,388 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:48,390 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:48,390 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:48,390 INFO: Done:	 Result Analysis
+2016-09-06 10:23:48,477 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:48,477 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:48,477 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:23:48,477 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:23:48,477 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:48,477 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:48,478 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:48,478 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:48,478 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:48,478 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:48,478 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:48,478 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:48,478 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:48,478 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:48,509 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:48,509 DEBUG: Start:	 Training
+2016-09-06 10:23:48,510 DEBUG: Info:	 Time for Training: 0.0335669517517[s]
+2016-09-06 10:23:48,510 DEBUG: Done:	 Training
+2016-09-06 10:23:48,510 DEBUG: Start:	 Predicting
+2016-09-06 10:23:48,514 DEBUG: Done:	 Predicting
+2016-09-06 10:23:48,514 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:48,516 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:48,516 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.67619047619
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:48,516 INFO: Done:	 Result Analysis
+2016-09-06 10:23:48,820 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:48,820 DEBUG: Start:	 Training
+2016-09-06 10:23:48,870 DEBUG: Info:	 Time for Training: 0.393655061722[s]
+2016-09-06 10:23:48,870 DEBUG: Done:	 Training
+2016-09-06 10:23:48,870 DEBUG: Start:	 Predicting
+2016-09-06 10:23:48,876 DEBUG: Done:	 Predicting
+2016-09-06 10:23:48,876 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:48,878 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:48,878 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 20, max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:48,878 INFO: Done:	 Result Analysis
+2016-09-06 10:23:49,036 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,036 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:23:49,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:23:49,037 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:49,037 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:49,038 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:49,038 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:49,038 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:49,038 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:49,038 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:49,038 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:49,038 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,038 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,100 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:49,100 DEBUG: Start:	 Training
+2016-09-06 10:23:49,122 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:49,122 DEBUG: Start:	 Training
+2016-09-06 10:23:49,123 DEBUG: Info:	 Time for Training: 0.0881431102753[s]
+2016-09-06 10:23:49,124 DEBUG: Done:	 Training
+2016-09-06 10:23:49,124 DEBUG: Start:	 Predicting
+2016-09-06 10:23:49,125 DEBUG: Info:	 Time for Training: 0.0898480415344[s]
+2016-09-06 10:23:49,125 DEBUG: Done:	 Training
+2016-09-06 10:23:49,125 DEBUG: Start:	 Predicting
+2016-09-06 10:23:49,129 DEBUG: Done:	 Predicting
+2016-09-06 10:23:49,129 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:49,130 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:49,130 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.480952380952
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.480952380952
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,130 INFO: Done:	 Result Analysis
+2016-09-06 10:23:49,138 DEBUG: Done:	 Predicting
+2016-09-06 10:23:49,138 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:49,140 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:49,140 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.604761904762
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,140 INFO: Done:	 Result Analysis
+2016-09-06 10:23:49,278 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,279 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:23:49,279 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:49,280 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:49,280 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,280 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:49,280 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:23:49,280 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:49,280 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,280 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:49,281 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:23:49,281 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:23:49,282 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:49,282 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,329 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:49,329 DEBUG: Start:	 Training
+2016-09-06 10:23:49,340 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:49,340 DEBUG: Start:	 Training
+2016-09-06 10:23:49,345 DEBUG: Info:	 Time for Training: 0.0676798820496[s]
+2016-09-06 10:23:49,346 DEBUG: Done:	 Training
+2016-09-06 10:23:49,346 DEBUG: Start:	 Predicting
+2016-09-06 10:23:49,351 DEBUG: Done:	 Predicting
+2016-09-06 10:23:49,351 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:49,352 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:49,353 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,353 INFO: Done:	 Result Analysis
+2016-09-06 10:23:49,358 DEBUG: Info:	 Time for Training: 0.0790169239044[s]
+2016-09-06 10:23:49,358 DEBUG: Done:	 Training
+2016-09-06 10:23:49,358 DEBUG: Start:	 Predicting
+2016-09-06 10:23:49,361 DEBUG: Done:	 Predicting
+2016-09-06 10:23:49,361 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:49,363 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:49,364 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.752380952381
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.752380952381
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,364 INFO: Done:	 Result Analysis
+2016-09-06 10:23:49,427 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,427 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,427 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:23:49,427 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:23:49,427 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:49,427 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:49,428 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:49,428 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:49,428 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:49,428 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:49,428 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:49,428 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:49,428 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,428 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,470 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:49,470 DEBUG: Start:	 Training
+2016-09-06 10:23:49,472 DEBUG: Info:	 Time for Training: 0.0461599826813[s]
+2016-09-06 10:23:49,472 DEBUG: Done:	 Training
+2016-09-06 10:23:49,473 DEBUG: Start:	 Predicting
+2016-09-06 10:23:49,475 DEBUG: Done:	 Predicting
+2016-09-06 10:23:49,475 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:49,476 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:49,477 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.780952380952
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.780952380952
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,477 INFO: Done:	 Result Analysis
+2016-09-06 10:23:49,479 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:49,479 DEBUG: Start:	 Training
+2016-09-06 10:23:49,483 DEBUG: Info:	 Time for Training: 0.057247877121[s]
+2016-09-06 10:23:49,484 DEBUG: Done:	 Training
+2016-09-06 10:23:49,484 DEBUG: Start:	 Predicting
+2016-09-06 10:23:49,487 DEBUG: Done:	 Predicting
+2016-09-06 10:23:49,487 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:49,489 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:49,489 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,489 INFO: Done:	 Result Analysis
+2016-09-06 10:23:49,575 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,575 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:49,575 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:23:49,575 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:23:49,575 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:49,575 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:49,576 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:49,576 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:49,576 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:49,576 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:49,576 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:49,576 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:49,576 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,576 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:49,606 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:49,607 DEBUG: Start:	 Training
+2016-09-06 10:23:49,607 DEBUG: Info:	 Time for Training: 0.0329051017761[s]
+2016-09-06 10:23:49,607 DEBUG: Done:	 Training
+2016-09-06 10:23:49,607 DEBUG: Start:	 Predicting
+2016-09-06 10:23:49,612 DEBUG: Done:	 Predicting
+2016-09-06 10:23:49,613 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:49,614 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:49,614 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.657142857143
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.657142857143
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,614 INFO: Done:	 Result Analysis
+2016-09-06 10:23:49,922 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:49,922 DEBUG: Start:	 Training
+2016-09-06 10:23:49,974 DEBUG: Info:	 Time for Training: 0.399342060089[s]
+2016-09-06 10:23:49,974 DEBUG: Done:	 Training
+2016-09-06 10:23:49,974 DEBUG: Start:	 Predicting
+2016-09-06 10:23:49,981 DEBUG: Done:	 Predicting
+2016-09-06 10:23:49,981 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:49,982 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:49,982 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 20, max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:49,982 INFO: Done:	 Result Analysis
+2016-09-06 10:23:50,127 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,127 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,128 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:23:50,128 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:23:50,128 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:50,128 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:50,129 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:50,129 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:50,129 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:50,129 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:50,129 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:50,129 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:50,129 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,129 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,173 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:50,173 DEBUG: Start:	 Training
+2016-09-06 10:23:50,174 DEBUG: Info:	 Time for Training: 0.0477719306946[s]
+2016-09-06 10:23:50,174 DEBUG: Done:	 Training
+2016-09-06 10:23:50,174 DEBUG: Start:	 Predicting
+2016-09-06 10:23:50,179 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:50,179 DEBUG: Start:	 Training
+2016-09-06 10:23:50,187 DEBUG: Done:	 Predicting
+2016-09-06 10:23:50,188 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:50,190 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:50,190 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,190 INFO: Done:	 Result Analysis
+2016-09-06 10:23:50,203 DEBUG: Info:	 Time for Training: 0.0768611431122[s]
+2016-09-06 10:23:50,204 DEBUG: Done:	 Training
+2016-09-06 10:23:50,204 DEBUG: Start:	 Predicting
+2016-09-06 10:23:50,207 DEBUG: Done:	 Predicting
+2016-09-06 10:23:50,207 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:50,208 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:50,208 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,209 INFO: Done:	 Result Analysis
+2016-09-06 10:23:50,271 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,271 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:23:50,271 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,271 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:50,272 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:23:50,272 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:50,272 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:50,273 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:50,273 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:50,273 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,273 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:23:50,273 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:23:50,274 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:50,274 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,323 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:50,323 DEBUG: Start:	 Training
+2016-09-06 10:23:50,325 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:50,325 DEBUG: Start:	 Training
+2016-09-06 10:23:50,341 DEBUG: Info:	 Time for Training: 0.0708639621735[s]
+2016-09-06 10:23:50,341 DEBUG: Done:	 Training
+2016-09-06 10:23:50,341 DEBUG: Start:	 Predicting
+2016-09-06 10:23:50,344 DEBUG: Info:	 Time for Training: 0.0738418102264[s]
+2016-09-06 10:23:50,344 DEBUG: Done:	 Training
+2016-09-06 10:23:50,344 DEBUG: Start:	 Predicting
+2016-09-06 10:23:50,347 DEBUG: Done:	 Predicting
+2016-09-06 10:23:50,347 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:50,347 DEBUG: Done:	 Predicting
+2016-09-06 10:23:50,348 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:50,348 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:50,348 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,348 INFO: Done:	 Result Analysis
+2016-09-06 10:23:50,349 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:50,349 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,349 INFO: Done:	 Result Analysis
+2016-09-06 10:23:50,420 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,420 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,421 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:23:50,421 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:23:50,421 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:50,421 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:50,422 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:50,422 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:50,422 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:50,422 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:50,423 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:50,423 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:50,423 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,423 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,474 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:50,474 DEBUG: Start:	 Training
+2016-09-06 10:23:50,476 DEBUG: Info:	 Time for Training: 0.0563578605652[s]
+2016-09-06 10:23:50,476 DEBUG: Done:	 Training
+2016-09-06 10:23:50,476 DEBUG: Start:	 Predicting
+2016-09-06 10:23:50,480 DEBUG: Done:	 Predicting
+2016-09-06 10:23:50,480 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:50,482 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:50,482 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.904761904762
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.904761904762
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,483 INFO: Done:	 Result Analysis
+2016-09-06 10:23:50,493 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:50,493 DEBUG: Start:	 Training
+2016-09-06 10:23:50,496 DEBUG: Info:	 Time for Training: 0.0766098499298[s]
+2016-09-06 10:23:50,496 DEBUG: Done:	 Training
+2016-09-06 10:23:50,496 DEBUG: Start:	 Predicting
+2016-09-06 10:23:50,499 DEBUG: Done:	 Predicting
+2016-09-06 10:23:50,499 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:50,501 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:50,501 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,501 INFO: Done:	 Result Analysis
+2016-09-06 10:23:50,564 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,564 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:50,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:23:50,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:23:50,564 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:50,565 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:50,565 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:50,565 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:50,565 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:50,565 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:50,565 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:50,565 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,565 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:50,566 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:50,596 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:50,596 DEBUG: Start:	 Training
+2016-09-06 10:23:50,597 DEBUG: Info:	 Time for Training: 0.0331511497498[s]
+2016-09-06 10:23:50,597 DEBUG: Done:	 Training
+2016-09-06 10:23:50,597 DEBUG: Start:	 Predicting
+2016-09-06 10:23:50,602 DEBUG: Done:	 Predicting
+2016-09-06 10:23:50,602 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:50,603 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:50,603 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.62380952381
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,603 INFO: Done:	 Result Analysis
+2016-09-06 10:23:50,908 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:50,908 DEBUG: Start:	 Training
+2016-09-06 10:23:50,959 DEBUG: Info:	 Time for Training: 0.395528078079[s]
+2016-09-06 10:23:50,959 DEBUG: Done:	 Training
+2016-09-06 10:23:50,959 DEBUG: Start:	 Predicting
+2016-09-06 10:23:50,966 DEBUG: Done:	 Predicting
+2016-09-06 10:23:50,966 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:50,967 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:50,968 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.971428571429
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 20, max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.971428571429
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:50,968 INFO: Done:	 Result Analysis
+2016-09-06 10:23:51,112 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,112 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,113 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:23:51,113 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:23:51,113 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:51,113 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:51,113 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:51,113 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:51,113 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:51,113 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:51,113 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:51,113 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:51,114 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,114 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,158 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:51,159 DEBUG: Start:	 Training
+2016-09-06 10:23:51,159 DEBUG: Info:	 Time for Training: 0.0475599765778[s]
+2016-09-06 10:23:51,160 DEBUG: Done:	 Training
+2016-09-06 10:23:51,160 DEBUG: Start:	 Predicting
+2016-09-06 10:23:51,163 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:51,163 DEBUG: Start:	 Training
+2016-09-06 10:23:51,180 DEBUG: Info:	 Time for Training: 0.0679569244385[s]
+2016-09-06 10:23:51,180 DEBUG: Done:	 Training
+2016-09-06 10:23:51,180 DEBUG: Start:	 Predicting
+2016-09-06 10:23:51,184 DEBUG: Done:	 Predicting
+2016-09-06 10:23:51,184 DEBUG: Done:	 Predicting
+2016-09-06 10:23:51,184 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:51,184 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:51,186 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:51,187 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.490476190476
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,187 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:51,187 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,187 INFO: Done:	 Result Analysis
+2016-09-06 10:23:51,187 INFO: Done:	 Result Analysis
+2016-09-06 10:23:51,265 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,265 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,265 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:23:51,265 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:23:51,265 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:51,265 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:51,266 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:51,266 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:23:51,266 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:51,266 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:23:51,266 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:51,266 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:51,266 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,266 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,315 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:51,315 DEBUG: Start:	 Training
+2016-09-06 10:23:51,321 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:51,321 DEBUG: Start:	 Training
+2016-09-06 10:23:51,333 DEBUG: Info:	 Time for Training: 0.0680320262909[s]
+2016-09-06 10:23:51,333 DEBUG: Done:	 Training
+2016-09-06 10:23:51,333 DEBUG: Start:	 Predicting
+2016-09-06 10:23:51,338 DEBUG: Done:	 Predicting
+2016-09-06 10:23:51,338 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:51,339 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:51,340 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,340 INFO: Done:	 Result Analysis
+2016-09-06 10:23:51,340 DEBUG: Info:	 Time for Training: 0.075443983078[s]
+2016-09-06 10:23:51,340 DEBUG: Done:	 Training
+2016-09-06 10:23:51,340 DEBUG: Start:	 Predicting
+2016-09-06 10:23:51,343 DEBUG: Done:	 Predicting
+2016-09-06 10:23:51,343 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:51,345 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:51,345 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.609523809524
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.609523809524
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,345 INFO: Done:	 Result Analysis
+2016-09-06 10:23:51,420 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,420 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,420 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:23:51,420 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:23:51,420 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:51,420 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:51,421 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:51,421 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:51,421 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:51,421 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:51,421 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:51,422 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,422 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:51,422 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,462 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:51,462 DEBUG: Start:	 Training
+2016-09-06 10:23:51,463 DEBUG: Info:	 Time for Training: 0.0440928936005[s]
+2016-09-06 10:23:51,463 DEBUG: Done:	 Training
+2016-09-06 10:23:51,463 DEBUG: Start:	 Predicting
+2016-09-06 10:23:51,466 DEBUG: Done:	 Predicting
+2016-09-06 10:23:51,466 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:51,467 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:51,467 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.880952380952
+accuracy_score on test : 0.611111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.880952380952
+		- Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,467 INFO: Done:	 Result Analysis
+2016-09-06 10:23:51,470 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:51,470 DEBUG: Start:	 Training
+2016-09-06 10:23:51,473 DEBUG: Info:	 Time for Training: 0.0543620586395[s]
+2016-09-06 10:23:51,473 DEBUG: Done:	 Training
+2016-09-06 10:23:51,473 DEBUG: Start:	 Predicting
+2016-09-06 10:23:51,476 DEBUG: Done:	 Predicting
+2016-09-06 10:23:51,476 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:51,478 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:51,479 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,479 INFO: Done:	 Result Analysis
+2016-09-06 10:23:51,574 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,574 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:51,574 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:23:51,574 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:23:51,574 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:51,574 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:51,575 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:51,575 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:51,575 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:51,575 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:51,576 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:51,576 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:51,576 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,576 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:51,623 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:51,623 DEBUG: Start:	 Training
+2016-09-06 10:23:51,624 DEBUG: Info:	 Time for Training: 0.0511050224304[s]
+2016-09-06 10:23:51,624 DEBUG: Done:	 Training
+2016-09-06 10:23:51,624 DEBUG: Start:	 Predicting
+2016-09-06 10:23:51,631 DEBUG: Done:	 Predicting
+2016-09-06 10:23:51,631 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:51,633 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:51,633 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.695238095238
+accuracy_score on test : 0.611111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.695238095238
+		- Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,634 INFO: Done:	 Result Analysis
+2016-09-06 10:23:51,937 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:51,937 DEBUG: Start:	 Training
+2016-09-06 10:23:51,987 DEBUG: Info:	 Time for Training: 0.414555072784[s]
+2016-09-06 10:23:51,988 DEBUG: Done:	 Training
+2016-09-06 10:23:51,988 DEBUG: Start:	 Predicting
+2016-09-06 10:23:51,994 DEBUG: Done:	 Predicting
+2016-09-06 10:23:51,994 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:51,995 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:51,996 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.952380952381
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 20, max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.952380952381
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:51,996 INFO: Done:	 Result Analysis
+2016-09-06 10:23:52,119 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:52,119 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:23:52,120 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:23:52,120 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:23:52,120 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:52,120 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:23:52,120 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:52,120 DEBUG: Info:	 Shape X_train:(210, 6), Length of y_train:210
+2016-09-06 10:23:52,121 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:52,121 DEBUG: Info:	 Shape X_test:(90, 6), Length of y_test:90
+2016-09-06 10:23:52,121 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:52,121 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:23:52,121 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:52,121 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:23:52,167 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:52,167 DEBUG: Start:	 Training
+2016-09-06 10:23:52,167 DEBUG: Info:	 Time for Training: 0.0486381053925[s]
+2016-09-06 10:23:52,168 DEBUG: Done:	 Training
+2016-09-06 10:23:52,168 DEBUG: Start:	 Predicting
+2016-09-06 10:23:52,170 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:23:52,170 DEBUG: Start:	 Training
+2016-09-06 10:23:52,186 DEBUG: Done:	 Predicting
+2016-09-06 10:23:52,186 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:52,187 DEBUG: Info:	 Time for Training: 0.0685579776764[s]
+2016-09-06 10:23:52,188 DEBUG: Done:	 Training
+2016-09-06 10:23:52,188 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:52,188 DEBUG: Start:	 Predicting
+2016-09-06 10:23:52,188 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:52,188 INFO: Done:	 Result Analysis
+2016-09-06 10:23:52,191 DEBUG: Done:	 Predicting
+2016-09-06 10:23:52,191 DEBUG: Start:	 Getting Results
+2016-09-06 10:23:52,192 DEBUG: Done:	 Getting Results
+2016-09-06 10:23:52,192 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.514285714286
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:23:52,193 INFO: Done:	 Result Analysis
+2016-09-06 10:23:52,415 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:23:52,415 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:23:52,416 INFO: Info:	 Shape of View0 :(300, 8)
+2016-09-06 10:23:52,417 INFO: Info:	 Shape of View1 :(300, 13)
+2016-09-06 10:23:52,417 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:23:52,417 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:23:52,417 INFO: Info:	 Shape of View2 :(300, 7)
+2016-09-06 10:23:52,418 INFO: Info:	 Shape of View0 :(300, 8)
+2016-09-06 10:23:52,418 INFO: Info:	 Shape of View3 :(300, 6)
+2016-09-06 10:23:52,419 INFO: Done:	 Read Database Files
+2016-09-06 10:23:52,419 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:23:52,419 INFO: Info:	 Shape of View1 :(300, 13)
+2016-09-06 10:23:52,420 INFO: Info:	 Shape of View2 :(300, 7)
+2016-09-06 10:23:52,421 INFO: Info:	 Shape of View3 :(300, 6)
+2016-09-06 10:23:52,421 INFO: Done:	 Read Database Files
+2016-09-06 10:23:52,421 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:23:52,426 INFO: Done:	 Determine validation split
+2016-09-06 10:23:52,426 INFO: Start:	 Determine 5 folds
+2016-09-06 10:23:52,427 INFO: Done:	 Determine validation split
+2016-09-06 10:23:52,427 INFO: Start:	 Determine 5 folds
+2016-09-06 10:23:52,435 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:23:52,435 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:23:52,435 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:23:52,435 INFO: Done:	 Determine folds
+2016-09-06 10:23:52,435 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:23:52,435 INFO: Start:	 Classification
+2016-09-06 10:23:52,435 INFO: 	Start:	 Fold number 1
+2016-09-06 10:23:52,436 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:23:52,436 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:23:52,436 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:23:52,436 INFO: Done:	 Determine folds
+2016-09-06 10:23:52,436 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:23:52,436 INFO: Start:	 Classification
+2016-09-06 10:23:52,437 INFO: 	Start:	 Fold number 1
+2016-09-06 10:23:52,457 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:23:52,463 DEBUG: 			View 0 : 0.516666666667
+2016-09-06 10:23:52,469 DEBUG: 			View 1 : 0.475
+2016-09-06 10:23:52,475 DEBUG: 			View 2 : 0.525
+2016-09-06 10:23:52,480 DEBUG: 			View 3 : 0.525
+2016-09-06 10:23:52,503 DEBUG: 			 Best view : 		View2
+2016-09-06 10:23:52,559 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:23:52,565 DEBUG: 			View 0 : 0.733333333333
+2016-09-06 10:23:52,572 DEBUG: 			View 1 : 0.741666666667
+2016-09-06 10:23:52,577 DEBUG: 			View 2 : 0.741666666667
+2016-09-06 10:23:52,583 DEBUG: 			View 3 : 0.658333333333
+2016-09-06 10:23:52,610 DEBUG: 			 Best view : 		View1
+2016-09-06 10:23:52,727 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:23:52,737 DEBUG: 			View 0 : 0.733333333333
+2016-09-06 10:23:52,748 DEBUG: 			View 1 : 0.741666666667
+2016-09-06 10:23:52,758 DEBUG: 			View 2 : 0.741666666667
+2016-09-06 10:23:52,768 DEBUG: 			View 3 : 0.658333333333
+2016-09-06 10:23:52,803 DEBUG: 			 Best view : 		View1
+2016-09-06 10:23:52,947 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:23:52,953 DEBUG: 			View 0 : 0.675
+2016-09-06 10:23:52,958 DEBUG: 			View 1 : 0.725
+2016-09-06 10:23:52,964 DEBUG: 			View 2 : 0.691666666667
+2016-09-06 10:23:52,969 DEBUG: 			View 3 : 0.641666666667
+2016-09-06 10:23:52,998 DEBUG: 			 Best view : 		View1
+2016-09-06 10:23:53,188 INFO: 	Start: 	 Classification
+2016-09-06 10:23:53,569 INFO: 	Done: 	 Fold number 1
+2016-09-06 10:23:53,569 INFO: 	Start:	 Fold number 2
+2016-09-06 10:23:53,589 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:23:53,594 DEBUG: 			View 0 : 0.603448275862
+2016-09-06 10:23:53,600 DEBUG: 			View 1 : 0.568965517241
+2016-09-06 10:23:53,605 DEBUG: 			View 2 : 0.413793103448
+2016-09-06 10:23:53,609 DEBUG: 			View 3 : 0.48275862069
+2016-09-06 10:23:53,631 DEBUG: 			 Best view : 		View1
+2016-09-06 10:23:53,682 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:23:53,687 DEBUG: 			View 0 : 0.741379310345
+2016-09-06 10:23:53,693 DEBUG: 			View 1 : 0.724137931034
+2016-09-06 10:23:53,698 DEBUG: 			View 2 : 0.741379310345
+2016-09-06 10:23:53,703 DEBUG: 			View 3 : 0.741379310345
+2016-09-06 10:23:53,728 DEBUG: 			 Best view : 		View0
+2016-09-06 10:23:53,823 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:23:53,828 DEBUG: 			View 0 : 0.741379310345
+2016-09-06 10:23:53,833 DEBUG: 			View 1 : 0.724137931034
+2016-09-06 10:23:53,839 DEBUG: 			View 2 : 0.741379310345
+2016-09-06 10:23:53,844 DEBUG: 			View 3 : 0.741379310345
+2016-09-06 10:23:53,870 DEBUG: 			 Best view : 		View0
+2016-09-06 10:23:54,011 INFO: 	Start: 	 Classification
+2016-09-06 10:23:54,292 INFO: 	Done: 	 Fold number 2
+2016-09-06 10:23:54,292 INFO: 	Start:	 Fold number 3
+2016-09-06 10:23:54,313 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:23:54,319 DEBUG: 			View 0 : 0.471074380165
+2016-09-06 10:23:54,324 DEBUG: 			View 1 : 0.504132231405
+2016-09-06 10:23:54,329 DEBUG: 			View 2 : 0.504132231405
+2016-09-06 10:23:54,334 DEBUG: 			View 3 : 0.528925619835
+2016-09-06 10:23:54,356 DEBUG: 			 Best view : 		View0
+2016-09-06 10:23:54,410 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:23:54,415 DEBUG: 			View 0 : 0.685950413223
+2016-09-06 10:23:54,421 DEBUG: 			View 1 : 0.619834710744
+2016-09-06 10:23:54,426 DEBUG: 			View 2 : 0.685950413223
+2016-09-06 10:23:54,431 DEBUG: 			View 3 : 0.760330578512
+2016-09-06 10:23:54,457 DEBUG: 			 Best view : 		View3
+2016-09-06 10:23:54,556 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:23:54,562 DEBUG: 			View 0 : 0.685950413223
+2016-09-06 10:23:54,567 DEBUG: 			View 1 : 0.619834710744
+2016-09-06 10:23:54,573 DEBUG: 			View 2 : 0.702479338843
+2016-09-06 10:23:54,578 DEBUG: 			View 3 : 0.760330578512
+2016-09-06 10:23:54,605 DEBUG: 			 Best view : 		View3
+2016-09-06 10:23:54,750 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:23:54,756 DEBUG: 			View 0 : 0.702479338843
+2016-09-06 10:23:54,761 DEBUG: 			View 1 : 0.669421487603
+2016-09-06 10:23:54,766 DEBUG: 			View 2 : 0.735537190083
+2016-09-06 10:23:54,772 DEBUG: 			View 3 : 0.669421487603
+2016-09-06 10:23:54,801 DEBUG: 			 Best view : 		View2
+2016-09-06 10:23:54,990 INFO: 	Start: 	 Classification
+2016-09-06 10:23:55,369 INFO: 	Done: 	 Fold number 3
+2016-09-06 10:23:55,369 INFO: 	Start:	 Fold number 4
+2016-09-06 10:23:55,389 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:23:55,394 DEBUG: 			View 0 : 0.44347826087
+2016-09-06 10:23:55,399 DEBUG: 			View 1 : 0.547826086957
+2016-09-06 10:23:55,404 DEBUG: 			View 2 : 0.565217391304
+2016-09-06 10:23:55,409 DEBUG: 			View 3 : 0.582608695652
+2016-09-06 10:23:55,430 DEBUG: 			 Best view : 		View0
+2016-09-06 10:23:55,481 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:23:55,487 DEBUG: 			View 0 : 0.713043478261
+2016-09-06 10:23:55,492 DEBUG: 			View 1 : 0.75652173913
+2016-09-06 10:23:55,497 DEBUG: 			View 2 : 0.747826086957
+2016-09-06 10:23:55,502 DEBUG: 			View 3 : 0.704347826087
+2016-09-06 10:23:55,527 DEBUG: 			 Best view : 		View1
+2016-09-06 10:23:55,624 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:23:55,629 DEBUG: 			View 0 : 0.713043478261
+2016-09-06 10:23:55,634 DEBUG: 			View 1 : 0.75652173913
+2016-09-06 10:23:55,639 DEBUG: 			View 2 : 0.747826086957
+2016-09-06 10:23:55,644 DEBUG: 			View 3 : 0.704347826087
+2016-09-06 10:23:55,671 DEBUG: 			 Best view : 		View1
+2016-09-06 10:23:55,811 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:23:55,817 DEBUG: 			View 0 : 0.730434782609
+2016-09-06 10:23:55,822 DEBUG: 			View 1 : 0.704347826087
+2016-09-06 10:23:55,827 DEBUG: 			View 2 : 0.747826086957
+2016-09-06 10:23:55,832 DEBUG: 			View 3 : 0.704347826087
+2016-09-06 10:23:55,860 DEBUG: 			 Best view : 		View0
+2016-09-06 10:23:56,042 INFO: 	Start: 	 Classification
+2016-09-06 10:23:56,413 INFO: 	Done: 	 Fold number 4
+2016-09-06 10:23:56,413 INFO: 	Start:	 Fold number 5
+2016-09-06 10:23:56,433 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:23:56,438 DEBUG: 			View 0 : 0.529914529915
+2016-09-06 10:23:56,444 DEBUG: 			View 1 : 0.581196581197
+2016-09-06 10:23:56,449 DEBUG: 			View 2 : 0.512820512821
+2016-09-06 10:23:56,454 DEBUG: 			View 3 : 0.470085470085
+2016-09-06 10:23:56,475 DEBUG: 			 Best view : 		View1
+2016-09-06 10:23:56,527 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:23:56,533 DEBUG: 			View 0 : 0.717948717949
+2016-09-06 10:23:56,538 DEBUG: 			View 1 : 0.752136752137
+2016-09-06 10:23:56,543 DEBUG: 			View 2 : 0.735042735043
+2016-09-06 10:23:56,548 DEBUG: 			View 3 : 0.769230769231
+2016-09-06 10:23:56,573 DEBUG: 			 Best view : 		View3
+2016-09-06 10:23:56,669 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:23:56,675 DEBUG: 			View 0 : 0.717948717949
+2016-09-06 10:23:56,680 DEBUG: 			View 1 : 0.752136752137
+2016-09-06 10:23:56,686 DEBUG: 			View 2 : 0.735042735043
+2016-09-06 10:23:56,691 DEBUG: 			View 3 : 0.769230769231
+2016-09-06 10:23:56,718 DEBUG: 			 Best view : 		View3
+2016-09-06 10:23:56,859 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:23:56,864 DEBUG: 			View 0 : 0.683760683761
+2016-09-06 10:23:56,869 DEBUG: 			View 1 : 0.709401709402
+2016-09-06 10:23:56,875 DEBUG: 			View 2 : 0.683760683761
+2016-09-06 10:23:56,879 DEBUG: 			View 3 : 0.735042735043
+2016-09-06 10:23:56,908 DEBUG: 			 Best view : 		View0
+2016-09-06 10:23:57,092 INFO: 	Start: 	 Classification
+2016-09-06 10:23:57,464 INFO: 	Done: 	 Fold number 5
+2016-09-06 10:23:57,464 INFO: Done:	 Classification
+2016-09-06 10:23:57,464 INFO: Info:	 Time for Classification: 5[s]
+2016-09-06 10:23:57,464 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2b8d6b9f3c21d818349a4c0669a58802ca77b29b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7eddcdd01f2da52a31dc7fed912cbd795d584829
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.919047619048
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.919047619048
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a458111b3453d54e7c1b486bda26f584e9ed1df1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.67619047619
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..64b9eb5de6f311b0e451413e8bbe4fdf520e8952
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 20, max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3cfc707216bde1ec9e643ad720fe8c79f3711d5d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1253f669baccf968a48dc46e2210abcc2dff2197
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.780952380952
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.780952380952
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8df47bbac622443819233ff9907d5dc04f8aa153
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.657142857143
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.657142857143
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..87a46146d4e788f4795d39708d56454ba990212f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 20, max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..87eb9ae88058508f4930eb63a4a930f3116ae210
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.604761904762
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..464f736bea7422296e85d86c6942a303a077a245
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.480952380952
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.480952380952
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..353cb868ae1ab30c7d3b4c67bb9d730f2cc77f75
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.752380952381
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.752380952381
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e442b3e4ad373da4e6a288bff71afc3488258573
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f728e30391572e475b16ad3ff23757f582780454
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..50877a2de51bcf4d0aa78e6ed043e9794392bd24
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.904761904762
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.904761904762
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b10d74270d9fe596fd3b26a4b2e91ca029aee3a0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.62380952381
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..49d16072f3170f2c7b2563639b834e6a937b1237
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.971428571429
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 20, max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.971428571429
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19aa2ae143577682e96519d209be9779f08d367e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ff73da4153a55c69ef7aeeca57811002784e352e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c05ffbde6f8fde0e3b2aa6e96a57ed6a7f03ebd9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a611a167f0b1ed63be7d17601cc7fb77081d5046
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f3a2e850c0cf6bfc29a980f2017d9cc57bdb72fe
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d2adb74a8fa2b0ebdba69777cb1865078cf2fc33
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.880952380952
+accuracy_score on test : 0.611111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.880952380952
+		- Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f4bdfa1df43b7776915f7b68674fa23d1a2c18a5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.695238095238
+accuracy_score on test : 0.611111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.695238095238
+		- Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..463c74e7d44898801d6966f64fa4437f4878ab09
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.952380952381
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 20, max_depth : 7
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.952380952381
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d2530167cd09777b5b90f8d7edc12b7b89e16bd5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.542857142857
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.542857142857
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6564b15fe83a611e304c081175986beb65905e1f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.490476190476
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ffe6f80e387462997efb7430db5cccb835b3f4ce
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.609523809524
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.609523809524
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dbdf7554100a303f29009ca1a729a7a567dfa35b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..624e000799b54bf18f80a0e499249ef8442ef9b1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..56a9a0dc1e83e199e5f0cd8a6bc7a4ec551cf42c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 6)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 3508
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.514285714286
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..aa39260af52aa30c0981a78d563d344973da0220
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1369 @@
+2016-09-06 10:24:40,231 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:24:40,231 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00012634375 Gbytes /!\ 
+2016-09-06 10:24:45,246 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:24:45,248 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:24:45,303 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:45,303 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:45,303 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:24:45,303 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:24:45,303 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:45,303 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:45,304 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:45,304 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:45,305 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:45,305 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:45,305 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:45,305 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:45,305 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:45,305 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:45,348 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:45,348 DEBUG: Start:	 Training
+2016-09-06 10:24:45,350 DEBUG: Info:	 Time for Training: 0.0480980873108[s]
+2016-09-06 10:24:45,350 DEBUG: Done:	 Training
+2016-09-06 10:24:45,350 DEBUG: Start:	 Predicting
+2016-09-06 10:24:45,352 DEBUG: Done:	 Predicting
+2016-09-06 10:24:45,353 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:45,354 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:45,354 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:45,354 INFO: Done:	 Result Analysis
+2016-09-06 10:24:45,362 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:45,362 DEBUG: Start:	 Training
+2016-09-06 10:24:45,366 DEBUG: Info:	 Time for Training: 0.0647637844086[s]
+2016-09-06 10:24:45,366 DEBUG: Done:	 Training
+2016-09-06 10:24:45,366 DEBUG: Start:	 Predicting
+2016-09-06 10:24:45,369 DEBUG: Done:	 Predicting
+2016-09-06 10:24:45,369 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:45,371 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:45,371 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:45,372 INFO: Done:	 Result Analysis
+2016-09-06 10:24:45,442 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:45,442 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:24:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:24:45,443 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:45,443 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:45,444 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:45,444 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:45,444 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:45,444 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:45,444 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:45,444 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:45,444 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:45,444 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:45,476 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:45,476 DEBUG: Start:	 Training
+2016-09-06 10:24:45,476 DEBUG: Info:	 Time for Training: 0.0347051620483[s]
+2016-09-06 10:24:45,476 DEBUG: Done:	 Training
+2016-09-06 10:24:45,476 DEBUG: Start:	 Predicting
+2016-09-06 10:24:45,482 DEBUG: Done:	 Predicting
+2016-09-06 10:24:45,482 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:45,484 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:45,484 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.642857142857
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 26
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.642857142857
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:45,484 INFO: Done:	 Result Analysis
+2016-09-06 10:24:45,890 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:45,890 DEBUG: Start:	 Training
+2016-09-06 10:24:45,958 DEBUG: Info:	 Time for Training: 0.516037940979[s]
+2016-09-06 10:24:45,958 DEBUG: Done:	 Training
+2016-09-06 10:24:45,958 DEBUG: Start:	 Predicting
+2016-09-06 10:24:45,965 DEBUG: Done:	 Predicting
+2016-09-06 10:24:45,966 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:45,967 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:45,967 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 26, max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:45,967 INFO: Done:	 Result Analysis
+2016-09-06 10:24:46,099 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,099 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,099 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:24:46,099 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:24:46,099 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:46,099 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:46,100 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:46,100 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:46,100 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:46,100 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:46,100 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:46,100 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:46,100 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,100 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,144 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:46,144 DEBUG: Start:	 Training
+2016-09-06 10:24:46,144 DEBUG: Info:	 Time for Training: 0.0459389686584[s]
+2016-09-06 10:24:46,144 DEBUG: Done:	 Training
+2016-09-06 10:24:46,145 DEBUG: Start:	 Predicting
+2016-09-06 10:24:46,149 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:46,149 DEBUG: Start:	 Training
+2016-09-06 10:24:46,162 DEBUG: Done:	 Predicting
+2016-09-06 10:24:46,162 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:46,164 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:46,164 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.595238095238
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.595238095238
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,164 INFO: Done:	 Result Analysis
+2016-09-06 10:24:46,168 DEBUG: Info:	 Time for Training: 0.0690729618073[s]
+2016-09-06 10:24:46,168 DEBUG: Done:	 Training
+2016-09-06 10:24:46,168 DEBUG: Start:	 Predicting
+2016-09-06 10:24:46,171 DEBUG: Done:	 Predicting
+2016-09-06 10:24:46,171 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:46,172 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:46,172 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,173 INFO: Done:	 Result Analysis
+2016-09-06 10:24:46,250 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,250 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,251 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:24:46,251 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:24:46,251 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:46,251 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:46,252 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:46,252 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:24:46,252 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:46,252 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:24:46,253 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:46,253 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:46,253 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,253 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,325 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:46,325 DEBUG: Start:	 Training
+2016-09-06 10:24:46,329 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:46,330 DEBUG: Start:	 Training
+2016-09-06 10:24:46,348 DEBUG: Info:	 Time for Training: 0.0991640090942[s]
+2016-09-06 10:24:46,349 DEBUG: Done:	 Training
+2016-09-06 10:24:46,349 DEBUG: Start:	 Predicting
+2016-09-06 10:24:46,354 DEBUG: Info:	 Time for Training: 0.104753017426[s]
+2016-09-06 10:24:46,354 DEBUG: Done:	 Training
+2016-09-06 10:24:46,354 DEBUG: Start:	 Predicting
+2016-09-06 10:24:46,356 DEBUG: Done:	 Predicting
+2016-09-06 10:24:46,357 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:46,359 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:46,359 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,359 DEBUG: Done:	 Predicting
+2016-09-06 10:24:46,359 INFO: Done:	 Result Analysis
+2016-09-06 10:24:46,359 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:46,361 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:46,361 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,361 INFO: Done:	 Result Analysis
+2016-09-06 10:24:46,499 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,499 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,500 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:24:46,500 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:24:46,500 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:46,500 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:46,500 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:46,500 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:46,501 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:46,501 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:46,501 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:46,501 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:46,501 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,501 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,540 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:46,541 DEBUG: Start:	 Training
+2016-09-06 10:24:46,543 DEBUG: Info:	 Time for Training: 0.0447499752045[s]
+2016-09-06 10:24:46,543 DEBUG: Done:	 Training
+2016-09-06 10:24:46,544 DEBUG: Start:	 Predicting
+2016-09-06 10:24:46,546 DEBUG: Done:	 Predicting
+2016-09-06 10:24:46,546 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:46,548 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:46,548 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,548 INFO: Done:	 Result Analysis
+2016-09-06 10:24:46,555 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:46,555 DEBUG: Start:	 Training
+2016-09-06 10:24:46,560 DEBUG: Info:	 Time for Training: 0.0609669685364[s]
+2016-09-06 10:24:46,560 DEBUG: Done:	 Training
+2016-09-06 10:24:46,560 DEBUG: Start:	 Predicting
+2016-09-06 10:24:46,563 DEBUG: Done:	 Predicting
+2016-09-06 10:24:46,563 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:46,565 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:46,565 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,565 INFO: Done:	 Result Analysis
+2016-09-06 10:24:46,647 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,647 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:46,647 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:24:46,647 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:24:46,647 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:46,647 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:46,648 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:46,648 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:46,648 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:46,648 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:46,649 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:46,649 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:46,649 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,649 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:46,681 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:46,681 DEBUG: Start:	 Training
+2016-09-06 10:24:46,681 DEBUG: Info:	 Time for Training: 0.0352909564972[s]
+2016-09-06 10:24:46,681 DEBUG: Done:	 Training
+2016-09-06 10:24:46,682 DEBUG: Start:	 Predicting
+2016-09-06 10:24:46,688 DEBUG: Done:	 Predicting
+2016-09-06 10:24:46,688 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:46,689 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:46,689 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 26
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:46,690 INFO: Done:	 Result Analysis
+2016-09-06 10:24:47,105 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:47,106 DEBUG: Start:	 Training
+2016-09-06 10:24:47,176 DEBUG: Info:	 Time for Training: 0.529899120331[s]
+2016-09-06 10:24:47,176 DEBUG: Done:	 Training
+2016-09-06 10:24:47,176 DEBUG: Start:	 Predicting
+2016-09-06 10:24:47,184 DEBUG: Done:	 Predicting
+2016-09-06 10:24:47,184 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:47,185 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:47,185 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 26, max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,186 INFO: Done:	 Result Analysis
+2016-09-06 10:24:47,297 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,297 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,297 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:24:47,297 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:24:47,297 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:47,297 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:47,298 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:47,298 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:47,298 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:47,298 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:47,299 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:47,299 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:47,299 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,299 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,369 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:47,369 DEBUG: Start:	 Training
+2016-09-06 10:24:47,370 DEBUG: Info:	 Time for Training: 0.0735681056976[s]
+2016-09-06 10:24:47,370 DEBUG: Done:	 Training
+2016-09-06 10:24:47,370 DEBUG: Start:	 Predicting
+2016-09-06 10:24:47,376 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:47,376 DEBUG: Start:	 Training
+2016-09-06 10:24:47,387 DEBUG: Done:	 Predicting
+2016-09-06 10:24:47,388 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:47,390 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:47,390 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.377777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,390 INFO: Done:	 Result Analysis
+2016-09-06 10:24:47,402 DEBUG: Info:	 Time for Training: 0.106291055679[s]
+2016-09-06 10:24:47,403 DEBUG: Done:	 Training
+2016-09-06 10:24:47,403 DEBUG: Start:	 Predicting
+2016-09-06 10:24:47,406 DEBUG: Done:	 Predicting
+2016-09-06 10:24:47,406 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:47,408 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:47,408 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.485714285714
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,408 INFO: Done:	 Result Analysis
+2016-09-06 10:24:47,547 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,547 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,547 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:24:47,547 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:24:47,548 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:47,548 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:47,549 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:47,549 DEBUG: Info:	 Shape X_train:(210, 16), Length of y_train:210
+2016-09-06 10:24:47,549 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:47,549 DEBUG: Info:	 Shape X_test:(90, 16), Length of y_test:90
+2016-09-06 10:24:47,549 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:47,549 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:47,549 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,549 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,623 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:47,623 DEBUG: Start:	 Training
+2016-09-06 10:24:47,631 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:47,631 DEBUG: Start:	 Training
+2016-09-06 10:24:47,648 DEBUG: Info:	 Time for Training: 0.102329015732[s]
+2016-09-06 10:24:47,648 DEBUG: Done:	 Training
+2016-09-06 10:24:47,649 DEBUG: Start:	 Predicting
+2016-09-06 10:24:47,657 DEBUG: Done:	 Predicting
+2016-09-06 10:24:47,657 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:47,658 DEBUG: Info:	 Time for Training: 0.112071990967[s]
+2016-09-06 10:24:47,658 DEBUG: Done:	 Training
+2016-09-06 10:24:47,658 DEBUG: Start:	 Predicting
+2016-09-06 10:24:47,659 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:47,659 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,660 INFO: Done:	 Result Analysis
+2016-09-06 10:24:47,663 DEBUG: Done:	 Predicting
+2016-09-06 10:24:47,663 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:47,664 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:47,665 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,665 INFO: Done:	 Result Analysis
+2016-09-06 10:24:47,798 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,798 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,798 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:24:47,798 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:24:47,799 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:47,799 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:47,799 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:47,799 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:47,800 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:47,800 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:47,800 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:47,800 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:47,800 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,800 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,837 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:47,837 DEBUG: Start:	 Training
+2016-09-06 10:24:47,839 DEBUG: Info:	 Time for Training: 0.0416760444641[s]
+2016-09-06 10:24:47,839 DEBUG: Done:	 Training
+2016-09-06 10:24:47,839 DEBUG: Start:	 Predicting
+2016-09-06 10:24:47,842 DEBUG: Done:	 Predicting
+2016-09-06 10:24:47,842 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:47,843 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:47,843 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,844 INFO: Done:	 Result Analysis
+2016-09-06 10:24:47,852 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:47,852 DEBUG: Start:	 Training
+2016-09-06 10:24:47,856 DEBUG: Info:	 Time for Training: 0.0591020584106[s]
+2016-09-06 10:24:47,856 DEBUG: Done:	 Training
+2016-09-06 10:24:47,857 DEBUG: Start:	 Predicting
+2016-09-06 10:24:47,860 DEBUG: Done:	 Predicting
+2016-09-06 10:24:47,860 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:47,862 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:47,862 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,862 INFO: Done:	 Result Analysis
+2016-09-06 10:24:47,951 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,951 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:47,952 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:24:47,952 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:24:47,952 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:47,952 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:47,953 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:47,953 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:47,953 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:47,953 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:47,953 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:47,953 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:47,953 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,953 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:47,985 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:47,985 DEBUG: Start:	 Training
+2016-09-06 10:24:47,986 DEBUG: Info:	 Time for Training: 0.0345377922058[s]
+2016-09-06 10:24:47,986 DEBUG: Done:	 Training
+2016-09-06 10:24:47,986 DEBUG: Start:	 Predicting
+2016-09-06 10:24:47,992 DEBUG: Done:	 Predicting
+2016-09-06 10:24:47,992 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:47,994 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:47,994 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 26
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:47,994 INFO: Done:	 Result Analysis
+2016-09-06 10:24:48,411 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:48,411 DEBUG: Start:	 Training
+2016-09-06 10:24:48,481 DEBUG: Info:	 Time for Training: 0.529783964157[s]
+2016-09-06 10:24:48,481 DEBUG: Done:	 Training
+2016-09-06 10:24:48,481 DEBUG: Start:	 Predicting
+2016-09-06 10:24:48,489 DEBUG: Done:	 Predicting
+2016-09-06 10:24:48,489 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:48,490 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:48,490 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 26, max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:48,491 INFO: Done:	 Result Analysis
+2016-09-06 10:24:48,607 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:48,607 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:48,608 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:24:48,608 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:24:48,608 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:48,608 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:48,609 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:48,609 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:48,609 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:48,609 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:48,609 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:48,609 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:48,609 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:48,609 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:48,677 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:48,677 DEBUG: Start:	 Training
+2016-09-06 10:24:48,678 DEBUG: Info:	 Time for Training: 0.0721070766449[s]
+2016-09-06 10:24:48,679 DEBUG: Done:	 Training
+2016-09-06 10:24:48,679 DEBUG: Start:	 Predicting
+2016-09-06 10:24:48,684 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:48,684 DEBUG: Start:	 Training
+2016-09-06 10:24:48,693 DEBUG: Done:	 Predicting
+2016-09-06 10:24:48,693 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:48,694 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:48,694 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.628571428571
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:48,695 INFO: Done:	 Result Analysis
+2016-09-06 10:24:48,703 DEBUG: Info:	 Time for Training: 0.0963509082794[s]
+2016-09-06 10:24:48,703 DEBUG: Done:	 Training
+2016-09-06 10:24:48,703 DEBUG: Start:	 Predicting
+2016-09-06 10:24:48,706 DEBUG: Done:	 Predicting
+2016-09-06 10:24:48,707 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:48,708 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:48,708 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.42380952381
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.42380952381
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:48,708 INFO: Done:	 Result Analysis
+2016-09-06 10:24:48,851 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:48,851 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:48,851 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:24:48,851 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:24:48,852 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:48,852 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:48,852 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:48,852 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:48,852 DEBUG: Info:	 Shape X_train:(210, 11), Length of y_train:210
+2016-09-06 10:24:48,852 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:48,853 DEBUG: Info:	 Shape X_test:(90, 11), Length of y_test:90
+2016-09-06 10:24:48,853 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:48,853 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:48,853 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:48,902 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:48,902 DEBUG: Start:	 Training
+2016-09-06 10:24:48,903 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:48,903 DEBUG: Start:	 Training
+2016-09-06 10:24:48,918 DEBUG: Info:	 Time for Training: 0.066841840744[s]
+2016-09-06 10:24:48,918 DEBUG: Done:	 Training
+2016-09-06 10:24:48,918 DEBUG: Start:	 Predicting
+2016-09-06 10:24:48,921 DEBUG: Done:	 Predicting
+2016-09-06 10:24:48,922 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:48,922 DEBUG: Info:	 Time for Training: 0.0710518360138[s]
+2016-09-06 10:24:48,922 DEBUG: Done:	 Training
+2016-09-06 10:24:48,922 DEBUG: Start:	 Predicting
+2016-09-06 10:24:48,923 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:48,923 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:48,923 INFO: Done:	 Result Analysis
+2016-09-06 10:24:48,928 DEBUG: Done:	 Predicting
+2016-09-06 10:24:48,928 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:48,929 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:48,929 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:48,929 INFO: Done:	 Result Analysis
+2016-09-06 10:24:48,999 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,000 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:24:49,000 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:49,000 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,000 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:24:49,001 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:49,001 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,001 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,001 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,001 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:49,002 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,002 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,002 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:49,002 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,040 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:49,040 DEBUG: Start:	 Training
+2016-09-06 10:24:49,042 DEBUG: Info:	 Time for Training: 0.0424060821533[s]
+2016-09-06 10:24:49,042 DEBUG: Done:	 Training
+2016-09-06 10:24:49,042 DEBUG: Start:	 Predicting
+2016-09-06 10:24:49,045 DEBUG: Done:	 Predicting
+2016-09-06 10:24:49,046 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:49,047 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:49,047 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,048 INFO: Done:	 Result Analysis
+2016-09-06 10:24:49,051 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:49,051 DEBUG: Start:	 Training
+2016-09-06 10:24:49,054 DEBUG: Info:	 Time for Training: 0.0557050704956[s]
+2016-09-06 10:24:49,055 DEBUG: Done:	 Training
+2016-09-06 10:24:49,055 DEBUG: Start:	 Predicting
+2016-09-06 10:24:49,057 DEBUG: Done:	 Predicting
+2016-09-06 10:24:49,058 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:49,060 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:49,060 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,060 INFO: Done:	 Result Analysis
+2016-09-06 10:24:49,150 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,151 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:24:49,151 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:49,151 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,152 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,152 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:49,152 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,152 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,152 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:24:49,152 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:49,153 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,153 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,154 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:49,154 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,185 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:49,185 DEBUG: Start:	 Training
+2016-09-06 10:24:49,185 DEBUG: Info:	 Time for Training: 0.0358011722565[s]
+2016-09-06 10:24:49,185 DEBUG: Done:	 Training
+2016-09-06 10:24:49,186 DEBUG: Start:	 Predicting
+2016-09-06 10:24:49,192 DEBUG: Done:	 Predicting
+2016-09-06 10:24:49,192 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:49,193 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:49,193 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.57619047619
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 26
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.57619047619
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,194 INFO: Done:	 Result Analysis
+2016-09-06 10:24:49,647 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:49,647 DEBUG: Start:	 Training
+2016-09-06 10:24:49,717 DEBUG: Info:	 Time for Training: 0.565485954285[s]
+2016-09-06 10:24:49,717 DEBUG: Done:	 Training
+2016-09-06 10:24:49,717 DEBUG: Start:	 Predicting
+2016-09-06 10:24:49,725 DEBUG: Done:	 Predicting
+2016-09-06 10:24:49,725 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:49,726 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:49,726 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 26, max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,727 INFO: Done:	 Result Analysis
+2016-09-06 10:24:49,800 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:24:49,801 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:24:49,801 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:49,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:24:49,801 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:24:49,802 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,802 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:24:49,802 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,802 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:24:49,802 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:49,802 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,802 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:24:49,802 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:24:49,847 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:49,847 DEBUG: Start:	 Training
+2016-09-06 10:24:49,848 DEBUG: Info:	 Time for Training: 0.0476620197296[s]
+2016-09-06 10:24:49,848 DEBUG: Done:	 Training
+2016-09-06 10:24:49,848 DEBUG: Start:	 Predicting
+2016-09-06 10:24:49,854 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:24:49,854 DEBUG: Start:	 Training
+2016-09-06 10:24:49,869 DEBUG: Done:	 Predicting
+2016-09-06 10:24:49,870 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:49,872 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:49,872 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,872 INFO: Done:	 Result Analysis
+2016-09-06 10:24:49,879 DEBUG: Info:	 Time for Training: 0.0796000957489[s]
+2016-09-06 10:24:49,879 DEBUG: Done:	 Training
+2016-09-06 10:24:49,879 DEBUG: Start:	 Predicting
+2016-09-06 10:24:49,883 DEBUG: Done:	 Predicting
+2016-09-06 10:24:49,883 DEBUG: Start:	 Getting Results
+2016-09-06 10:24:49,884 DEBUG: Done:	 Getting Results
+2016-09-06 10:24:49,884 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:24:49,884 INFO: Done:	 Result Analysis
+2016-09-06 10:24:50,099 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:24:50,100 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:24:50,100 INFO: Info:	 Shape of View0 :(300, 9)
+2016-09-06 10:24:50,101 INFO: Info:	 Shape of View1 :(300, 16)
+2016-09-06 10:24:50,101 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:24:50,101 INFO: Info:	 Shape of View2 :(300, 11)
+2016-09-06 10:24:50,101 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:24:50,102 INFO: Info:	 Shape of View3 :(300, 10)
+2016-09-06 10:24:50,102 INFO: Info:	 Shape of View0 :(300, 9)
+2016-09-06 10:24:50,102 INFO: Done:	 Read Database Files
+2016-09-06 10:24:50,102 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:24:50,102 INFO: Info:	 Shape of View1 :(300, 16)
+2016-09-06 10:24:50,103 INFO: Info:	 Shape of View2 :(300, 11)
+2016-09-06 10:24:50,104 INFO: Info:	 Shape of View3 :(300, 10)
+2016-09-06 10:24:50,104 INFO: Done:	 Read Database Files
+2016-09-06 10:24:50,104 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:24:50,106 INFO: Done:	 Determine validation split
+2016-09-06 10:24:50,106 INFO: Start:	 Determine 5 folds
+2016-09-06 10:24:50,108 INFO: Done:	 Determine validation split
+2016-09-06 10:24:50,108 INFO: Start:	 Determine 5 folds
+2016-09-06 10:24:50,116 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:24:50,116 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:24:50,116 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:24:50,117 INFO: Done:	 Determine folds
+2016-09-06 10:24:50,117 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:24:50,117 INFO: Start:	 Classification
+2016-09-06 10:24:50,117 INFO: 	Start:	 Fold number 1
+2016-09-06 10:24:50,118 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:24:50,118 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:24:50,118 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:24:50,118 INFO: Done:	 Determine folds
+2016-09-06 10:24:50,119 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:24:50,119 INFO: Start:	 Classification
+2016-09-06 10:24:50,119 INFO: 	Start:	 Fold number 1
+2016-09-06 10:24:50,149 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:24:50,158 DEBUG: 			View 0 : 0.479289940828
+2016-09-06 10:24:50,167 DEBUG: 			View 1 : 0.526627218935
+2016-09-06 10:24:50,175 DEBUG: 			View 2 : 0.538461538462
+2016-09-06 10:24:50,184 DEBUG: 			View 3 : 0.532544378698
+2016-09-06 10:24:50,217 DEBUG: 			 Best view : 		View2
+2016-09-06 10:24:50,296 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:24:50,304 DEBUG: 			View 0 : 0.704142011834
+2016-09-06 10:24:50,312 DEBUG: 			View 1 : 0.644970414201
+2016-09-06 10:24:50,320 DEBUG: 			View 2 : 0.668639053254
+2016-09-06 10:24:50,328 DEBUG: 			View 3 : 0.739644970414
+2016-09-06 10:24:50,366 DEBUG: 			 Best view : 		View3
+2016-09-06 10:24:50,510 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:24:50,521 DEBUG: 			View 0 : 0.704142011834
+2016-09-06 10:24:50,529 DEBUG: 			View 1 : 0.644970414201
+2016-09-06 10:24:50,537 DEBUG: 			View 2 : 0.668639053254
+2016-09-06 10:24:50,544 DEBUG: 			View 3 : 0.739644970414
+2016-09-06 10:24:50,582 DEBUG: 			 Best view : 		View3
+2016-09-06 10:24:50,783 INFO: 	Start: 	 Classification
+2016-09-06 10:24:51,128 INFO: 	Done: 	 Fold number 1
+2016-09-06 10:24:51,128 INFO: 	Start:	 Fold number 2
+2016-09-06 10:24:51,157 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:24:51,164 DEBUG: 			View 0 : 0.502958579882
+2016-09-06 10:24:51,172 DEBUG: 			View 1 : 0.526627218935
+2016-09-06 10:24:51,179 DEBUG: 			View 2 : 0.479289940828
+2016-09-06 10:24:51,186 DEBUG: 			View 3 : 0.562130177515
+2016-09-06 10:24:51,216 DEBUG: 			 Best view : 		View0
+2016-09-06 10:24:51,291 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:24:51,298 DEBUG: 			View 0 : 0.656804733728
+2016-09-06 10:24:51,306 DEBUG: 			View 1 : 0.710059171598
+2016-09-06 10:24:51,313 DEBUG: 			View 2 : 0.745562130178
+2016-09-06 10:24:51,321 DEBUG: 			View 3 : 0.757396449704
+2016-09-06 10:24:51,357 DEBUG: 			 Best view : 		View3
+2016-09-06 10:24:51,497 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:24:51,505 DEBUG: 			View 0 : 0.656804733728
+2016-09-06 10:24:51,512 DEBUG: 			View 1 : 0.710059171598
+2016-09-06 10:24:51,520 DEBUG: 			View 2 : 0.745562130178
+2016-09-06 10:24:51,527 DEBUG: 			View 3 : 0.757396449704
+2016-09-06 10:24:51,566 DEBUG: 			 Best view : 		View3
+2016-09-06 10:24:51,768 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:24:51,776 DEBUG: 			View 0 : 0.639053254438
+2016-09-06 10:24:51,783 DEBUG: 			View 1 : 0.627218934911
+2016-09-06 10:24:51,791 DEBUG: 			View 2 : 0.721893491124
+2016-09-06 10:24:51,798 DEBUG: 			View 3 : 0.692307692308
+2016-09-06 10:24:51,839 DEBUG: 			 Best view : 		View2
+2016-09-06 10:24:52,104 INFO: 	Start: 	 Classification
+2016-09-06 10:24:52,553 INFO: 	Done: 	 Fold number 2
+2016-09-06 10:24:52,553 INFO: 	Start:	 Fold number 3
+2016-09-06 10:24:52,582 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:24:52,589 DEBUG: 			View 0 : 0.467455621302
+2016-09-06 10:24:52,596 DEBUG: 			View 1 : 0.550295857988
+2016-09-06 10:24:52,603 DEBUG: 			View 2 : 0.520710059172
+2016-09-06 10:24:52,609 DEBUG: 			View 3 : 0.479289940828
+2016-09-06 10:24:52,640 DEBUG: 			 Best view : 		View0
+2016-09-06 10:24:52,714 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:24:52,722 DEBUG: 			View 0 : 0.721893491124
+2016-09-06 10:24:52,729 DEBUG: 			View 1 : 0.674556213018
+2016-09-06 10:24:52,737 DEBUG: 			View 2 : 0.656804733728
+2016-09-06 10:24:52,744 DEBUG: 			View 3 : 0.674556213018
+2016-09-06 10:24:52,780 DEBUG: 			 Best view : 		View0
+2016-09-06 10:24:52,919 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:24:52,926 DEBUG: 			View 0 : 0.721893491124
+2016-09-06 10:24:52,933 DEBUG: 			View 1 : 0.674556213018
+2016-09-06 10:24:52,941 DEBUG: 			View 2 : 0.656804733728
+2016-09-06 10:24:52,948 DEBUG: 			View 3 : 0.674556213018
+2016-09-06 10:24:52,987 DEBUG: 			 Best view : 		View0
+2016-09-06 10:24:53,188 INFO: 	Start: 	 Classification
+2016-09-06 10:24:53,527 INFO: 	Done: 	 Fold number 3
+2016-09-06 10:24:53,527 INFO: 	Start:	 Fold number 4
+2016-09-06 10:24:53,556 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:24:53,563 DEBUG: 			View 0 : 0.526627218935
+2016-09-06 10:24:53,570 DEBUG: 			View 1 : 0.485207100592
+2016-09-06 10:24:53,577 DEBUG: 			View 2 : 0.514792899408
+2016-09-06 10:24:53,584 DEBUG: 			View 3 : 0.497041420118
+2016-09-06 10:24:53,614 DEBUG: 			 Best view : 		View1
+2016-09-06 10:24:53,689 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:24:53,696 DEBUG: 			View 0 : 0.692307692308
+2016-09-06 10:24:53,704 DEBUG: 			View 1 : 0.680473372781
+2016-09-06 10:24:53,711 DEBUG: 			View 2 : 0.739644970414
+2016-09-06 10:24:53,719 DEBUG: 			View 3 : 0.615384615385
+2016-09-06 10:24:53,755 DEBUG: 			 Best view : 		View2
+2016-09-06 10:24:53,893 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:24:53,900 DEBUG: 			View 0 : 0.692307692308
+2016-09-06 10:24:53,908 DEBUG: 			View 1 : 0.615384615385
+2016-09-06 10:24:53,915 DEBUG: 			View 2 : 0.739644970414
+2016-09-06 10:24:53,923 DEBUG: 			View 3 : 0.615384615385
+2016-09-06 10:24:53,960 DEBUG: 			 Best view : 		View2
+2016-09-06 10:24:54,163 INFO: 	Start: 	 Classification
+2016-09-06 10:24:54,501 INFO: 	Done: 	 Fold number 4
+2016-09-06 10:24:54,501 INFO: 	Start:	 Fold number 5
+2016-09-06 10:24:54,530 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:24:54,538 DEBUG: 			View 0 : 0.538461538462
+2016-09-06 10:24:54,545 DEBUG: 			View 1 : 0.556213017751
+2016-09-06 10:24:54,552 DEBUG: 			View 2 : 0.479289940828
+2016-09-06 10:24:54,560 DEBUG: 			View 3 : 0.550295857988
+2016-09-06 10:24:54,590 DEBUG: 			 Best view : 		View2
+2016-09-06 10:24:54,665 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:24:54,672 DEBUG: 			View 0 : 0.686390532544
+2016-09-06 10:24:54,680 DEBUG: 			View 1 : 0.710059171598
+2016-09-06 10:24:54,687 DEBUG: 			View 2 : 0.745562130178
+2016-09-06 10:24:54,695 DEBUG: 			View 3 : 0.763313609467
+2016-09-06 10:24:54,731 DEBUG: 			 Best view : 		View3
+2016-09-06 10:24:54,871 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:24:54,878 DEBUG: 			View 0 : 0.710059171598
+2016-09-06 10:24:54,886 DEBUG: 			View 1 : 0.710059171598
+2016-09-06 10:24:54,893 DEBUG: 			View 2 : 0.745562130178
+2016-09-06 10:24:54,900 DEBUG: 			View 3 : 0.763313609467
+2016-09-06 10:24:54,939 DEBUG: 			 Best view : 		View3
+2016-09-06 10:24:55,142 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:24:55,149 DEBUG: 			View 0 : 0.680473372781
+2016-09-06 10:24:55,157 DEBUG: 			View 1 : 0.668639053254
+2016-09-06 10:24:55,164 DEBUG: 			View 2 : 0.775147928994
+2016-09-06 10:24:55,172 DEBUG: 			View 3 : 0.698224852071
+2016-09-06 10:24:55,213 DEBUG: 			 Best view : 		View2
+2016-09-06 10:24:55,477 INFO: 	Start: 	 Classification
+2016-09-06 10:24:55,929 INFO: 	Done: 	 Fold number 5
+2016-09-06 10:24:55,930 INFO: Done:	 Classification
+2016-09-06 10:24:55,930 INFO: Info:	 Time for Classification: 5[s]
+2016-09-06 10:24:55,930 INFO: Start:	 Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0a29c62f3e32e23b6606421aa079b827c934d979
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..50d1ffc426c81b549e261486aba92e796abadc90
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ec4b6b244ea621fbbd2c9e35b97a7071e3e032d4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.642857142857
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 26
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.642857142857
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f6c913520ecd71b9863e4ba56aa82083337a032e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 26, max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..99221590745bb8c9c5fbfe598775de43496596b2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ff3894ab6513257fb8739eecf6b789e65e116e08
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d1109d9fb625d0c89a52880e5ae0e964b55c72db
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 26
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..68f5fddd55b9eb79dfe3605602826ad8eb27eeea
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.595238095238
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.595238095238
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2ddf8a810a4f724be229872a570637dbb5a98ce8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8f0337470a622cce8d1e40718deca066ac70b21c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..417cdc1146fec8514d165717f1911019c6490ab6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..803d6342fe6f3366e9a977258b75c4f6c889fe3a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1bfba82f407e7d9fcfc31aba43fa118ba624a621
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7fbf2011cd3629064447a1e63db67afd753d2b49
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 26
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4792d66c67f3f3cf27bb115c07f5ae1096f1fd75
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 26, max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..886be2013e2a96b1192f0998a12b2f09e139d234
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.377777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8612a084aa35a3416a20728aa22570233df8b6ee
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.485714285714
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2421faf6a81d4b9a998547fd8da87efdf09285de
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bfb5e334cf003155dde149912bfb030e9532cec5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 16)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..90bc016315849eebfbd723a5967ae7e704558590
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 26, max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5cf265c51a8b13b86719047a88d06c3e05806e5d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.628571428571
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.628571428571
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d333a548beb19b94e9a0ec49d870ba2f197543eb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.42380952381
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.42380952381
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9bb3ab9502b26f86a0dff649d5b336283db18d46
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3985361b9097bbf774cf8a63fc5aac32b76e584b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 11)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..81641b03b13251625bf3108426bdabb188c77568
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b6a08cb0d94534e1fb6fdb548bccedbf64c80c4c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..774d9b323e609480cea44d712206ebc1dce8e8fc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.57619047619
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 26
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.57619047619
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ca851391b9530aaf1fa9ae5626dbd7d7fee8bb5b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 26, max_depth : 18
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4672960d9a77459d08cfec74f582ca76b108b6cc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7f7f53ac0c08482c08b5117b9eaa499e614a38b6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5274
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..5e79d40609d8f17f6157d51eb27c0e6249fe4b7e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1623 @@
+2016-09-06 10:25:43,492 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:25:43,492 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00020134375 Gbytes /!\ 
+2016-09-06 10:25:48,507 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:25:48,510 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:25:48,559 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:48,559 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:48,560 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:25:48,560 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:25:48,560 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:48,560 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:48,560 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:48,560 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:48,561 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:48,561 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:48,561 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:48,561 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:48,561 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:48,561 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:48,604 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:48,604 DEBUG: Start:	 Training
+2016-09-06 10:25:48,607 DEBUG: Info:	 Time for Training: 0.0481219291687[s]
+2016-09-06 10:25:48,607 DEBUG: Done:	 Training
+2016-09-06 10:25:48,607 DEBUG: Start:	 Predicting
+2016-09-06 10:25:48,609 DEBUG: Done:	 Predicting
+2016-09-06 10:25:48,610 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:48,611 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:48,611 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.890476190476
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:48,611 INFO: Done:	 Result Analysis
+2016-09-06 10:25:48,619 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:48,619 DEBUG: Start:	 Training
+2016-09-06 10:25:48,624 DEBUG: Info:	 Time for Training: 0.0653121471405[s]
+2016-09-06 10:25:48,624 DEBUG: Done:	 Training
+2016-09-06 10:25:48,624 DEBUG: Start:	 Predicting
+2016-09-06 10:25:48,627 DEBUG: Done:	 Predicting
+2016-09-06 10:25:48,627 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:48,629 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:48,629 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:48,629 INFO: Done:	 Result Analysis
+2016-09-06 10:25:48,709 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:48,709 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:48,710 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:25:48,710 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:25:48,710 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:48,710 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:48,710 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:48,710 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:48,711 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:48,711 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:48,711 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:48,711 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:48,711 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:48,711 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:48,767 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:48,767 DEBUG: Start:	 Training
+2016-09-06 10:25:48,768 DEBUG: Info:	 Time for Training: 0.0595369338989[s]
+2016-09-06 10:25:48,768 DEBUG: Done:	 Training
+2016-09-06 10:25:48,768 DEBUG: Start:	 Predicting
+2016-09-06 10:25:48,780 DEBUG: Done:	 Predicting
+2016-09-06 10:25:48,780 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:48,782 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:48,782 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 41
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:48,783 INFO: Done:	 Result Analysis
+2016-09-06 10:25:49,068 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:49,068 DEBUG: Start:	 Training
+2016-09-06 10:25:49,115 DEBUG: Info:	 Time for Training: 0.406928062439[s]
+2016-09-06 10:25:49,116 DEBUG: Done:	 Training
+2016-09-06 10:25:49,116 DEBUG: Start:	 Predicting
+2016-09-06 10:25:49,122 DEBUG: Done:	 Predicting
+2016-09-06 10:25:49,122 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:49,123 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:49,123 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 18, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,123 INFO: Done:	 Result Analysis
+2016-09-06 10:25:49,263 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,263 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,264 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:25:49,264 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:25:49,264 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:49,264 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:49,265 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,265 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,265 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,265 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,265 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:49,265 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:49,265 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,265 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,313 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:49,313 DEBUG: Start:	 Training
+2016-09-06 10:25:49,314 DEBUG: Info:	 Time for Training: 0.0512311458588[s]
+2016-09-06 10:25:49,314 DEBUG: Done:	 Training
+2016-09-06 10:25:49,314 DEBUG: Start:	 Predicting
+2016-09-06 10:25:49,320 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:49,320 DEBUG: Start:	 Training
+2016-09-06 10:25:49,330 DEBUG: Done:	 Predicting
+2016-09-06 10:25:49,330 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:49,332 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:49,332 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,332 INFO: Done:	 Result Analysis
+2016-09-06 10:25:49,344 DEBUG: Info:	 Time for Training: 0.0809261798859[s]
+2016-09-06 10:25:49,344 DEBUG: Done:	 Training
+2016-09-06 10:25:49,344 DEBUG: Start:	 Predicting
+2016-09-06 10:25:49,348 DEBUG: Done:	 Predicting
+2016-09-06 10:25:49,348 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:49,349 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:49,349 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.466666666667
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,349 INFO: Done:	 Result Analysis
+2016-09-06 10:25:49,411 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,411 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,412 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:25:49,412 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:25:49,412 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:49,412 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:49,413 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,413 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,413 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,413 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:49,413 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,413 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,413 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:49,413 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,462 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:49,462 DEBUG: Start:	 Training
+2016-09-06 10:25:49,472 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:49,472 DEBUG: Start:	 Training
+2016-09-06 10:25:49,480 DEBUG: Info:	 Time for Training: 0.0694200992584[s]
+2016-09-06 10:25:49,480 DEBUG: Done:	 Training
+2016-09-06 10:25:49,480 DEBUG: Start:	 Predicting
+2016-09-06 10:25:49,486 DEBUG: Done:	 Predicting
+2016-09-06 10:25:49,486 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:49,487 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:49,488 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,488 INFO: Done:	 Result Analysis
+2016-09-06 10:25:49,494 DEBUG: Info:	 Time for Training: 0.0838282108307[s]
+2016-09-06 10:25:49,495 DEBUG: Done:	 Training
+2016-09-06 10:25:49,495 DEBUG: Start:	 Predicting
+2016-09-06 10:25:49,499 DEBUG: Done:	 Predicting
+2016-09-06 10:25:49,499 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:49,500 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:49,501 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,501 INFO: Done:	 Result Analysis
+2016-09-06 10:25:49,565 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,565 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,566 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:25:49,566 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:25:49,566 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:49,566 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:49,567 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,567 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,567 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,567 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,567 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:49,567 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:49,568 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,568 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,617 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:49,617 DEBUG: Start:	 Training
+2016-09-06 10:25:49,620 DEBUG: Info:	 Time for Training: 0.0563011169434[s]
+2016-09-06 10:25:49,620 DEBUG: Done:	 Training
+2016-09-06 10:25:49,620 DEBUG: Start:	 Predicting
+2016-09-06 10:25:49,623 DEBUG: Done:	 Predicting
+2016-09-06 10:25:49,623 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:49,624 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:49,624 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,625 INFO: Done:	 Result Analysis
+2016-09-06 10:25:49,632 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:49,632 DEBUG: Start:	 Training
+2016-09-06 10:25:49,636 DEBUG: Info:	 Time for Training: 0.072704076767[s]
+2016-09-06 10:25:49,636 DEBUG: Done:	 Training
+2016-09-06 10:25:49,637 DEBUG: Start:	 Predicting
+2016-09-06 10:25:49,639 DEBUG: Done:	 Predicting
+2016-09-06 10:25:49,639 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:49,641 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:49,641 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,642 INFO: Done:	 Result Analysis
+2016-09-06 10:25:49,706 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,706 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:49,706 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:25:49,706 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:25:49,706 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:49,706 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:49,707 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,707 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:49,707 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,707 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:49,707 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:49,707 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:49,707 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,707 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:49,745 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:49,745 DEBUG: Start:	 Training
+2016-09-06 10:25:49,745 DEBUG: Info:	 Time for Training: 0.0398440361023[s]
+2016-09-06 10:25:49,745 DEBUG: Done:	 Training
+2016-09-06 10:25:49,746 DEBUG: Start:	 Predicting
+2016-09-06 10:25:49,755 DEBUG: Done:	 Predicting
+2016-09-06 10:25:49,755 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:49,756 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:49,756 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 41
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:49,756 INFO: Done:	 Result Analysis
+2016-09-06 10:25:50,033 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:50,033 DEBUG: Start:	 Training
+2016-09-06 10:25:50,081 DEBUG: Info:	 Time for Training: 0.37574505806[s]
+2016-09-06 10:25:50,081 DEBUG: Done:	 Training
+2016-09-06 10:25:50,082 DEBUG: Start:	 Predicting
+2016-09-06 10:25:50,087 DEBUG: Done:	 Predicting
+2016-09-06 10:25:50,088 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:50,089 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:50,089 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 18, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,089 INFO: Done:	 Result Analysis
+2016-09-06 10:25:50,155 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,156 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:25:50,156 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:50,156 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,156 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:25:50,156 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:50,156 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,157 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,157 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,157 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,157 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:50,157 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:50,157 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,157 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,204 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:50,204 DEBUG: Start:	 Training
+2016-09-06 10:25:50,205 DEBUG: Info:	 Time for Training: 0.0503079891205[s]
+2016-09-06 10:25:50,205 DEBUG: Done:	 Training
+2016-09-06 10:25:50,205 DEBUG: Start:	 Predicting
+2016-09-06 10:25:50,216 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:50,216 DEBUG: Start:	 Training
+2016-09-06 10:25:50,232 DEBUG: Done:	 Predicting
+2016-09-06 10:25:50,232 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:50,236 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:50,236 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.514285714286
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,236 INFO: Done:	 Result Analysis
+2016-09-06 10:25:50,245 DEBUG: Info:	 Time for Training: 0.0895159244537[s]
+2016-09-06 10:25:50,245 DEBUG: Done:	 Training
+2016-09-06 10:25:50,245 DEBUG: Start:	 Predicting
+2016-09-06 10:25:50,249 DEBUG: Done:	 Predicting
+2016-09-06 10:25:50,249 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:50,250 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:50,251 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.480952380952
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.480952380952
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,251 INFO: Done:	 Result Analysis
+2016-09-06 10:25:50,403 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,403 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,403 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:25:50,403 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:50,403 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:25:50,403 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:50,404 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,404 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,404 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:50,404 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,404 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,404 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,404 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:50,405 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,454 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:50,454 DEBUG: Start:	 Training
+2016-09-06 10:25:50,466 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:50,466 DEBUG: Start:	 Training
+2016-09-06 10:25:50,472 DEBUG: Info:	 Time for Training: 0.0699520111084[s]
+2016-09-06 10:25:50,472 DEBUG: Done:	 Training
+2016-09-06 10:25:50,472 DEBUG: Start:	 Predicting
+2016-09-06 10:25:50,479 DEBUG: Done:	 Predicting
+2016-09-06 10:25:50,479 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:50,480 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:50,480 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,480 INFO: Done:	 Result Analysis
+2016-09-06 10:25:50,487 DEBUG: Info:	 Time for Training: 0.084676027298[s]
+2016-09-06 10:25:50,487 DEBUG: Done:	 Training
+2016-09-06 10:25:50,487 DEBUG: Start:	 Predicting
+2016-09-06 10:25:50,493 DEBUG: Done:	 Predicting
+2016-09-06 10:25:50,493 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:50,494 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:50,495 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,495 INFO: Done:	 Result Analysis
+2016-09-06 10:25:50,549 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,549 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:25:50,549 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:50,549 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,549 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:25:50,549 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:50,550 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,550 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,550 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,550 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:50,550 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,550 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:50,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,594 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:50,594 DEBUG: Start:	 Training
+2016-09-06 10:25:50,598 DEBUG: Info:	 Time for Training: 0.0494570732117[s]
+2016-09-06 10:25:50,598 DEBUG: Done:	 Training
+2016-09-06 10:25:50,598 DEBUG: Start:	 Predicting
+2016-09-06 10:25:50,601 DEBUG: Done:	 Predicting
+2016-09-06 10:25:50,601 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:50,602 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:50,603 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.971428571429
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.971428571429
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,603 INFO: Done:	 Result Analysis
+2016-09-06 10:25:50,604 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:50,605 DEBUG: Start:	 Training
+2016-09-06 10:25:50,610 DEBUG: Info:	 Time for Training: 0.0612750053406[s]
+2016-09-06 10:25:50,610 DEBUG: Done:	 Training
+2016-09-06 10:25:50,610 DEBUG: Start:	 Predicting
+2016-09-06 10:25:50,613 DEBUG: Done:	 Predicting
+2016-09-06 10:25:50,613 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:50,615 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:50,615 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,615 INFO: Done:	 Result Analysis
+2016-09-06 10:25:50,695 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,695 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:50,695 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:25:50,695 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:25:50,696 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:50,696 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:50,696 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,696 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:50,696 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,696 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:50,696 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:50,697 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,697 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:50,697 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:50,731 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:50,732 DEBUG: Start:	 Training
+2016-09-06 10:25:50,732 DEBUG: Info:	 Time for Training: 0.0376601219177[s]
+2016-09-06 10:25:50,732 DEBUG: Done:	 Training
+2016-09-06 10:25:50,732 DEBUG: Start:	 Predicting
+2016-09-06 10:25:50,740 DEBUG: Done:	 Predicting
+2016-09-06 10:25:50,741 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:50,742 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:50,742 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 41
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.580952380952
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:50,742 INFO: Done:	 Result Analysis
+2016-09-06 10:25:51,022 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:51,022 DEBUG: Start:	 Training
+2016-09-06 10:25:51,071 DEBUG: Info:	 Time for Training: 0.37623500824[s]
+2016-09-06 10:25:51,071 DEBUG: Done:	 Training
+2016-09-06 10:25:51,071 DEBUG: Start:	 Predicting
+2016-09-06 10:25:51,077 DEBUG: Done:	 Predicting
+2016-09-06 10:25:51,077 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:51,079 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:51,079 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 18, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,079 INFO: Done:	 Result Analysis
+2016-09-06 10:25:51,152 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,152 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,153 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:25:51,153 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:25:51,153 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:51,153 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:51,154 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:51,154 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:51,154 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:51,154 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:51,154 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:51,154 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:51,154 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,154 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,224 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:51,225 DEBUG: Start:	 Training
+2016-09-06 10:25:51,226 DEBUG: Info:	 Time for Training: 0.0744400024414[s]
+2016-09-06 10:25:51,226 DEBUG: Done:	 Training
+2016-09-06 10:25:51,226 DEBUG: Start:	 Predicting
+2016-09-06 10:25:51,232 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:51,232 DEBUG: Start:	 Training
+2016-09-06 10:25:51,243 DEBUG: Done:	 Predicting
+2016-09-06 10:25:51,244 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:51,246 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:51,247 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.47619047619
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.47619047619
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,247 INFO: Done:	 Result Analysis
+2016-09-06 10:25:51,262 DEBUG: Info:	 Time for Training: 0.110822916031[s]
+2016-09-06 10:25:51,263 DEBUG: Done:	 Training
+2016-09-06 10:25:51,263 DEBUG: Start:	 Predicting
+2016-09-06 10:25:51,266 DEBUG: Done:	 Predicting
+2016-09-06 10:25:51,267 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:51,268 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:51,268 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.633333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,268 INFO: Done:	 Result Analysis
+2016-09-06 10:25:51,400 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,400 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,401 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:25:51,401 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:25:51,401 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:51,401 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:51,402 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:51,402 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:25:51,402 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:51,403 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:25:51,403 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:51,403 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:51,403 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,403 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,474 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:51,474 DEBUG: Start:	 Training
+2016-09-06 10:25:51,487 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:51,487 DEBUG: Start:	 Training
+2016-09-06 10:25:51,500 DEBUG: Info:	 Time for Training: 0.101441144943[s]
+2016-09-06 10:25:51,501 DEBUG: Done:	 Training
+2016-09-06 10:25:51,501 DEBUG: Start:	 Predicting
+2016-09-06 10:25:51,509 DEBUG: Done:	 Predicting
+2016-09-06 10:25:51,510 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:51,511 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:51,512 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,512 INFO: Done:	 Result Analysis
+2016-09-06 10:25:51,513 DEBUG: Info:	 Time for Training: 0.114230155945[s]
+2016-09-06 10:25:51,513 DEBUG: Done:	 Training
+2016-09-06 10:25:51,513 DEBUG: Start:	 Predicting
+2016-09-06 10:25:51,518 DEBUG: Done:	 Predicting
+2016-09-06 10:25:51,518 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:51,519 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:51,519 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,519 INFO: Done:	 Result Analysis
+2016-09-06 10:25:51,649 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,649 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,649 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:25:51,649 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:25:51,650 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:51,650 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:51,650 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:51,650 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:51,650 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:51,650 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:51,650 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:51,651 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,651 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:51,651 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,688 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:51,688 DEBUG: Start:	 Training
+2016-09-06 10:25:51,690 DEBUG: Info:	 Time for Training: 0.0416340827942[s]
+2016-09-06 10:25:51,690 DEBUG: Done:	 Training
+2016-09-06 10:25:51,690 DEBUG: Start:	 Predicting
+2016-09-06 10:25:51,693 DEBUG: Done:	 Predicting
+2016-09-06 10:25:51,693 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:51,694 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:51,694 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.914285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.914285714286
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,695 INFO: Done:	 Result Analysis
+2016-09-06 10:25:51,703 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:51,703 DEBUG: Start:	 Training
+2016-09-06 10:25:51,708 DEBUG: Info:	 Time for Training: 0.0598511695862[s]
+2016-09-06 10:25:51,708 DEBUG: Done:	 Training
+2016-09-06 10:25:51,709 DEBUG: Start:	 Predicting
+2016-09-06 10:25:51,712 DEBUG: Done:	 Predicting
+2016-09-06 10:25:51,712 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:51,713 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:51,714 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,714 INFO: Done:	 Result Analysis
+2016-09-06 10:25:51,800 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,800 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:51,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:25:51,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:25:51,801 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:51,801 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:51,802 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:51,802 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:51,802 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:51,802 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:51,802 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:51,802 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:51,802 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,802 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:51,853 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:51,853 DEBUG: Start:	 Training
+2016-09-06 10:25:51,854 DEBUG: Info:	 Time for Training: 0.0547788143158[s]
+2016-09-06 10:25:51,855 DEBUG: Done:	 Training
+2016-09-06 10:25:51,855 DEBUG: Start:	 Predicting
+2016-09-06 10:25:51,866 DEBUG: Done:	 Predicting
+2016-09-06 10:25:51,866 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:51,868 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:51,868 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 41
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:51,868 INFO: Done:	 Result Analysis
+2016-09-06 10:25:52,153 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:52,153 DEBUG: Start:	 Training
+2016-09-06 10:25:52,201 DEBUG: Info:	 Time for Training: 0.401795864105[s]
+2016-09-06 10:25:52,202 DEBUG: Done:	 Training
+2016-09-06 10:25:52,202 DEBUG: Start:	 Predicting
+2016-09-06 10:25:52,208 DEBUG: Done:	 Predicting
+2016-09-06 10:25:52,208 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:52,209 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:52,209 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 18, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:52,209 INFO: Done:	 Result Analysis
+2016-09-06 10:25:52,349 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:52,350 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:25:52,350 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:25:52,350 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:25:52,350 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:52,350 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:25:52,351 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:52,351 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:25:52,351 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:52,351 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:25:52,351 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:52,351 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:25:52,351 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:52,351 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:25:52,399 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:52,399 DEBUG: Start:	 Training
+2016-09-06 10:25:52,400 DEBUG: Info:	 Time for Training: 0.0507619380951[s]
+2016-09-06 10:25:52,400 DEBUG: Done:	 Training
+2016-09-06 10:25:52,400 DEBUG: Start:	 Predicting
+2016-09-06 10:25:52,407 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:25:52,407 DEBUG: Start:	 Training
+2016-09-06 10:25:52,425 DEBUG: Done:	 Predicting
+2016-09-06 10:25:52,426 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:52,427 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:52,427 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.644444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.644444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:52,428 INFO: Done:	 Result Analysis
+2016-09-06 10:25:52,431 DEBUG: Info:	 Time for Training: 0.0818819999695[s]
+2016-09-06 10:25:52,431 DEBUG: Done:	 Training
+2016-09-06 10:25:52,431 DEBUG: Start:	 Predicting
+2016-09-06 10:25:52,435 DEBUG: Done:	 Predicting
+2016-09-06 10:25:52,435 DEBUG: Start:	 Getting Results
+2016-09-06 10:25:52,436 DEBUG: Done:	 Getting Results
+2016-09-06 10:25:52,436 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.452380952381
+accuracy_score on test : 0.388888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.452380952381
+		- Score on test : 0.388888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:25:52,436 INFO: Done:	 Result Analysis
+2016-09-06 10:25:52,647 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:25:52,647 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:25:52,648 INFO: Info:	 Shape of View0 :(300, 20)
+2016-09-06 10:25:52,649 INFO: Info:	 Shape of View1 :(300, 20)
+2016-09-06 10:25:52,649 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:25:52,649 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:25:52,649 INFO: Info:	 Shape of View2 :(300, 20)
+2016-09-06 10:25:52,650 INFO: Info:	 Shape of View3 :(300, 18)
+2016-09-06 10:25:52,650 INFO: Info:	 Shape of View0 :(300, 20)
+2016-09-06 10:25:52,650 INFO: Done:	 Read Database Files
+2016-09-06 10:25:52,651 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:25:52,652 INFO: Info:	 Shape of View1 :(300, 20)
+2016-09-06 10:25:52,653 INFO: Info:	 Shape of View2 :(300, 20)
+2016-09-06 10:25:52,654 INFO: Info:	 Shape of View3 :(300, 18)
+2016-09-06 10:25:52,654 INFO: Done:	 Read Database Files
+2016-09-06 10:25:52,654 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:25:52,656 INFO: Done:	 Determine validation split
+2016-09-06 10:25:52,656 INFO: Start:	 Determine 5 folds
+2016-09-06 10:25:52,658 INFO: Done:	 Determine validation split
+2016-09-06 10:25:52,658 INFO: Start:	 Determine 5 folds
+2016-09-06 10:25:52,665 INFO: Info:	 Length of Learning Sets: 168
+2016-09-06 10:25:52,665 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:25:52,666 INFO: Info:	 Length of Validation Set: 90
+2016-09-06 10:25:52,666 INFO: Done:	 Determine folds
+2016-09-06 10:25:52,666 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:25:52,666 INFO: Start:	 Classification
+2016-09-06 10:25:52,666 INFO: 	Start:	 Fold number 1
+2016-09-06 10:25:52,669 INFO: Info:	 Length of Learning Sets: 168
+2016-09-06 10:25:52,669 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:25:52,670 INFO: Info:	 Length of Validation Set: 90
+2016-09-06 10:25:52,670 INFO: Done:	 Determine folds
+2016-09-06 10:25:52,670 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:25:52,670 INFO: Start:	 Classification
+2016-09-06 10:25:52,670 INFO: 	Start:	 Fold number 1
+2016-09-06 10:25:52,699 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:25:52,708 DEBUG: 			View 0 : 0.434523809524
+2016-09-06 10:25:52,716 DEBUG: 			View 1 : 0.470238095238
+2016-09-06 10:25:52,724 DEBUG: 			View 2 : 0.505952380952
+2016-09-06 10:25:52,731 DEBUG: 			View 3 : 0.553571428571
+2016-09-06 10:25:52,763 DEBUG: 			 Best view : 		View0
+2016-09-06 10:25:52,841 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:25:52,849 DEBUG: 			View 0 : 0.720238095238
+2016-09-06 10:25:52,857 DEBUG: 			View 1 : 0.666666666667
+2016-09-06 10:25:52,865 DEBUG: 			View 2 : 0.660714285714
+2016-09-06 10:25:52,872 DEBUG: 			View 3 : 0.744047619048
+2016-09-06 10:25:52,909 DEBUG: 			 Best view : 		View3
+2016-09-06 10:25:53,053 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:25:53,071 DEBUG: 			View 0 : 0.720238095238
+2016-09-06 10:25:53,080 DEBUG: 			View 1 : 0.666666666667
+2016-09-06 10:25:53,088 DEBUG: 			View 2 : 0.660714285714
+2016-09-06 10:25:53,095 DEBUG: 			View 3 : 0.744047619048
+2016-09-06 10:25:53,134 DEBUG: 			 Best view : 		View3
+2016-09-06 10:25:53,338 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:25:53,345 DEBUG: 			View 0 : 0.77380952381
+2016-09-06 10:25:53,353 DEBUG: 			View 1 : 0.732142857143
+2016-09-06 10:25:53,360 DEBUG: 			View 2 : 0.678571428571
+2016-09-06 10:25:53,369 DEBUG: 			View 3 : 0.690476190476
+2016-09-06 10:25:53,419 DEBUG: 			 Best view : 		View0
+2016-09-06 10:25:53,697 INFO: 	Start: 	 Classification
+2016-09-06 10:25:54,148 INFO: 	Done: 	 Fold number 1
+2016-09-06 10:25:54,148 INFO: 	Start:	 Fold number 2
+2016-09-06 10:25:54,177 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:25:54,184 DEBUG: 			View 0 : 0.5
+2016-09-06 10:25:54,190 DEBUG: 			View 1 : 0.5
+2016-09-06 10:25:54,197 DEBUG: 			View 2 : 0.5
+2016-09-06 10:25:54,204 DEBUG: 			View 3 : 0.5
+2016-09-06 10:25:54,234 DEBUG: 			 Best view : 		View0
+2016-09-06 10:25:54,309 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:25:54,317 DEBUG: 			View 0 : 0.72619047619
+2016-09-06 10:25:54,325 DEBUG: 			View 1 : 0.761904761905
+2016-09-06 10:25:54,333 DEBUG: 			View 2 : 0.666666666667
+2016-09-06 10:25:54,341 DEBUG: 			View 3 : 0.732142857143
+2016-09-06 10:25:54,377 DEBUG: 			 Best view : 		View1
+2016-09-06 10:25:54,515 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:25:54,523 DEBUG: 			View 0 : 0.72619047619
+2016-09-06 10:25:54,531 DEBUG: 			View 1 : 0.761904761905
+2016-09-06 10:25:54,539 DEBUG: 			View 2 : 0.666666666667
+2016-09-06 10:25:54,546 DEBUG: 			View 3 : 0.732142857143
+2016-09-06 10:25:54,584 DEBUG: 			 Best view : 		View1
+2016-09-06 10:25:54,786 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:25:54,794 DEBUG: 			View 0 : 0.678571428571
+2016-09-06 10:25:54,802 DEBUG: 			View 1 : 0.744047619048
+2016-09-06 10:25:54,809 DEBUG: 			View 2 : 0.738095238095
+2016-09-06 10:25:54,817 DEBUG: 			View 3 : 0.708333333333
+2016-09-06 10:25:54,857 DEBUG: 			 Best view : 		View1
+2016-09-06 10:25:55,121 INFO: 	Start: 	 Classification
+2016-09-06 10:25:55,575 INFO: 	Done: 	 Fold number 2
+2016-09-06 10:25:55,575 INFO: 	Start:	 Fold number 3
+2016-09-06 10:25:55,604 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:25:55,611 DEBUG: 			View 0 : 0.577380952381
+2016-09-06 10:25:55,619 DEBUG: 			View 1 : 0.52380952381
+2016-09-06 10:25:55,626 DEBUG: 			View 2 : 0.47619047619
+2016-09-06 10:25:55,633 DEBUG: 			View 3 : 0.541666666667
+2016-09-06 10:25:55,664 DEBUG: 			 Best view : 		View0
+2016-09-06 10:25:55,738 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:25:55,746 DEBUG: 			View 0 : 0.72619047619
+2016-09-06 10:25:55,754 DEBUG: 			View 1 : 0.77380952381
+2016-09-06 10:25:55,762 DEBUG: 			View 2 : 0.714285714286
+2016-09-06 10:25:55,769 DEBUG: 			View 3 : 0.767857142857
+2016-09-06 10:25:55,805 DEBUG: 			 Best view : 		View1
+2016-09-06 10:25:55,944 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:25:55,952 DEBUG: 			View 0 : 0.72619047619
+2016-09-06 10:25:55,960 DEBUG: 			View 1 : 0.77380952381
+2016-09-06 10:25:55,967 DEBUG: 			View 2 : 0.714285714286
+2016-09-06 10:25:55,975 DEBUG: 			View 3 : 0.767857142857
+2016-09-06 10:25:56,013 DEBUG: 			 Best view : 		View1
+2016-09-06 10:25:56,215 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:25:56,223 DEBUG: 			View 0 : 0.72619047619
+2016-09-06 10:25:56,231 DEBUG: 			View 1 : 0.672619047619
+2016-09-06 10:25:56,239 DEBUG: 			View 2 : 0.738095238095
+2016-09-06 10:25:56,246 DEBUG: 			View 3 : 0.702380952381
+2016-09-06 10:25:56,287 DEBUG: 			 Best view : 		View2
+2016-09-06 10:25:56,554 INFO: 	Start: 	 Classification
+2016-09-06 10:25:57,010 INFO: 	Done: 	 Fold number 3
+2016-09-06 10:25:57,010 INFO: 	Start:	 Fold number 4
+2016-09-06 10:25:57,038 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:25:57,045 DEBUG: 			View 0 : 0.535714285714
+2016-09-06 10:25:57,053 DEBUG: 			View 1 : 0.5
+2016-09-06 10:25:57,060 DEBUG: 			View 2 : 0.440476190476
+2016-09-06 10:25:57,067 DEBUG: 			View 3 : 0.47619047619
+2016-09-06 10:25:57,097 DEBUG: 			 Best view : 		View3
+2016-09-06 10:25:57,171 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:25:57,178 DEBUG: 			View 0 : 0.779761904762
+2016-09-06 10:25:57,186 DEBUG: 			View 1 : 0.744047619048
+2016-09-06 10:25:57,193 DEBUG: 			View 2 : 0.702380952381
+2016-09-06 10:25:57,201 DEBUG: 			View 3 : 0.714285714286
+2016-09-06 10:25:57,237 DEBUG: 			 Best view : 		View0
+2016-09-06 10:25:57,376 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:25:57,383 DEBUG: 			View 0 : 0.779761904762
+2016-09-06 10:25:57,391 DEBUG: 			View 1 : 0.744047619048
+2016-09-06 10:25:57,399 DEBUG: 			View 2 : 0.702380952381
+2016-09-06 10:25:57,406 DEBUG: 			View 3 : 0.714285714286
+2016-09-06 10:25:57,445 DEBUG: 			 Best view : 		View0
+2016-09-06 10:25:57,648 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:25:57,656 DEBUG: 			View 0 : 0.72619047619
+2016-09-06 10:25:57,664 DEBUG: 			View 1 : 0.672619047619
+2016-09-06 10:25:57,671 DEBUG: 			View 2 : 0.690476190476
+2016-09-06 10:25:57,678 DEBUG: 			View 3 : 0.732142857143
+2016-09-06 10:25:57,719 DEBUG: 			 Best view : 		View3
+2016-09-06 10:25:57,985 INFO: 	Start: 	 Classification
+2016-09-06 10:25:58,436 INFO: 	Done: 	 Fold number 4
+2016-09-06 10:25:58,436 INFO: 	Start:	 Fold number 5
+2016-09-06 10:25:58,464 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:25:58,471 DEBUG: 			View 0 : 0.5
+2016-09-06 10:25:58,477 DEBUG: 			View 1 : 0.5
+2016-09-06 10:25:58,484 DEBUG: 			View 2 : 0.5
+2016-09-06 10:25:58,491 DEBUG: 			View 3 : 0.5
+2016-09-06 10:25:58,520 DEBUG: 			 Best view : 		View0
+2016-09-06 10:25:58,595 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:25:58,602 DEBUG: 			View 0 : 0.75
+2016-09-06 10:25:58,610 DEBUG: 			View 1 : 0.75
+2016-09-06 10:25:58,618 DEBUG: 			View 2 : 0.714285714286
+2016-09-06 10:25:58,625 DEBUG: 			View 3 : 0.738095238095
+2016-09-06 10:25:58,661 DEBUG: 			 Best view : 		View0
+2016-09-06 10:25:58,798 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:25:58,806 DEBUG: 			View 0 : 0.75
+2016-09-06 10:25:58,813 DEBUG: 			View 1 : 0.75
+2016-09-06 10:25:58,821 DEBUG: 			View 2 : 0.714285714286
+2016-09-06 10:25:58,829 DEBUG: 			View 3 : 0.738095238095
+2016-09-06 10:25:58,867 DEBUG: 			 Best view : 		View0
+2016-09-06 10:25:59,068 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:25:59,075 DEBUG: 			View 0 : 0.720238095238
+2016-09-06 10:25:59,083 DEBUG: 			View 1 : 0.642857142857
+2016-09-06 10:25:59,091 DEBUG: 			View 2 : 0.660714285714
+2016-09-06 10:25:59,098 DEBUG: 			View 3 : 0.678571428571
+2016-09-06 10:25:59,138 DEBUG: 			 Best view : 		View0
+2016-09-06 10:25:59,405 INFO: 	Start: 	 Classification
+2016-09-06 10:25:59,857 INFO: 	Done: 	 Fold number 5
+2016-09-06 10:25:59,857 INFO: Done:	 Classification
+2016-09-06 10:25:59,857 INFO: Info:	 Time for Classification: 7[s]
+2016-09-06 10:25:59,857 INFO: Start:	 Result Analysis for Mumbo
+2016-09-06 10:26:02,299 INFO: 		Result for Multiview classification with Mumbo
+
+Average accuracy :
+	-On Train : 76.1904761905
+	-On Test : 50.9523809524
+	-On Validation : 51.3333333333Dataset info :
+	-Dataset name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : View0 of shape (300, 20), View1 of shape (300, 20), View2 of shape (300, 20), View3 of shape (300, 18)
+	-5 folds
+	- Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration : 
+	-Used 1 core(s)
+	-Iterations : min 1, max 10, threshold 0.01
+	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:01        0:00:00
+	         Fold 2        0:00:02        0:00:00
+	         Fold 3        0:00:03        0:00:00
+	         Fold 4        0:00:05        0:00:00
+	         Fold 5        0:00:06        0:00:00
+	          Total        0:00:19        0:00:02
+	So a total classification time of 0:00:07.
+
+
+
+Mean average accuracies and stats for each fold :
+	- Fold 0, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.264880952381
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.253571428571
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.250595238095
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.273214285714
+			- Percentage of time chosen : 0.2
+	- Fold 1, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.263095238095
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.276785714286
+			- Percentage of time chosen : 0.3
+		- On View2 : 
+			- Mean average Accuracy : 0.257142857143
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.267261904762
+			- Percentage of time chosen : 0.0
+	- Fold 2, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.275595238095
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.274404761905
+			- Percentage of time chosen : 0.2
+		- On View2 : 
+			- Mean average Accuracy : 0.264285714286
+			- Percentage of time chosen : 0.1
+		- On View3 : 
+			- Mean average Accuracy : 0.277976190476
+			- Percentage of time chosen : 0.0
+	- Fold 3, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.282142857143
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.266071428571
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.253571428571
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.26369047619
+			- Percentage of time chosen : 0.2
+	- Fold 4, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.272023809524
+			- Percentage of time chosen : 1.0
+		- On View1 : 
+			- Mean average Accuracy : 0.264285714286
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.258928571429
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.265476190476
+			- Percentage of time chosen : 0.0
+
+ For each iteration : 
+	- Iteration 1
+		 Fold 1
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+	- Iteration 2
+		 Fold 1
+			Accuracy on train : 74.4047619048
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 76.1904761905
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 77.380952381
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View1
+		 Fold 4
+			Accuracy on train : 77.9761904762
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 75.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View0
+	- Iteration 3
+		 Fold 1
+			Accuracy on train : 74.4047619048
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 76.1904761905
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 77.380952381
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View1
+		 Fold 4
+			Accuracy on train : 77.9761904762
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 75.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View0
+	- Iteration 4
+		 Fold 1
+			Accuracy on train : 74.4047619048
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 76.1904761905
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 77.380952381
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View2
+		 Fold 4
+			Accuracy on train : 77.9761904762
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 75.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View0
+	- Iteration 5
+		 Fold 1
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+2016-09-06 10:26:02,608 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d211664d715e653f105e4cb7348645cbee180914
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d51b4eaf83f6fcd9f130c294e0468833ed4a5a30
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.890476190476
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.890476190476
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e3ecbf951561881a8374881ac8bc0fe3a642d4ed
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 41
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4b027c7737631df018275a3fa863d4b0c5d4f4a1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a7e7788a97badf1e4004631a77488335330e38ad
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..30353e793e5d8fda38d37a59b6d47062fa33d2bd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 41
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b1fdee3f802ec1e9b294a3063836dcdded981bdc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 18, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d845e8ffe5475d6824fa5fb1eb0715b5419d135c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7979763ec2fe478fd1f9ac624b8b1e7c859e43fe
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.466666666667
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0d73c12c86fbd5a461212442740b218cfa2865cc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e7530b2030eb1b89de274a1e738a79311c21a64b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..81b99c2e1a21bae2f05c93cfaaf197b41ed1f81a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e1cc8a5dea9e439bd0dd2d2126906ab6b3301da2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.971428571429
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.971428571429
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..28d885b8a2594e3239a77e5e2f9c6d69fb6ad797
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 41
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.580952380952
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e4c62d656eaffd32dfaf75c88479c53853bf48ef
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 18, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..524dffb8908cbb276a7bf45ce4e62809630f93c9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.514285714286
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.514285714286
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d606bc6922cebc9421c17329044fdbc329fa896a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.480952380952
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.480952380952
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..453b1235bd05275bd81329f3b067ab1a19fbcbc1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7c633b5cdb2f55d96155f1c9644cf98c72fb197d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..672961f84dae6107ded9cb484468ad5e0b44d685
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0eb148d0f94635a4eb45860c0dc29b277b382a35
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.914285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.914285714286
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bb5e97e21ed3fc4818f8d322ea1ddfc1c8c8fdd9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 41
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c393d7069fb9490036b11dac6e2a9a93c422a45d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 18, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..871e4290219af97a41fc070e7ea4627668709946
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.47619047619
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.47619047619
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a6d7a51981416562a40a5d96442d9dce564cf7c6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.633333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.633333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f7ae9cc62f78613dcc5d334c654ac33efab193e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1a3bc86eb86d31f9a6159e631eae547a64a50248
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e56487859a03db2850790b6dc6fa129cf8c9b808
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 18, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a809878b1228936053994b3d07334593ea238e29
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.566666666667
+accuracy_score on test : 0.644444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.566666666667
+		- Score on test : 0.644444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..805ad6deb8240290a77fc5dbec24f4b5ff19d9d8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.452380952381
+accuracy_score on test : 0.388888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 5608
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.452380952381
+		- Score on test : 0.388888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
new file mode 100644
index 0000000000000000000000000000000000000000..26f1647524b9834de293998d66cd7531623da06f
Binary files /dev/null and b/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0873f3f6aef8fd84300dab66628803e450ab4c47
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,235 @@
+		Result for Multiview classification with Mumbo
+
+Average accuracy :
+	-On Train : 76.1904761905
+	-On Test : 50.9523809524
+	-On Validation : 51.3333333333Dataset info :
+	-Dataset name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : View0 of shape (300, 20), View1 of shape (300, 20), View2 of shape (300, 20), View3 of shape (300, 18)
+	-5 folds
+	- Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration : 
+	-Used 1 core(s)
+	-Iterations : min 1, max 10, threshold 0.01
+	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:01        0:00:00
+	         Fold 2        0:00:02        0:00:00
+	         Fold 3        0:00:03        0:00:00
+	         Fold 4        0:00:05        0:00:00
+	         Fold 5        0:00:06        0:00:00
+	          Total        0:00:19        0:00:02
+	So a total classification time of 0:00:07.
+
+
+
+Mean average accuracies and stats for each fold :
+	- Fold 0, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.264880952381
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.253571428571
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.250595238095
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.273214285714
+			- Percentage of time chosen : 0.2
+	- Fold 1, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.263095238095
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.276785714286
+			- Percentage of time chosen : 0.3
+		- On View2 : 
+			- Mean average Accuracy : 0.257142857143
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.267261904762
+			- Percentage of time chosen : 0.0
+	- Fold 2, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.275595238095
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.274404761905
+			- Percentage of time chosen : 0.2
+		- On View2 : 
+			- Mean average Accuracy : 0.264285714286
+			- Percentage of time chosen : 0.1
+		- On View3 : 
+			- Mean average Accuracy : 0.277976190476
+			- Percentage of time chosen : 0.0
+	- Fold 3, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.282142857143
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.266071428571
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.253571428571
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.26369047619
+			- Percentage of time chosen : 0.2
+	- Fold 4, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.272023809524
+			- Percentage of time chosen : 1.0
+		- On View1 : 
+			- Mean average Accuracy : 0.264285714286
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.258928571429
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.265476190476
+			- Percentage of time chosen : 0.0
+
+ For each iteration : 
+	- Iteration 1
+		 Fold 1
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+	- Iteration 2
+		 Fold 1
+			Accuracy on train : 74.4047619048
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 76.1904761905
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 77.380952381
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View1
+		 Fold 4
+			Accuracy on train : 77.9761904762
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 75.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View0
+	- Iteration 3
+		 Fold 1
+			Accuracy on train : 74.4047619048
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 76.1904761905
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 77.380952381
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View1
+		 Fold 4
+			Accuracy on train : 77.9761904762
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 75.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View0
+	- Iteration 4
+		 Fold 1
+			Accuracy on train : 74.4047619048
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 76.1904761905
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 77.380952381
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.1111111111
+			Selected View : View2
+		 Fold 4
+			Accuracy on train : 77.9761904762
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 75.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.2222222222
+			Selected View : View0
+	- Iteration 5
+		 Fold 1
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 50.0
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.0
+			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..edd1146e89d61f806df070c36677845a7acca855
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1624 @@
+2016-09-06 10:31:15,988 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:31:15,989 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00010290625 Gbytes /!\ 
+2016-09-06 10:31:20,994 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:31:20,996 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:31:21,043 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,043 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,044 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:31:21,044 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:31:21,044 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:21,044 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:21,045 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,045 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,045 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,045 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,045 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:21,045 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:21,045 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,045 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,080 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:21,080 DEBUG: Start:	 Training
+2016-09-06 10:31:21,082 DEBUG: Info:	 Time for Training: 0.0389211177826[s]
+2016-09-06 10:31:21,082 DEBUG: Done:	 Training
+2016-09-06 10:31:21,082 DEBUG: Start:	 Predicting
+2016-09-06 10:31:21,085 DEBUG: Done:	 Predicting
+2016-09-06 10:31:21,085 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:21,086 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:21,086 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.938095238095
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.938095238095
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,087 INFO: Done:	 Result Analysis
+2016-09-06 10:31:21,095 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:21,095 DEBUG: Start:	 Training
+2016-09-06 10:31:21,098 DEBUG: Info:	 Time for Training: 0.0554401874542[s]
+2016-09-06 10:31:21,098 DEBUG: Done:	 Training
+2016-09-06 10:31:21,099 DEBUG: Start:	 Predicting
+2016-09-06 10:31:21,101 DEBUG: Done:	 Predicting
+2016-09-06 10:31:21,101 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:21,103 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:21,103 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,104 INFO: Done:	 Result Analysis
+2016-09-06 10:31:21,196 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,196 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:31:21,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:31:21,197 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:21,197 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:21,197 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,197 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,198 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,198 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,198 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:21,198 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:21,198 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,198 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,230 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:21,230 DEBUG: Start:	 Training
+2016-09-06 10:31:21,231 DEBUG: Info:	 Time for Training: 0.0347671508789[s]
+2016-09-06 10:31:21,231 DEBUG: Done:	 Training
+2016-09-06 10:31:21,231 DEBUG: Start:	 Predicting
+2016-09-06 10:31:21,238 DEBUG: Done:	 Predicting
+2016-09-06 10:31:21,238 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:21,239 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:21,239 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 40
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,240 INFO: Done:	 Result Analysis
+2016-09-06 10:31:21,497 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:21,497 DEBUG: Start:	 Training
+2016-09-06 10:31:21,540 DEBUG: Info:	 Time for Training: 0.343922138214[s]
+2016-09-06 10:31:21,540 DEBUG: Done:	 Training
+2016-09-06 10:31:21,540 DEBUG: Start:	 Predicting
+2016-09-06 10:31:21,546 DEBUG: Done:	 Predicting
+2016-09-06 10:31:21,546 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:21,548 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:21,548 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 17, max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.990476190476
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,548 INFO: Done:	 Result Analysis
+2016-09-06 10:31:21,646 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,646 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,646 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:31:21,646 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:31:21,646 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:21,647 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:21,647 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,647 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,647 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,647 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,647 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:21,647 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:21,647 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,647 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,692 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:21,692 DEBUG: Start:	 Training
+2016-09-06 10:31:21,693 DEBUG: Info:	 Time for Training: 0.0476639270782[s]
+2016-09-06 10:31:21,693 DEBUG: Done:	 Training
+2016-09-06 10:31:21,693 DEBUG: Start:	 Predicting
+2016-09-06 10:31:21,698 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:21,698 DEBUG: Start:	 Training
+2016-09-06 10:31:21,710 DEBUG: Done:	 Predicting
+2016-09-06 10:31:21,710 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:21,713 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:21,713 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.538095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,713 INFO: Done:	 Result Analysis
+2016-09-06 10:31:21,721 DEBUG: Info:	 Time for Training: 0.0758979320526[s]
+2016-09-06 10:31:21,722 DEBUG: Done:	 Training
+2016-09-06 10:31:21,722 DEBUG: Start:	 Predicting
+2016-09-06 10:31:21,725 DEBUG: Done:	 Predicting
+2016-09-06 10:31:21,725 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:21,726 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:21,726 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,727 INFO: Done:	 Result Analysis
+2016-09-06 10:31:21,797 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,797 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:21,798 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:31:21,798 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:31:21,798 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:21,798 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:21,799 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,799 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:31:21,799 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,799 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:31:21,799 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:21,799 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:21,799 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,799 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:21,869 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:21,869 DEBUG: Start:	 Training
+2016-09-06 10:31:21,877 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:21,877 DEBUG: Start:	 Training
+2016-09-06 10:31:21,892 DEBUG: Info:	 Time for Training: 0.0960838794708[s]
+2016-09-06 10:31:21,893 DEBUG: Done:	 Training
+2016-09-06 10:31:21,893 DEBUG: Start:	 Predicting
+2016-09-06 10:31:21,899 DEBUG: Done:	 Predicting
+2016-09-06 10:31:21,900 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:21,900 DEBUG: Info:	 Time for Training: 0.103469848633[s]
+2016-09-06 10:31:21,900 DEBUG: Done:	 Training
+2016-09-06 10:31:21,900 DEBUG: Start:	 Predicting
+2016-09-06 10:31:21,901 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:21,901 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,901 INFO: Done:	 Result Analysis
+2016-09-06 10:31:21,903 DEBUG: Done:	 Predicting
+2016-09-06 10:31:21,903 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:21,905 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:21,905 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:21,905 INFO: Done:	 Result Analysis
+2016-09-06 10:31:22,048 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,048 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,049 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:31:22,049 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:31:22,049 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:22,049 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:22,051 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,051 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,051 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,051 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,051 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:22,051 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:22,051 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,051 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,102 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:22,102 DEBUG: Start:	 Training
+2016-09-06 10:31:22,104 DEBUG: Info:	 Time for Training: 0.0569620132446[s]
+2016-09-06 10:31:22,105 DEBUG: Done:	 Training
+2016-09-06 10:31:22,105 DEBUG: Start:	 Predicting
+2016-09-06 10:31:22,108 DEBUG: Done:	 Predicting
+2016-09-06 10:31:22,109 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:22,111 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:22,111 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.838095238095
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.838095238095
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,111 INFO: Done:	 Result Analysis
+2016-09-06 10:31:22,121 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:22,121 DEBUG: Start:	 Training
+2016-09-06 10:31:22,125 DEBUG: Info:	 Time for Training: 0.0774049758911[s]
+2016-09-06 10:31:22,125 DEBUG: Done:	 Training
+2016-09-06 10:31:22,125 DEBUG: Start:	 Predicting
+2016-09-06 10:31:22,128 DEBUG: Done:	 Predicting
+2016-09-06 10:31:22,128 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:22,130 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:22,130 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,130 INFO: Done:	 Result Analysis
+2016-09-06 10:31:22,194 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,194 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,194 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:31:22,194 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:31:22,194 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:22,194 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:22,195 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,195 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,195 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,195 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,196 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:22,196 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:22,196 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,196 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,248 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:22,248 DEBUG: Start:	 Training
+2016-09-06 10:31:22,248 DEBUG: Info:	 Time for Training: 0.0551888942719[s]
+2016-09-06 10:31:22,248 DEBUG: Done:	 Training
+2016-09-06 10:31:22,249 DEBUG: Start:	 Predicting
+2016-09-06 10:31:22,255 DEBUG: Done:	 Predicting
+2016-09-06 10:31:22,256 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:22,257 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:22,257 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 40
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.490476190476
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,257 INFO: Done:	 Result Analysis
+2016-09-06 10:31:22,555 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:22,555 DEBUG: Start:	 Training
+2016-09-06 10:31:22,602 DEBUG: Info:	 Time for Training: 0.409327983856[s]
+2016-09-06 10:31:22,603 DEBUG: Done:	 Training
+2016-09-06 10:31:22,603 DEBUG: Start:	 Predicting
+2016-09-06 10:31:22,609 DEBUG: Done:	 Predicting
+2016-09-06 10:31:22,609 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:22,610 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:22,610 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 17, max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,611 INFO: Done:	 Result Analysis
+2016-09-06 10:31:22,751 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,751 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,752 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:31:22,752 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:31:22,752 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:22,752 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:22,753 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,753 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,753 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,753 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,753 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:22,753 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:22,753 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,753 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,823 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:22,823 DEBUG: Start:	 Training
+2016-09-06 10:31:22,824 DEBUG: Info:	 Time for Training: 0.0737769603729[s]
+2016-09-06 10:31:22,824 DEBUG: Done:	 Training
+2016-09-06 10:31:22,825 DEBUG: Start:	 Predicting
+2016-09-06 10:31:22,828 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:22,828 DEBUG: Start:	 Training
+2016-09-06 10:31:22,836 DEBUG: Done:	 Predicting
+2016-09-06 10:31:22,837 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:22,839 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:22,839 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.538095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,839 INFO: Done:	 Result Analysis
+2016-09-06 10:31:22,852 DEBUG: Info:	 Time for Training: 0.10190820694[s]
+2016-09-06 10:31:22,853 DEBUG: Done:	 Training
+2016-09-06 10:31:22,853 DEBUG: Start:	 Predicting
+2016-09-06 10:31:22,856 DEBUG: Done:	 Predicting
+2016-09-06 10:31:22,856 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:22,857 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:22,857 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:22,858 INFO: Done:	 Result Analysis
+2016-09-06 10:31:22,994 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,994 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:22,994 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:31:22,994 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:31:22,994 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:22,994 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:22,996 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,996 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:31:22,996 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,996 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:31:22,996 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:22,996 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:22,996 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:22,996 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,070 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:23,070 DEBUG: Start:	 Training
+2016-09-06 10:31:23,077 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:23,077 DEBUG: Start:	 Training
+2016-09-06 10:31:23,093 DEBUG: Info:	 Time for Training: 0.10053896904[s]
+2016-09-06 10:31:23,094 DEBUG: Done:	 Training
+2016-09-06 10:31:23,094 DEBUG: Start:	 Predicting
+2016-09-06 10:31:23,101 DEBUG: Done:	 Predicting
+2016-09-06 10:31:23,101 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:23,102 DEBUG: Info:	 Time for Training: 0.108783006668[s]
+2016-09-06 10:31:23,102 DEBUG: Done:	 Training
+2016-09-06 10:31:23,102 DEBUG: Start:	 Predicting
+2016-09-06 10:31:23,103 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:23,104 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,104 INFO: Done:	 Result Analysis
+2016-09-06 10:31:23,107 DEBUG: Done:	 Predicting
+2016-09-06 10:31:23,107 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:23,108 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:23,108 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,108 INFO: Done:	 Result Analysis
+2016-09-06 10:31:23,242 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,243 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:31:23,243 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:23,243 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,243 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:31:23,244 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:23,244 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,244 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,244 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:23,244 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,245 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,245 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,245 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:23,245 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,282 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:23,282 DEBUG: Start:	 Training
+2016-09-06 10:31:23,283 DEBUG: Info:	 Time for Training: 0.0416178703308[s]
+2016-09-06 10:31:23,283 DEBUG: Done:	 Training
+2016-09-06 10:31:23,284 DEBUG: Start:	 Predicting
+2016-09-06 10:31:23,286 DEBUG: Done:	 Predicting
+2016-09-06 10:31:23,286 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:23,288 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:23,288 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.785714285714
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.785714285714
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,288 INFO: Done:	 Result Analysis
+2016-09-06 10:31:23,292 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:23,292 DEBUG: Start:	 Training
+2016-09-06 10:31:23,296 DEBUG: Info:	 Time for Training: 0.0540850162506[s]
+2016-09-06 10:31:23,296 DEBUG: Done:	 Training
+2016-09-06 10:31:23,296 DEBUG: Start:	 Predicting
+2016-09-06 10:31:23,299 DEBUG: Done:	 Predicting
+2016-09-06 10:31:23,299 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:23,301 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:23,301 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,301 INFO: Done:	 Result Analysis
+2016-09-06 10:31:23,391 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,391 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,391 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:31:23,391 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:31:23,391 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:23,391 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:23,392 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,392 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,392 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,392 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,393 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:23,393 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:23,393 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,393 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,430 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:23,430 DEBUG: Start:	 Training
+2016-09-06 10:31:23,431 DEBUG: Info:	 Time for Training: 0.0408799648285[s]
+2016-09-06 10:31:23,431 DEBUG: Done:	 Training
+2016-09-06 10:31:23,431 DEBUG: Start:	 Predicting
+2016-09-06 10:31:23,439 DEBUG: Done:	 Predicting
+2016-09-06 10:31:23,439 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:23,440 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:23,441 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 40
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.614285714286
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,441 INFO: Done:	 Result Analysis
+2016-09-06 10:31:23,693 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:23,693 DEBUG: Start:	 Training
+2016-09-06 10:31:23,736 DEBUG: Info:	 Time for Training: 0.345639944077[s]
+2016-09-06 10:31:23,736 DEBUG: Done:	 Training
+2016-09-06 10:31:23,736 DEBUG: Start:	 Predicting
+2016-09-06 10:31:23,742 DEBUG: Done:	 Predicting
+2016-09-06 10:31:23,742 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:23,743 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:23,743 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.961904761905
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 17, max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.961904761905
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,743 INFO: Done:	 Result Analysis
+2016-09-06 10:31:23,843 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,843 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:23,844 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:31:23,844 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:31:23,844 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:23,844 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:23,845 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,845 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:23,845 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,845 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:23,845 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:23,845 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:23,845 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,845 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:23,914 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:23,914 DEBUG: Start:	 Training
+2016-09-06 10:31:23,915 DEBUG: Info:	 Time for Training: 0.0723230838776[s]
+2016-09-06 10:31:23,915 DEBUG: Done:	 Training
+2016-09-06 10:31:23,915 DEBUG: Start:	 Predicting
+2016-09-06 10:31:23,917 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:23,917 DEBUG: Start:	 Training
+2016-09-06 10:31:23,927 DEBUG: Done:	 Predicting
+2016-09-06 10:31:23,927 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:23,929 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:23,929 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.538095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,930 INFO: Done:	 Result Analysis
+2016-09-06 10:31:23,939 DEBUG: Info:	 Time for Training: 0.0961401462555[s]
+2016-09-06 10:31:23,939 DEBUG: Done:	 Training
+2016-09-06 10:31:23,939 DEBUG: Start:	 Predicting
+2016-09-06 10:31:23,942 DEBUG: Done:	 Predicting
+2016-09-06 10:31:23,942 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:23,943 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:23,943 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.490476190476
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:23,943 INFO: Done:	 Result Analysis
+2016-09-06 10:31:24,085 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,085 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,085 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:31:24,085 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:31:24,085 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:24,085 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:24,086 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:24,086 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:31:24,086 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:24,086 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:31:24,086 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:24,086 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:24,086 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,086 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,144 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:24,144 DEBUG: Start:	 Training
+2016-09-06 10:31:24,149 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:24,149 DEBUG: Start:	 Training
+2016-09-06 10:31:24,161 DEBUG: Info:	 Time for Training: 0.0771129131317[s]
+2016-09-06 10:31:24,161 DEBUG: Done:	 Training
+2016-09-06 10:31:24,161 DEBUG: Start:	 Predicting
+2016-09-06 10:31:24,164 DEBUG: Done:	 Predicting
+2016-09-06 10:31:24,165 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:24,166 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:24,166 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.6
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:24,166 INFO: Done:	 Result Analysis
+2016-09-06 10:31:24,172 DEBUG: Info:	 Time for Training: 0.0878200531006[s]
+2016-09-06 10:31:24,172 DEBUG: Done:	 Training
+2016-09-06 10:31:24,172 DEBUG: Start:	 Predicting
+2016-09-06 10:31:24,178 DEBUG: Done:	 Predicting
+2016-09-06 10:31:24,178 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:24,179 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:24,179 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:24,180 INFO: Done:	 Result Analysis
+2016-09-06 10:31:24,332 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,332 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:31:24,332 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:24,333 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,333 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:24,333 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:31:24,333 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:24,333 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:24,333 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:24,333 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,334 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:24,334 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:24,334 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:24,334 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,384 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:24,384 DEBUG: Start:	 Training
+2016-09-06 10:31:24,386 DEBUG: Info:	 Time for Training: 0.0551080703735[s]
+2016-09-06 10:31:24,386 DEBUG: Done:	 Training
+2016-09-06 10:31:24,387 DEBUG: Start:	 Predicting
+2016-09-06 10:31:24,389 DEBUG: Done:	 Predicting
+2016-09-06 10:31:24,389 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:24,391 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:24,391 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.919047619048
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.919047619048
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:24,392 INFO: Done:	 Result Analysis
+2016-09-06 10:31:24,418 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:24,418 DEBUG: Start:	 Training
+2016-09-06 10:31:24,423 DEBUG: Info:	 Time for Training: 0.0905458927155[s]
+2016-09-06 10:31:24,423 DEBUG: Done:	 Training
+2016-09-06 10:31:24,423 DEBUG: Start:	 Predicting
+2016-09-06 10:31:24,427 DEBUG: Done:	 Predicting
+2016-09-06 10:31:24,428 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:24,430 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:24,430 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:24,431 INFO: Done:	 Result Analysis
+2016-09-06 10:31:24,595 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,595 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:31:24,596 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:24,596 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:24,597 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:24,597 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:24,597 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,600 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:24,601 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:31:24,601 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:24,602 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:24,602 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:24,602 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:24,602 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:24,652 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:24,652 DEBUG: Start:	 Training
+2016-09-06 10:31:24,653 DEBUG: Info:	 Time for Training: 0.0590059757233[s]
+2016-09-06 10:31:24,653 DEBUG: Done:	 Training
+2016-09-06 10:31:24,653 DEBUG: Start:	 Predicting
+2016-09-06 10:31:24,664 DEBUG: Done:	 Predicting
+2016-09-06 10:31:24,665 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:24,667 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:24,667 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 40
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.614285714286
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:24,667 INFO: Done:	 Result Analysis
+2016-09-06 10:31:24,960 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:24,960 DEBUG: Start:	 Training
+2016-09-06 10:31:25,007 DEBUG: Info:	 Time for Training: 0.407965898514[s]
+2016-09-06 10:31:25,008 DEBUG: Done:	 Training
+2016-09-06 10:31:25,008 DEBUG: Start:	 Predicting
+2016-09-06 10:31:25,014 DEBUG: Done:	 Predicting
+2016-09-06 10:31:25,014 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:25,015 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:25,016 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 17, max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.990476190476
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:25,016 INFO: Done:	 Result Analysis
+2016-09-06 10:31:25,149 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:25,149 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:31:25,149 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:31:25,149 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:31:25,149 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:25,149 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:31:25,150 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:25,150 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:31:25,150 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:25,150 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:31:25,151 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:25,151 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:31:25,151 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:25,151 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:31:25,220 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:25,220 DEBUG: Start:	 Training
+2016-09-06 10:31:25,221 DEBUG: Info:	 Time for Training: 0.0730149745941[s]
+2016-09-06 10:31:25,222 DEBUG: Done:	 Training
+2016-09-06 10:31:25,222 DEBUG: Start:	 Predicting
+2016-09-06 10:31:25,226 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:31:25,226 DEBUG: Start:	 Training
+2016-09-06 10:31:25,234 DEBUG: Done:	 Predicting
+2016-09-06 10:31:25,235 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:25,236 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:25,236 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.538095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:25,237 INFO: Done:	 Result Analysis
+2016-09-06 10:31:25,251 DEBUG: Info:	 Time for Training: 0.103055000305[s]
+2016-09-06 10:31:25,252 DEBUG: Done:	 Training
+2016-09-06 10:31:25,253 DEBUG: Start:	 Predicting
+2016-09-06 10:31:25,259 DEBUG: Done:	 Predicting
+2016-09-06 10:31:25,259 DEBUG: Start:	 Getting Results
+2016-09-06 10:31:25,261 DEBUG: Done:	 Getting Results
+2016-09-06 10:31:25,261 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.622222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.528571428571
+		- Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:31:25,262 INFO: Done:	 Result Analysis
+2016-09-06 10:31:25,546 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:31:25,546 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:31:25,547 INFO: Info:	 Shape of View0 :(300, 8)
+2016-09-06 10:31:25,547 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:31:25,547 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:31:25,547 INFO: Info:	 Shape of View1 :(300, 10)
+2016-09-06 10:31:25,548 INFO: Info:	 Shape of View0 :(300, 8)
+2016-09-06 10:31:25,548 INFO: Info:	 Shape of View2 :(300, 5)
+2016-09-06 10:31:25,548 INFO: Info:	 Shape of View1 :(300, 10)
+2016-09-06 10:31:25,548 INFO: Info:	 Shape of View3 :(300, 13)
+2016-09-06 10:31:25,548 INFO: Done:	 Read Database Files
+2016-09-06 10:31:25,549 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:31:25,549 INFO: Info:	 Shape of View2 :(300, 5)
+2016-09-06 10:31:25,549 INFO: Info:	 Shape of View3 :(300, 13)
+2016-09-06 10:31:25,549 INFO: Done:	 Read Database Files
+2016-09-06 10:31:25,550 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:31:25,553 INFO: Done:	 Determine validation split
+2016-09-06 10:31:25,553 INFO: Start:	 Determine 5 folds
+2016-09-06 10:31:25,554 INFO: Done:	 Determine validation split
+2016-09-06 10:31:25,554 INFO: Start:	 Determine 5 folds
+2016-09-06 10:31:25,561 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:31:25,561 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:31:25,561 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:31:25,561 INFO: Done:	 Determine folds
+2016-09-06 10:31:25,561 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:31:25,561 INFO: Start:	 Classification
+2016-09-06 10:31:25,561 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:31:25,561 INFO: 	Start:	 Fold number 1
+2016-09-06 10:31:25,561 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:31:25,561 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:31:25,562 INFO: Done:	 Determine folds
+2016-09-06 10:31:25,562 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:31:25,562 INFO: Start:	 Classification
+2016-09-06 10:31:25,562 INFO: 	Start:	 Fold number 1
+2016-09-06 10:31:25,604 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:31:25,618 DEBUG: 			View 0 : 0.538461538462
+2016-09-06 10:31:25,626 DEBUG: 			View 1 : 0.550295857988
+2016-09-06 10:31:25,634 DEBUG: 			View 2 : 0.520710059172
+2016-09-06 10:31:25,642 DEBUG: 			View 3 : 0.508875739645
+2016-09-06 10:31:25,674 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:25,752 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:31:25,760 DEBUG: 			View 0 : 0.715976331361
+2016-09-06 10:31:25,767 DEBUG: 			View 1 : 0.715976331361
+2016-09-06 10:31:25,774 DEBUG: 			View 2 : 0.704142011834
+2016-09-06 10:31:25,782 DEBUG: 			View 3 : 0.751479289941
+2016-09-06 10:31:25,819 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:25,964 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:31:25,980 DEBUG: 			View 0 : 0.715976331361
+2016-09-06 10:31:25,987 DEBUG: 			View 1 : 0.715976331361
+2016-09-06 10:31:25,994 DEBUG: 			View 2 : 0.704142011834
+2016-09-06 10:31:26,001 DEBUG: 			View 3 : 0.751479289941
+2016-09-06 10:31:26,040 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:26,244 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:31:26,251 DEBUG: 			View 0 : 0.692307692308
+2016-09-06 10:31:26,258 DEBUG: 			View 1 : 0.573964497041
+2016-09-06 10:31:26,265 DEBUG: 			View 2 : 0.686390532544
+2016-09-06 10:31:26,272 DEBUG: 			View 3 : 0.633136094675
+2016-09-06 10:31:26,313 DEBUG: 			 Best view : 		View2
+2016-09-06 10:31:26,583 INFO: 	Start: 	 Classification
+2016-09-06 10:31:27,038 INFO: 	Done: 	 Fold number 1
+2016-09-06 10:31:27,038 INFO: 	Start:	 Fold number 2
+2016-09-06 10:31:27,068 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:31:27,075 DEBUG: 			View 0 : 0.514792899408
+2016-09-06 10:31:27,082 DEBUG: 			View 1 : 0.538461538462
+2016-09-06 10:31:27,089 DEBUG: 			View 2 : 0.473372781065
+2016-09-06 10:31:27,096 DEBUG: 			View 3 : 0.497041420118
+2016-09-06 10:31:27,127 DEBUG: 			 Best view : 		View1
+2016-09-06 10:31:27,203 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:31:27,210 DEBUG: 			View 0 : 0.674556213018
+2016-09-06 10:31:27,217 DEBUG: 			View 1 : 0.727810650888
+2016-09-06 10:31:27,224 DEBUG: 			View 2 : 0.644970414201
+2016-09-06 10:31:27,232 DEBUG: 			View 3 : 0.686390532544
+2016-09-06 10:31:27,268 DEBUG: 			 Best view : 		View1
+2016-09-06 10:31:27,409 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:31:27,416 DEBUG: 			View 0 : 0.674556213018
+2016-09-06 10:31:27,423 DEBUG: 			View 1 : 0.727810650888
+2016-09-06 10:31:27,430 DEBUG: 			View 2 : 0.644970414201
+2016-09-06 10:31:27,437 DEBUG: 			View 3 : 0.686390532544
+2016-09-06 10:31:27,478 DEBUG: 			 Best view : 		View1
+2016-09-06 10:31:27,682 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:31:27,689 DEBUG: 			View 0 : 0.644970414201
+2016-09-06 10:31:27,696 DEBUG: 			View 1 : 0.692307692308
+2016-09-06 10:31:27,703 DEBUG: 			View 2 : 0.650887573964
+2016-09-06 10:31:27,711 DEBUG: 			View 3 : 0.698224852071
+2016-09-06 10:31:27,752 DEBUG: 			 Best view : 		View1
+2016-09-06 10:31:28,021 INFO: 	Start: 	 Classification
+2016-09-06 10:31:28,472 INFO: 	Done: 	 Fold number 2
+2016-09-06 10:31:28,472 INFO: 	Start:	 Fold number 3
+2016-09-06 10:31:28,501 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:31:28,508 DEBUG: 			View 0 : 0.497041420118
+2016-09-06 10:31:28,514 DEBUG: 			View 1 : 0.485207100592
+2016-09-06 10:31:28,521 DEBUG: 			View 2 : 0.491124260355
+2016-09-06 10:31:28,528 DEBUG: 			View 3 : 0.568047337278
+2016-09-06 10:31:28,558 DEBUG: 			 Best view : 		View0
+2016-09-06 10:31:28,633 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:31:28,640 DEBUG: 			View 0 : 0.674556213018
+2016-09-06 10:31:28,647 DEBUG: 			View 1 : 0.597633136095
+2016-09-06 10:31:28,654 DEBUG: 			View 2 : 0.692307692308
+2016-09-06 10:31:28,661 DEBUG: 			View 3 : 0.751479289941
+2016-09-06 10:31:28,697 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:28,836 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:31:28,844 DEBUG: 			View 0 : 0.674556213018
+2016-09-06 10:31:28,851 DEBUG: 			View 1 : 0.597633136095
+2016-09-06 10:31:28,857 DEBUG: 			View 2 : 0.692307692308
+2016-09-06 10:31:28,865 DEBUG: 			View 3 : 0.751479289941
+2016-09-06 10:31:28,903 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:29,107 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:31:29,114 DEBUG: 			View 0 : 0.656804733728
+2016-09-06 10:31:29,121 DEBUG: 			View 1 : 0.639053254438
+2016-09-06 10:31:29,128 DEBUG: 			View 2 : 0.674556213018
+2016-09-06 10:31:29,136 DEBUG: 			View 3 : 0.792899408284
+2016-09-06 10:31:29,177 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:29,444 INFO: 	Start: 	 Classification
+2016-09-06 10:31:29,896 INFO: 	Done: 	 Fold number 3
+2016-09-06 10:31:29,896 INFO: 	Start:	 Fold number 4
+2016-09-06 10:31:29,924 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:31:29,931 DEBUG: 			View 0 : 0.479289940828
+2016-09-06 10:31:29,938 DEBUG: 			View 1 : 0.479289940828
+2016-09-06 10:31:29,945 DEBUG: 			View 2 : 0.479289940828
+2016-09-06 10:31:29,951 DEBUG: 			View 3 : 0.479289940828
+2016-09-06 10:31:29,951 WARNING: 		WARNING:	All bad for iteration 0
+2016-09-06 10:31:29,982 DEBUG: 			 Best view : 		View0
+2016-09-06 10:31:30,058 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:31:30,065 DEBUG: 			View 0 : 0.627218934911
+2016-09-06 10:31:30,072 DEBUG: 			View 1 : 0.686390532544
+2016-09-06 10:31:30,080 DEBUG: 			View 2 : 0.686390532544
+2016-09-06 10:31:30,087 DEBUG: 			View 3 : 0.751479289941
+2016-09-06 10:31:30,123 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:30,265 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:31:30,272 DEBUG: 			View 0 : 0.627218934911
+2016-09-06 10:31:30,280 DEBUG: 			View 1 : 0.686390532544
+2016-09-06 10:31:30,287 DEBUG: 			View 2 : 0.686390532544
+2016-09-06 10:31:30,294 DEBUG: 			View 3 : 0.751479289941
+2016-09-06 10:31:30,334 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:30,537 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:31:30,544 DEBUG: 			View 0 : 0.615384615385
+2016-09-06 10:31:30,551 DEBUG: 			View 1 : 0.603550295858
+2016-09-06 10:31:30,559 DEBUG: 			View 2 : 0.644970414201
+2016-09-06 10:31:30,566 DEBUG: 			View 3 : 0.662721893491
+2016-09-06 10:31:30,607 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:30,876 INFO: 	Start: 	 Classification
+2016-09-06 10:31:31,332 INFO: 	Done: 	 Fold number 4
+2016-09-06 10:31:31,332 INFO: 	Start:	 Fold number 5
+2016-09-06 10:31:31,361 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:31:31,368 DEBUG: 			View 0 : 0.491124260355
+2016-09-06 10:31:31,375 DEBUG: 			View 1 : 0.479289940828
+2016-09-06 10:31:31,382 DEBUG: 			View 2 : 0.479289940828
+2016-09-06 10:31:31,388 DEBUG: 			View 3 : 0.526627218935
+2016-09-06 10:31:31,418 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:31,493 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:31:31,501 DEBUG: 			View 0 : 0.668639053254
+2016-09-06 10:31:31,508 DEBUG: 			View 1 : 0.656804733728
+2016-09-06 10:31:31,515 DEBUG: 			View 2 : 0.656804733728
+2016-09-06 10:31:31,523 DEBUG: 			View 3 : 0.715976331361
+2016-09-06 10:31:31,559 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:31,698 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:31:31,705 DEBUG: 			View 0 : 0.668639053254
+2016-09-06 10:31:31,712 DEBUG: 			View 1 : 0.656804733728
+2016-09-06 10:31:31,719 DEBUG: 			View 2 : 0.656804733728
+2016-09-06 10:31:31,727 DEBUG: 			View 3 : 0.715976331361
+2016-09-06 10:31:31,765 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:31,969 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:31:31,977 DEBUG: 			View 0 : 0.633136094675
+2016-09-06 10:31:31,984 DEBUG: 			View 1 : 0.556213017751
+2016-09-06 10:31:31,991 DEBUG: 			View 2 : 0.568047337278
+2016-09-06 10:31:31,998 DEBUG: 			View 3 : 0.639053254438
+2016-09-06 10:31:32,039 DEBUG: 			 Best view : 		View3
+2016-09-06 10:31:32,311 INFO: 	Start: 	 Classification
+2016-09-06 10:31:32,766 INFO: 	Done: 	 Fold number 5
+2016-09-06 10:31:32,767 INFO: Done:	 Classification
+2016-09-06 10:31:32,767 INFO: Info:	 Time for Classification: 7[s]
+2016-09-06 10:31:32,767 INFO: Start:	 Result Analysis for Mumbo
+2016-09-06 10:31:35,218 INFO: 		Result for Multiview classification with Mumbo
+
+Average accuracy :
+	-On Train : 73.9644970414
+	-On Test : 48.5714285714
+	-On Validation : 51.6853932584Dataset info :
+	-Dataset name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : View0 of shape (300, 8), View1 of shape (300, 10), View2 of shape (300, 5), View3 of shape (300, 13)
+	-5 folds
+	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration : 
+	-Used 1 core(s)
+	-Iterations : min 1, max 10, threshold 0.01
+	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:01        0:00:00
+	         Fold 2        0:00:02        0:00:00
+	         Fold 3        0:00:03        0:00:00
+	         Fold 4        0:00:05        0:00:00
+	         Fold 5        0:00:06        0:00:00
+	          Total        0:00:19        0:00:02
+	So a total classification time of 0:00:07.
+
+
+
+Mean average accuracies and stats for each fold :
+	- Fold 0, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.266272189349
+			- Percentage of time chosen : 0.6
+		- On View1 : 
+			- Mean average Accuracy : 0.255621301775
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.261538461538
+			- Percentage of time chosen : 0.1
+		- On View3 : 
+			- Mean average Accuracy : 0.26449704142
+			- Percentage of time chosen : 0.3
+	- Fold 1, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.250887573964
+			- Percentage of time chosen : 0.6
+		- On View1 : 
+			- Mean average Accuracy : 0.268639053254
+			- Percentage of time chosen : 0.4
+		- On View2 : 
+			- Mean average Accuracy : 0.241420118343
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.256804733728
+			- Percentage of time chosen : 0.0
+	- Fold 2, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.250295857988
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.231952662722
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.255029585799
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.286390532544
+			- Percentage of time chosen : 0.3
+	- Fold 3, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.234911242604
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.245562130178
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.249704142012
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.26449704142
+			- Percentage of time chosen : 0.3
+	- Fold 4, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.246153846154
+			- Percentage of time chosen : 0.6
+		- On View1 : 
+			- Mean average Accuracy : 0.234911242604
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.236094674556
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.259763313609
+			- Percentage of time chosen : 0.4
+
+ For each iteration : 
+	- Iteration 1
+		 Fold 1
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View3
+	- Iteration 2
+		 Fold 1
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 72.7810650888
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 62.9213483146
+			Selected View : View3
+		 Fold 4
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 71.5976331361
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+	- Iteration 3
+		 Fold 1
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 72.7810650888
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 62.9213483146
+			Selected View : View3
+		 Fold 4
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 71.5976331361
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+	- Iteration 4
+		 Fold 1
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View2
+		 Fold 2
+			Accuracy on train : 72.7810650888
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 62.9213483146
+			Selected View : View3
+		 Fold 4
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 71.5976331361
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+	- Iteration 5
+		 Fold 1
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+2016-09-06 10:31:35,405 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..47001d62273a4a5ea015e3796b2c3ec2e25a4196
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..03adc80f728caac1b9b830f0b577977feba3c44a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.938095238095
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.938095238095
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c7e9d43274727a3c4aec18d8b743fc62f0a91b01
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 40
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..461f44c9e4e58ede514323459fe9974d3f848019
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 17, max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.990476190476
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..207b9a7e43cdd6abc7fd3387bf0a390ef0bc7ef5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.538095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ba87e538664d0cfbffd36505ed1c464111c02f6a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3c15e79999a413450bdc3caab769a6f33b2bb73a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8f4eb2ffd27c3c75a6e3266a9d377a79093e1a4a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a51fc411d8e5e72d67393891f9a87fb2e742de3f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a89da4e85160e762e0120082c1416988040498a5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.838095238095
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.838095238095
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f62702a63465eba6fa423787af629e2e0346e47b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 40
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.490476190476
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2ca481244c162bee62e23f73e2def3b3ad8e18cb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 17, max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5dfa38e3609dec5f1f4936c3ebaef76e51a2d938
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.538095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7a807ce4fcf2c721abea540f95dca1d1385ff64e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c696ff7a1b0aadcedba2b7ef82d442e9441b88e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..12a61d1ed8e3c7b34937737b04ed946a4741e12e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.785714285714
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.785714285714
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7afc9baf908d951b570ebd741436d025caacda42
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 40
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.614285714286
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b74e15997b5669225e79e81368184888dd75321
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.961904761905
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 17, max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.961904761905
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5368fdbf81e67273da1a517f7f7cec0b61c464ae
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.538095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..66e58177f2e55924821ce8a85708d80d55c123d4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.490476190476
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4959e4f4d4bdb768828241fc18a03bb20bc721a9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e1f9847a2a81f2ff1d479e62dddbe37a473d987a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..474fe79a70c8360c5d1a26e84e43be1b5027fe37
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e82c8d88143a257c1a0520149eaaf739e0190808
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.919047619048
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.919047619048
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bb1b14bb1cbdf6dfbd7d9205a1f4990b63a998b9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 40
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.614285714286
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b9e72ceaf74f5230c64760fa06d8eb9b4836e9c9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.6
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c25b7951cdc7de7d0e1ac57b7707f5147f5078d5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..06e42109d453523c1aaf677df928e42e4e5078cf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.4
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 17, max_depth : 8
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.990476190476
+		- Score on test : 0.4
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ec5c49784c9deeab11d9cb6b398de439e22b6e81
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.538095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..39374ee4729da9905a32b9f5346d79ebe57027f3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.622222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2481
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.528571428571
+		- Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
new file mode 100644
index 0000000000000000000000000000000000000000..129780daaf421ae71fa74885fd4baaf13677ad38
Binary files /dev/null and b/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d66943e2c8b83707705f0a7c047f3475dec17d1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,235 @@
+		Result for Multiview classification with Mumbo
+
+Average accuracy :
+	-On Train : 73.9644970414
+	-On Test : 48.5714285714
+	-On Validation : 51.6853932584Dataset info :
+	-Dataset name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : View0 of shape (300, 8), View1 of shape (300, 10), View2 of shape (300, 5), View3 of shape (300, 13)
+	-5 folds
+	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration : 
+	-Used 1 core(s)
+	-Iterations : min 1, max 10, threshold 0.01
+	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:01        0:00:00
+	         Fold 2        0:00:02        0:00:00
+	         Fold 3        0:00:03        0:00:00
+	         Fold 4        0:00:05        0:00:00
+	         Fold 5        0:00:06        0:00:00
+	          Total        0:00:19        0:00:02
+	So a total classification time of 0:00:07.
+
+
+
+Mean average accuracies and stats for each fold :
+	- Fold 0, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.266272189349
+			- Percentage of time chosen : 0.6
+		- On View1 : 
+			- Mean average Accuracy : 0.255621301775
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.261538461538
+			- Percentage of time chosen : 0.1
+		- On View3 : 
+			- Mean average Accuracy : 0.26449704142
+			- Percentage of time chosen : 0.3
+	- Fold 1, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.250887573964
+			- Percentage of time chosen : 0.6
+		- On View1 : 
+			- Mean average Accuracy : 0.268639053254
+			- Percentage of time chosen : 0.4
+		- On View2 : 
+			- Mean average Accuracy : 0.241420118343
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.256804733728
+			- Percentage of time chosen : 0.0
+	- Fold 2, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.250295857988
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.231952662722
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.255029585799
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.286390532544
+			- Percentage of time chosen : 0.3
+	- Fold 3, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.234911242604
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.245562130178
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.249704142012
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.26449704142
+			- Percentage of time chosen : 0.3
+	- Fold 4, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.246153846154
+			- Percentage of time chosen : 0.6
+		- On View1 : 
+			- Mean average Accuracy : 0.234911242604
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.236094674556
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.259763313609
+			- Percentage of time chosen : 0.4
+
+ For each iteration : 
+	- Iteration 1
+		 Fold 1
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View3
+	- Iteration 2
+		 Fold 1
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 72.7810650888
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 62.9213483146
+			Selected View : View3
+		 Fold 4
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 71.5976331361
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+	- Iteration 3
+		 Fold 1
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 72.7810650888
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 62.9213483146
+			Selected View : View3
+		 Fold 4
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 71.5976331361
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+	- Iteration 4
+		 Fold 1
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View2
+		 Fold 2
+			Accuracy on train : 72.7810650888
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 62.9213483146
+			Selected View : View3
+		 Fold 4
+			Accuracy on train : 75.1479289941
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 71.5976331361
+			Accuracy on test : 0.0
+			Accuracy on validation : 49.4382022472
+			Selected View : View3
+	- Iteration 5
+		 Fold 1
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 47.9289940828
+			Accuracy on test : 0.0
+			Accuracy on validation : 47.191011236
+			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..df4e947675f7e8d232b7dd710800e7689ec2c36d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1580 @@
+2016-09-06 10:34:09,127 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:34:09,127 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.0001380625 Gbytes /!\ 
+2016-09-06 10:34:14,141 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:34:14,144 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:34:14,196 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,196 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:34:14,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:34:14,197 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:14,197 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:14,197 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,197 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,197 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,197 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,198 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:14,198 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:14,198 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,198 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,227 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:14,227 DEBUG: Start:	 Training
+2016-09-06 10:34:14,228 DEBUG: Info:	 Time for Training: 0.0323169231415[s]
+2016-09-06 10:34:14,228 DEBUG: Done:	 Training
+2016-09-06 10:34:14,228 DEBUG: Start:	 Predicting
+2016-09-06 10:34:14,231 DEBUG: Done:	 Predicting
+2016-09-06 10:34:14,231 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:14,232 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:14,233 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.652380952381
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.652380952381
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,233 INFO: Done:	 Result Analysis
+2016-09-06 10:34:14,245 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:14,245 DEBUG: Start:	 Training
+2016-09-06 10:34:14,249 DEBUG: Info:	 Time for Training: 0.0530989170074[s]
+2016-09-06 10:34:14,249 DEBUG: Done:	 Training
+2016-09-06 10:34:14,249 DEBUG: Start:	 Predicting
+2016-09-06 10:34:14,252 DEBUG: Done:	 Predicting
+2016-09-06 10:34:14,252 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:14,254 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:14,254 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,254 INFO: Done:	 Result Analysis
+2016-09-06 10:34:14,344 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,344 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,344 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:34:14,344 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:34:14,345 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:14,345 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:14,345 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,345 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,345 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,345 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,345 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:14,345 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:14,345 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,345 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,378 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:14,379 DEBUG: Start:	 Training
+2016-09-06 10:34:14,379 DEBUG: Info:	 Time for Training: 0.0353238582611[s]
+2016-09-06 10:34:14,379 DEBUG: Done:	 Training
+2016-09-06 10:34:14,379 DEBUG: Start:	 Predicting
+2016-09-06 10:34:14,386 DEBUG: Done:	 Predicting
+2016-09-06 10:34:14,386 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:14,387 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:14,387 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.57619047619
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 34
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.57619047619
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,388 INFO: Done:	 Result Analysis
+2016-09-06 10:34:14,487 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:14,487 DEBUG: Start:	 Training
+2016-09-06 10:34:14,505 DEBUG: Info:	 Time for Training: 0.160764932632[s]
+2016-09-06 10:34:14,505 DEBUG: Done:	 Training
+2016-09-06 10:34:14,505 DEBUG: Start:	 Predicting
+2016-09-06 10:34:14,509 DEBUG: Done:	 Predicting
+2016-09-06 10:34:14,509 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:14,510 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:14,510 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.62380952381
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,510 INFO: Done:	 Result Analysis
+2016-09-06 10:34:14,599 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,599 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,599 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:34:14,599 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:34:14,599 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:14,599 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:14,600 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,600 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,600 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,600 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,601 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:14,601 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:14,601 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,601 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,648 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:14,648 DEBUG: Start:	 Training
+2016-09-06 10:34:14,649 DEBUG: Info:	 Time for Training: 0.0514440536499[s]
+2016-09-06 10:34:14,649 DEBUG: Done:	 Training
+2016-09-06 10:34:14,649 DEBUG: Start:	 Predicting
+2016-09-06 10:34:14,651 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:14,651 DEBUG: Start:	 Training
+2016-09-06 10:34:14,653 DEBUG: Done:	 Predicting
+2016-09-06 10:34:14,654 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:14,655 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:14,655 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,656 INFO: Done:	 Result Analysis
+2016-09-06 10:34:14,670 DEBUG: Info:	 Time for Training: 0.0719361305237[s]
+2016-09-06 10:34:14,670 DEBUG: Done:	 Training
+2016-09-06 10:34:14,670 DEBUG: Start:	 Predicting
+2016-09-06 10:34:14,673 DEBUG: Done:	 Predicting
+2016-09-06 10:34:14,673 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:14,675 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:14,675 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.471428571429
+accuracy_score on test : 0.622222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.471428571429
+		- Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,675 INFO: Done:	 Result Analysis
+2016-09-06 10:34:14,747 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,748 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:34:14,748 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:14,748 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,748 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,749 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,749 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:34:14,749 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:14,749 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:14,749 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,750 DEBUG: Info:	 Shape X_train:(210, 9), Length of y_train:210
+2016-09-06 10:34:14,750 DEBUG: Info:	 Shape X_test:(90, 9), Length of y_test:90
+2016-09-06 10:34:14,750 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:14,750 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,803 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:14,803 DEBUG: Start:	 Training
+2016-09-06 10:34:14,803 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:14,804 DEBUG: Start:	 Training
+2016-09-06 10:34:14,821 DEBUG: Info:	 Time for Training: 0.0726821422577[s]
+2016-09-06 10:34:14,821 DEBUG: Done:	 Training
+2016-09-06 10:34:14,821 DEBUG: Start:	 Predicting
+2016-09-06 10:34:14,821 DEBUG: Info:	 Time for Training: 0.0746259689331[s]
+2016-09-06 10:34:14,822 DEBUG: Done:	 Training
+2016-09-06 10:34:14,822 DEBUG: Start:	 Predicting
+2016-09-06 10:34:14,825 DEBUG: Done:	 Predicting
+2016-09-06 10:34:14,825 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:14,826 DEBUG: Done:	 Predicting
+2016-09-06 10:34:14,826 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:14,826 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:14,827 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.990476190476
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,827 INFO: Done:	 Result Analysis
+2016-09-06 10:34:14,827 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:14,827 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,828 INFO: Done:	 Result Analysis
+2016-09-06 10:34:14,892 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,893 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:34:14,893 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:14,893 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:14,893 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:34:14,893 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:14,894 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:14,894 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:14,894 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:14,894 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:14,894 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:14,894 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,894 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:14,895 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:14,927 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:14,927 DEBUG: Start:	 Training
+2016-09-06 10:34:14,928 DEBUG: Info:	 Time for Training: 0.036386013031[s]
+2016-09-06 10:34:14,928 DEBUG: Done:	 Training
+2016-09-06 10:34:14,928 DEBUG: Start:	 Predicting
+2016-09-06 10:34:14,931 DEBUG: Done:	 Predicting
+2016-09-06 10:34:14,932 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:14,933 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:14,933 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.6
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,933 INFO: Done:	 Result Analysis
+2016-09-06 10:34:14,948 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:14,948 DEBUG: Start:	 Training
+2016-09-06 10:34:14,952 DEBUG: Info:	 Time for Training: 0.0607821941376[s]
+2016-09-06 10:34:14,953 DEBUG: Done:	 Training
+2016-09-06 10:34:14,953 DEBUG: Start:	 Predicting
+2016-09-06 10:34:14,955 DEBUG: Done:	 Predicting
+2016-09-06 10:34:14,956 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:14,957 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:14,957 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:14,958 INFO: Done:	 Result Analysis
+2016-09-06 10:34:15,039 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,039 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,039 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:34:15,039 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:34:15,040 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:15,040 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:15,040 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,040 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,040 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,040 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,040 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:15,040 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:15,040 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,040 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,073 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:15,073 DEBUG: Start:	 Training
+2016-09-06 10:34:15,074 DEBUG: Info:	 Time for Training: 0.0351400375366[s]
+2016-09-06 10:34:15,074 DEBUG: Done:	 Training
+2016-09-06 10:34:15,074 DEBUG: Start:	 Predicting
+2016-09-06 10:34:15,081 DEBUG: Done:	 Predicting
+2016-09-06 10:34:15,081 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:15,083 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:15,083 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 34
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,083 INFO: Done:	 Result Analysis
+2016-09-06 10:34:15,182 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:15,182 DEBUG: Start:	 Training
+2016-09-06 10:34:15,200 DEBUG: Info:	 Time for Training: 0.161078929901[s]
+2016-09-06 10:34:15,200 DEBUG: Done:	 Training
+2016-09-06 10:34:15,200 DEBUG: Start:	 Predicting
+2016-09-06 10:34:15,204 DEBUG: Done:	 Predicting
+2016-09-06 10:34:15,204 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:15,205 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:15,205 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.685714285714
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.685714285714
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,205 INFO: Done:	 Result Analysis
+2016-09-06 10:34:15,291 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,291 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:34:15,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:34:15,292 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:15,292 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:15,293 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,293 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,294 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,294 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,294 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:15,294 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:15,294 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,294 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,362 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:15,362 DEBUG: Start:	 Training
+2016-09-06 10:34:15,363 DEBUG: Info:	 Time for Training: 0.07275390625[s]
+2016-09-06 10:34:15,363 DEBUG: Done:	 Training
+2016-09-06 10:34:15,363 DEBUG: Start:	 Predicting
+2016-09-06 10:34:15,369 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:15,369 DEBUG: Start:	 Training
+2016-09-06 10:34:15,378 DEBUG: Done:	 Predicting
+2016-09-06 10:34:15,378 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:15,380 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:15,380 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.633333333333
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.633333333333
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,381 INFO: Done:	 Result Analysis
+2016-09-06 10:34:15,396 DEBUG: Info:	 Time for Training: 0.105423927307[s]
+2016-09-06 10:34:15,396 DEBUG: Done:	 Training
+2016-09-06 10:34:15,396 DEBUG: Start:	 Predicting
+2016-09-06 10:34:15,399 DEBUG: Done:	 Predicting
+2016-09-06 10:34:15,399 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:15,401 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:15,401 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.388888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.388888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,401 INFO: Done:	 Result Analysis
+2016-09-06 10:34:15,536 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,536 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:34:15,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:34:15,536 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:15,536 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:15,537 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,537 DEBUG: Info:	 Shape X_train:(210, 15), Length of y_train:210
+2016-09-06 10:34:15,537 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,537 DEBUG: Info:	 Shape X_test:(90, 15), Length of y_test:90
+2016-09-06 10:34:15,537 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:15,537 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:15,537 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,537 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,607 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:15,607 DEBUG: Start:	 Training
+2016-09-06 10:34:15,616 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:15,616 DEBUG: Start:	 Training
+2016-09-06 10:34:15,632 DEBUG: Info:	 Time for Training: 0.0972080230713[s]
+2016-09-06 10:34:15,632 DEBUG: Done:	 Training
+2016-09-06 10:34:15,632 DEBUG: Start:	 Predicting
+2016-09-06 10:34:15,641 DEBUG: Done:	 Predicting
+2016-09-06 10:34:15,641 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:15,643 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:15,643 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,643 DEBUG: Info:	 Time for Training: 0.10800909996[s]
+2016-09-06 10:34:15,643 DEBUG: Done:	 Training
+2016-09-06 10:34:15,643 INFO: Done:	 Result Analysis
+2016-09-06 10:34:15,643 DEBUG: Start:	 Predicting
+2016-09-06 10:34:15,647 DEBUG: Done:	 Predicting
+2016-09-06 10:34:15,648 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:15,649 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:15,649 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,649 INFO: Done:	 Result Analysis
+2016-09-06 10:34:15,791 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,791 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,792 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:34:15,792 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:34:15,792 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:15,792 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:15,793 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:15,793 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:15,793 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:15,793 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:15,793 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:15,793 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:15,793 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,793 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,840 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:15,840 DEBUG: Start:	 Training
+2016-09-06 10:34:15,841 DEBUG: Info:	 Time for Training: 0.0506250858307[s]
+2016-09-06 10:34:15,842 DEBUG: Done:	 Training
+2016-09-06 10:34:15,842 DEBUG: Start:	 Predicting
+2016-09-06 10:34:15,845 DEBUG: Done:	 Predicting
+2016-09-06 10:34:15,846 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:15,848 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:15,848 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,848 INFO: Done:	 Result Analysis
+2016-09-06 10:34:15,867 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:15,867 DEBUG: Start:	 Training
+2016-09-06 10:34:15,873 DEBUG: Info:	 Time for Training: 0.081845998764[s]
+2016-09-06 10:34:15,873 DEBUG: Done:	 Training
+2016-09-06 10:34:15,873 DEBUG: Start:	 Predicting
+2016-09-06 10:34:15,876 DEBUG: Done:	 Predicting
+2016-09-06 10:34:15,876 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:15,877 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:15,878 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.611111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:15,878 INFO: Done:	 Result Analysis
+2016-09-06 10:34:15,935 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,935 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:15,936 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:34:15,936 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:34:15,936 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:15,936 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:15,937 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:15,937 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:15,937 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:15,937 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:15,937 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:15,937 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:15,937 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,937 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:15,986 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:15,986 DEBUG: Start:	 Training
+2016-09-06 10:34:15,987 DEBUG: Info:	 Time for Training: 0.0520849227905[s]
+2016-09-06 10:34:15,987 DEBUG: Done:	 Training
+2016-09-06 10:34:15,987 DEBUG: Start:	 Predicting
+2016-09-06 10:34:15,997 DEBUG: Done:	 Predicting
+2016-09-06 10:34:15,997 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:16,000 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:16,000 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 34
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,000 INFO: Done:	 Result Analysis
+2016-09-06 10:34:16,100 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:16,100 DEBUG: Start:	 Training
+2016-09-06 10:34:16,117 DEBUG: Info:	 Time for Training: 0.182558059692[s]
+2016-09-06 10:34:16,117 DEBUG: Done:	 Training
+2016-09-06 10:34:16,117 DEBUG: Start:	 Predicting
+2016-09-06 10:34:16,121 DEBUG: Done:	 Predicting
+2016-09-06 10:34:16,121 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:16,123 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:16,123 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.680952380952
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.680952380952
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,123 INFO: Done:	 Result Analysis
+2016-09-06 10:34:16,184 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,184 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,185 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:34:16,185 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:34:16,185 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:16,185 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:16,185 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:16,185 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:16,185 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:16,186 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:16,186 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:16,186 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:16,186 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,186 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,231 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:16,232 DEBUG: Start:	 Training
+2016-09-06 10:34:16,232 DEBUG: Info:	 Time for Training: 0.0485727787018[s]
+2016-09-06 10:34:16,232 DEBUG: Done:	 Training
+2016-09-06 10:34:16,233 DEBUG: Start:	 Predicting
+2016-09-06 10:34:16,239 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:16,239 DEBUG: Start:	 Training
+2016-09-06 10:34:16,248 DEBUG: Done:	 Predicting
+2016-09-06 10:34:16,248 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:16,250 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:16,250 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.614285714286
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,250 INFO: Done:	 Result Analysis
+2016-09-06 10:34:16,262 DEBUG: Info:	 Time for Training: 0.0779819488525[s]
+2016-09-06 10:34:16,262 DEBUG: Done:	 Training
+2016-09-06 10:34:16,262 DEBUG: Start:	 Predicting
+2016-09-06 10:34:16,265 DEBUG: Done:	 Predicting
+2016-09-06 10:34:16,266 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:16,267 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:16,267 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.528571428571
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,267 INFO: Done:	 Result Analysis
+2016-09-06 10:34:16,341 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,341 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,342 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:34:16,342 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:34:16,342 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:16,342 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:16,343 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:16,343 DEBUG: Info:	 Shape X_train:(210, 17), Length of y_train:210
+2016-09-06 10:34:16,343 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:16,343 DEBUG: Info:	 Shape X_test:(90, 17), Length of y_test:90
+2016-09-06 10:34:16,343 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:16,343 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:16,344 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,344 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,409 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:16,410 DEBUG: Start:	 Training
+2016-09-06 10:34:16,413 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:16,413 DEBUG: Start:	 Training
+2016-09-06 10:34:16,431 DEBUG: Info:	 Time for Training: 0.0899150371552[s]
+2016-09-06 10:34:16,431 DEBUG: Done:	 Training
+2016-09-06 10:34:16,431 DEBUG: Start:	 Predicting
+2016-09-06 10:34:16,431 DEBUG: Info:	 Time for Training: 0.0902900695801[s]
+2016-09-06 10:34:16,431 DEBUG: Done:	 Training
+2016-09-06 10:34:16,431 DEBUG: Start:	 Predicting
+2016-09-06 10:34:16,435 DEBUG: Done:	 Predicting
+2016-09-06 10:34:16,436 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:16,437 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:16,437 DEBUG: Done:	 Predicting
+2016-09-06 10:34:16,437 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,437 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:16,437 INFO: Done:	 Result Analysis
+2016-09-06 10:34:16,439 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:16,439 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,439 INFO: Done:	 Result Analysis
+2016-09-06 10:34:16,585 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,585 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:34:16,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:34:16,586 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:16,586 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:16,586 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,586 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,586 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,586 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,586 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:16,586 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:16,587 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,587 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,617 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:16,617 DEBUG: Start:	 Training
+2016-09-06 10:34:16,618 DEBUG: Info:	 Time for Training: 0.0332248210907[s]
+2016-09-06 10:34:16,618 DEBUG: Done:	 Training
+2016-09-06 10:34:16,618 DEBUG: Start:	 Predicting
+2016-09-06 10:34:16,621 DEBUG: Done:	 Predicting
+2016-09-06 10:34:16,621 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:16,622 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:16,622 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,623 INFO: Done:	 Result Analysis
+2016-09-06 10:34:16,636 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:16,636 DEBUG: Start:	 Training
+2016-09-06 10:34:16,640 DEBUG: Info:	 Time for Training: 0.054888010025[s]
+2016-09-06 10:34:16,640 DEBUG: Done:	 Training
+2016-09-06 10:34:16,640 DEBUG: Start:	 Predicting
+2016-09-06 10:34:16,643 DEBUG: Done:	 Predicting
+2016-09-06 10:34:16,643 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:16,645 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:16,645 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,645 INFO: Done:	 Result Analysis
+2016-09-06 10:34:16,736 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,736 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,736 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:34:16,736 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:34:16,737 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:16,737 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:16,737 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,737 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,737 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,737 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,737 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:16,737 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:16,738 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,738 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,771 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:16,771 DEBUG: Start:	 Training
+2016-09-06 10:34:16,772 DEBUG: Info:	 Time for Training: 0.0359029769897[s]
+2016-09-06 10:34:16,772 DEBUG: Done:	 Training
+2016-09-06 10:34:16,772 DEBUG: Start:	 Predicting
+2016-09-06 10:34:16,779 DEBUG: Done:	 Predicting
+2016-09-06 10:34:16,779 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:16,780 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:16,780 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 34
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,780 INFO: Done:	 Result Analysis
+2016-09-06 10:34:16,879 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:16,879 DEBUG: Start:	 Training
+2016-09-06 10:34:16,896 DEBUG: Info:	 Time for Training: 0.160787820816[s]
+2016-09-06 10:34:16,897 DEBUG: Done:	 Training
+2016-09-06 10:34:16,897 DEBUG: Start:	 Predicting
+2016-09-06 10:34:16,900 DEBUG: Done:	 Predicting
+2016-09-06 10:34:16,900 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:16,902 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:16,902 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.709523809524
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.709523809524
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:16,902 INFO: Done:	 Result Analysis
+2016-09-06 10:34:16,988 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,988 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:34:16,989 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:34:16,989 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:34:16,989 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:16,989 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:34:16,990 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,990 DEBUG: Info:	 Shape X_train:(210, 10), Length of y_train:210
+2016-09-06 10:34:16,990 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,990 DEBUG: Info:	 Shape X_test:(90, 10), Length of y_test:90
+2016-09-06 10:34:16,990 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:16,990 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:34:16,990 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:16,990 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:34:17,060 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:17,061 DEBUG: Start:	 Training
+2016-09-06 10:34:17,062 DEBUG: Info:	 Time for Training: 0.0743260383606[s]
+2016-09-06 10:34:17,062 DEBUG: Done:	 Training
+2016-09-06 10:34:17,062 DEBUG: Start:	 Predicting
+2016-09-06 10:34:17,066 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:34:17,066 DEBUG: Start:	 Training
+2016-09-06 10:34:17,076 DEBUG: Done:	 Predicting
+2016-09-06 10:34:17,077 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:17,080 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:17,080 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:17,081 INFO: Done:	 Result Analysis
+2016-09-06 10:34:17,094 DEBUG: Info:	 Time for Training: 0.106731176376[s]
+2016-09-06 10:34:17,094 DEBUG: Done:	 Training
+2016-09-06 10:34:17,094 DEBUG: Start:	 Predicting
+2016-09-06 10:34:17,097 DEBUG: Done:	 Predicting
+2016-09-06 10:34:17,097 DEBUG: Start:	 Getting Results
+2016-09-06 10:34:17,099 DEBUG: Done:	 Getting Results
+2016-09-06 10:34:17,099 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.490476190476
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:34:17,099 INFO: Done:	 Result Analysis
+2016-09-06 10:34:17,389 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:34:17,389 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:34:17,390 INFO: Info:	 Shape of View0 :(300, 9)
+2016-09-06 10:34:17,390 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:34:17,390 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:34:17,390 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-06 10:34:17,391 INFO: Info:	 Shape of View0 :(300, 9)
+2016-09-06 10:34:17,391 INFO: Info:	 Shape of View2 :(300, 17)
+2016-09-06 10:34:17,392 INFO: Info:	 Shape of View1 :(300, 15)
+2016-09-06 10:34:17,392 INFO: Info:	 Shape of View3 :(300, 10)
+2016-09-06 10:34:17,392 INFO: Done:	 Read Database Files
+2016-09-06 10:34:17,392 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:34:17,392 INFO: Info:	 Shape of View2 :(300, 17)
+2016-09-06 10:34:17,393 INFO: Info:	 Shape of View3 :(300, 10)
+2016-09-06 10:34:17,393 INFO: Done:	 Read Database Files
+2016-09-06 10:34:17,393 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:34:17,397 INFO: Done:	 Determine validation split
+2016-09-06 10:34:17,398 INFO: Start:	 Determine 5 folds
+2016-09-06 10:34:17,398 INFO: Done:	 Determine validation split
+2016-09-06 10:34:17,398 INFO: Start:	 Determine 5 folds
+2016-09-06 10:34:17,407 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:34:17,408 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:34:17,408 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:34:17,408 INFO: Done:	 Determine folds
+2016-09-06 10:34:17,408 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:34:17,408 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:34:17,408 INFO: Start:	 Classification
+2016-09-06 10:34:17,408 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:34:17,408 INFO: 	Start:	 Fold number 1
+2016-09-06 10:34:17,408 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:34:17,408 INFO: Done:	 Determine folds
+2016-09-06 10:34:17,408 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:34:17,408 INFO: Start:	 Classification
+2016-09-06 10:34:17,408 INFO: 	Start:	 Fold number 1
+2016-09-06 10:34:17,439 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:34:17,447 DEBUG: 			View 0 : 0.526627218935
+2016-09-06 10:34:17,455 DEBUG: 			View 1 : 0.455621301775
+2016-09-06 10:34:17,463 DEBUG: 			View 2 : 0.473372781065
+2016-09-06 10:34:17,471 DEBUG: 			View 3 : 0.485207100592
+2016-09-06 10:34:17,504 DEBUG: 			 Best view : 		View2
+2016-09-06 10:34:17,582 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:34:17,590 DEBUG: 			View 0 : 0.710059171598
+2016-09-06 10:34:17,598 DEBUG: 			View 1 : 0.763313609467
+2016-09-06 10:34:17,605 DEBUG: 			View 2 : 0.775147928994
+2016-09-06 10:34:17,613 DEBUG: 			View 3 : 0.715976331361
+2016-09-06 10:34:17,650 DEBUG: 			 Best view : 		View2
+2016-09-06 10:34:17,793 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:34:17,807 DEBUG: 			View 0 : 0.710059171598
+2016-09-06 10:34:17,815 DEBUG: 			View 1 : 0.763313609467
+2016-09-06 10:34:17,824 DEBUG: 			View 2 : 0.775147928994
+2016-09-06 10:34:17,831 DEBUG: 			View 3 : 0.715976331361
+2016-09-06 10:34:17,875 DEBUG: 			 Best view : 		View2
+2016-09-06 10:34:18,092 DEBUG: 		Start:	 Iteration 4
+2016-09-06 10:34:18,099 DEBUG: 			View 0 : 0.662721893491
+2016-09-06 10:34:18,107 DEBUG: 			View 1 : 0.662721893491
+2016-09-06 10:34:18,114 DEBUG: 			View 2 : 0.633136094675
+2016-09-06 10:34:18,121 DEBUG: 			View 3 : 0.680473372781
+2016-09-06 10:34:18,162 DEBUG: 			 Best view : 		View3
+2016-09-06 10:34:18,430 INFO: 	Start: 	 Classification
+2016-09-06 10:34:18,888 INFO: 	Done: 	 Fold number 1
+2016-09-06 10:34:18,888 INFO: 	Start:	 Fold number 2
+2016-09-06 10:34:18,917 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:34:18,924 DEBUG: 			View 0 : 0.556213017751
+2016-09-06 10:34:18,931 DEBUG: 			View 1 : 0.603550295858
+2016-09-06 10:34:18,939 DEBUG: 			View 2 : 0.497041420118
+2016-09-06 10:34:18,946 DEBUG: 			View 3 : 0.491124260355
+2016-09-06 10:34:18,976 DEBUG: 			 Best view : 		View1
+2016-09-06 10:34:19,051 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:34:19,059 DEBUG: 			View 0 : 0.715976331361
+2016-09-06 10:34:19,066 DEBUG: 			View 1 : 0.680473372781
+2016-09-06 10:34:19,073 DEBUG: 			View 2 : 0.710059171598
+2016-09-06 10:34:19,080 DEBUG: 			View 3 : 0.704142011834
+2016-09-06 10:34:19,116 DEBUG: 			 Best view : 		View0
+2016-09-06 10:34:19,257 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:34:19,264 DEBUG: 			View 0 : 0.715976331361
+2016-09-06 10:34:19,272 DEBUG: 			View 1 : 0.680473372781
+2016-09-06 10:34:19,279 DEBUG: 			View 2 : 0.710059171598
+2016-09-06 10:34:19,287 DEBUG: 			View 3 : 0.704142011834
+2016-09-06 10:34:19,325 DEBUG: 			 Best view : 		View0
+2016-09-06 10:34:19,529 INFO: 	Start: 	 Classification
+2016-09-06 10:34:19,873 INFO: 	Done: 	 Fold number 2
+2016-09-06 10:34:19,874 INFO: 	Start:	 Fold number 3
+2016-09-06 10:34:19,903 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:34:19,909 DEBUG: 			View 0 : 0.544378698225
+2016-09-06 10:34:19,916 DEBUG: 			View 1 : 0.544378698225
+2016-09-06 10:34:19,923 DEBUG: 			View 2 : 0.544378698225
+2016-09-06 10:34:19,930 DEBUG: 			View 3 : 0.544378698225
+2016-09-06 10:34:19,961 DEBUG: 			 Best view : 		View0
+2016-09-06 10:34:20,036 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:34:20,043 DEBUG: 			View 0 : 0.692307692308
+2016-09-06 10:34:20,051 DEBUG: 			View 1 : 0.692307692308
+2016-09-06 10:34:20,058 DEBUG: 			View 2 : 0.769230769231
+2016-09-06 10:34:20,065 DEBUG: 			View 3 : 0.733727810651
+2016-09-06 10:34:20,101 DEBUG: 			 Best view : 		View2
+2016-09-06 10:34:20,241 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:34:20,249 DEBUG: 			View 0 : 0.692307692308
+2016-09-06 10:34:20,256 DEBUG: 			View 1 : 0.692307692308
+2016-09-06 10:34:20,264 DEBUG: 			View 2 : 0.769230769231
+2016-09-06 10:34:20,271 DEBUG: 			View 3 : 0.733727810651
+2016-09-06 10:34:20,310 DEBUG: 			 Best view : 		View2
+2016-09-06 10:34:20,513 INFO: 	Start: 	 Classification
+2016-09-06 10:34:20,853 INFO: 	Done: 	 Fold number 3
+2016-09-06 10:34:20,853 INFO: 	Start:	 Fold number 4
+2016-09-06 10:34:20,882 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:34:20,889 DEBUG: 			View 0 : 0.585798816568
+2016-09-06 10:34:20,896 DEBUG: 			View 1 : 0.502958579882
+2016-09-06 10:34:20,904 DEBUG: 			View 2 : 0.538461538462
+2016-09-06 10:34:20,911 DEBUG: 			View 3 : 0.544378698225
+2016-09-06 10:34:20,940 DEBUG: 			 Best view : 		View3
+2016-09-06 10:34:21,016 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:34:21,023 DEBUG: 			View 0 : 0.692307692308
+2016-09-06 10:34:21,031 DEBUG: 			View 1 : 0.704142011834
+2016-09-06 10:34:21,038 DEBUG: 			View 2 : 0.650887573964
+2016-09-06 10:34:21,045 DEBUG: 			View 3 : 0.698224852071
+2016-09-06 10:34:21,081 DEBUG: 			 Best view : 		View1
+2016-09-06 10:34:21,221 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:34:21,228 DEBUG: 			View 0 : 0.692307692308
+2016-09-06 10:34:21,235 DEBUG: 			View 1 : 0.704142011834
+2016-09-06 10:34:21,243 DEBUG: 			View 2 : 0.650887573964
+2016-09-06 10:34:21,250 DEBUG: 			View 3 : 0.698224852071
+2016-09-06 10:34:21,289 DEBUG: 			 Best view : 		View1
+2016-09-06 10:34:21,493 INFO: 	Start: 	 Classification
+2016-09-06 10:34:21,834 INFO: 	Done: 	 Fold number 4
+2016-09-06 10:34:21,834 INFO: 	Start:	 Fold number 5
+2016-09-06 10:34:21,864 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:34:21,871 DEBUG: 			View 0 : 0.455621301775
+2016-09-06 10:34:21,877 DEBUG: 			View 1 : 0.455621301775
+2016-09-06 10:34:21,884 DEBUG: 			View 2 : 0.455621301775
+2016-09-06 10:34:21,891 DEBUG: 			View 3 : 0.455621301775
+2016-09-06 10:34:21,891 WARNING: 		WARNING:	All bad for iteration 0
+2016-09-06 10:34:21,920 DEBUG: 			 Best view : 		View0
+2016-09-06 10:34:21,995 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:34:22,002 DEBUG: 			View 0 : 0.680473372781
+2016-09-06 10:34:22,010 DEBUG: 			View 1 : 0.733727810651
+2016-09-06 10:34:22,017 DEBUG: 			View 2 : 0.745562130178
+2016-09-06 10:34:22,024 DEBUG: 			View 3 : 0.710059171598
+2016-09-06 10:34:22,060 DEBUG: 			 Best view : 		View2
+2016-09-06 10:34:22,200 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:34:22,206 DEBUG: 			View 0 : 0.680473372781
+2016-09-06 10:34:22,214 DEBUG: 			View 1 : 0.733727810651
+2016-09-06 10:34:22,221 DEBUG: 			View 2 : 0.745562130178
+2016-09-06 10:34:22,228 DEBUG: 			View 3 : 0.710059171598
+2016-09-06 10:34:22,267 DEBUG: 			 Best view : 		View2
+2016-09-06 10:34:22,470 INFO: 	Start: 	 Classification
+2016-09-06 10:34:22,812 INFO: 	Done: 	 Fold number 5
+2016-09-06 10:34:22,812 INFO: Done:	 Classification
+2016-09-06 10:34:22,812 INFO: Info:	 Time for Classification: 5[s]
+2016-09-06 10:34:22,812 INFO: Start:	 Result Analysis for Mumbo
+2016-09-06 10:34:24,767 INFO: 		Result for Multiview classification with Mumbo
+
+Average accuracy :
+	-On Train : 74.201183432
+	-On Test : 49.5238095238
+	-On Validation : 52.808988764Dataset info :
+	-Dataset name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : View0 of shape (300, 9), View1 of shape (300, 15), View2 of shape (300, 17), View3 of shape (300, 10)
+	-5 folds
+	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration : 
+	-Used 1 core(s)
+	-Iterations : min 1, max 10, threshold 0.01
+	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:01        0:00:00
+	         Fold 2        0:00:02        0:00:00
+	         Fold 3        0:00:03        0:00:00
+	         Fold 4        0:00:04        0:00:00
+	         Fold 5        0:00:05        0:00:00
+	          Total        0:00:15        0:00:01
+	So a total classification time of 0:00:05.
+
+
+
+Mean average accuracies and stats for each fold :
+	- Fold 0, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.260946745562
+			- Percentage of time chosen : 0.6
+		- On View1 : 
+			- Mean average Accuracy : 0.26449704142
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.265680473373
+			- Percentage of time chosen : 0.3
+		- On View3 : 
+			- Mean average Accuracy : 0.259763313609
+			- Percentage of time chosen : 0.1
+	- Fold 1, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.198816568047
+			- Percentage of time chosen : 0.9
+		- On View1 : 
+			- Mean average Accuracy : 0.196449704142
+			- Percentage of time chosen : 0.1
+		- On View2 : 
+			- Mean average Accuracy : 0.191715976331
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.189940828402
+			- Percentage of time chosen : 0.0
+	- Fold 2, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.192899408284
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.192899408284
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.208284023669
+			- Percentage of time chosen : 0.2
+		- On View3 : 
+			- Mean average Accuracy : 0.201183431953
+			- Percentage of time chosen : 0.0
+	- Fold 3, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.197041420118
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.191124260355
+			- Percentage of time chosen : 0.2
+		- On View2 : 
+			- Mean average Accuracy : 0.184023668639
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.194082840237
+			- Percentage of time chosen : 0.1
+	- Fold 4, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.181656804734
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.192307692308
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.194674556213
+			- Percentage of time chosen : 0.2
+		- On View3 : 
+			- Mean average Accuracy : 0.187573964497
+			- Percentage of time chosen : 0.0
+
+ For each iteration : 
+	- Iteration 1
+		 Fold 1
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View2
+		 Fold 2
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+	- Iteration 2
+		 Fold 1
+			Accuracy on train : 77.5147928994
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.5617977528
+			Selected View : View2
+		 Fold 2
+			Accuracy on train : 71.5976331361
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 76.9230769231
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.808988764
+			Selected View : View2
+		 Fold 4
+			Accuracy on train : 70.4142011834
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View1
+		 Fold 5
+			Accuracy on train : 74.5562130178
+			Accuracy on test : 0.0
+			Accuracy on validation : 46.0674157303
+			Selected View : View2
+	- Iteration 3
+		 Fold 1
+			Accuracy on train : 77.5147928994
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.5617977528
+			Selected View : View2
+		 Fold 2
+			Accuracy on train : 71.5976331361
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 76.9230769231
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.808988764
+			Selected View : View2
+		 Fold 4
+			Accuracy on train : 70.4142011834
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View1
+		 Fold 5
+			Accuracy on train : 74.5562130178
+			Accuracy on test : 0.0
+			Accuracy on validation : 46.0674157303
+			Selected View : View2
+	- Iteration 4
+		 Fold 1
+			Accuracy on train : 77.5147928994
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.5617977528
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+	- Iteration 5
+		 Fold 1
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+2016-09-06 10:34:24,951 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b21ed4f89b12e4e6cc230bb6c2c11cce8c59bafb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..77474e0f2cfa4d49e3cc6bdcb34c985e935a8331
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.6
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..09eb9bd997ef68d2394c6169df6157316cf189c6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.57619047619
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 34
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.57619047619
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..75aeeab8d5f66df7d6c23af6c3a6e17eb066910e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.62380952381
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..de64a87eba45859e1bba6de874ddcd46cef0b48a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8accfa21b0b077c4478095c473f44ae09a2de3e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.471428571429
+accuracy_score on test : 0.622222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.471428571429
+		- Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8d98bcf93125e7cc7c715dd8f8a84c79024e2387
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.990476190476
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ebca06a942aecfa1f3d5ffb4934bf508a10271f0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 9)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ca874bc8bb3d2e4a6c3aa970a37b2e5bfdeab058
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.611111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.611111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..33f3d2e5301edd079b28096300c0f3454e5a744f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ef3258f892730096ac37a2e27b2b2ae30580dbb6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 34
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..42abfcbf91a82ab1deec96781e4c053620e3f3bb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.685714285714
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.685714285714
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..098bc8e60e42866c1bd874028d7e84f3fbc51829
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.633333333333
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.633333333333
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f8a69440f753bd0b9135db3c7090bee2fec84e90
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.5
+accuracy_score on test : 0.388888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.5
+		- Score on test : 0.388888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..85ebfdd4167460ea1e22921a464245bba62cbf57
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d4cd5d9a640932eb06164cd69e789bba45a1c367
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 15)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e41e726a1e154dfcce7ac5a2ca77060ce501d3c1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3c3a4f96943d487d993e2ec45692351be9998f68
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9bf78e66e84b4bfff3fc3f5edf7fe5be6f0510d1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.590476190476
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 34
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.590476190476
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0b0dc718c52da05c632439a0aa6ecccd1923b079
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.709523809524
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 7, max_depth : 2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.709523809524
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fe4a10e8c09a7d242953e9b36f98a6fcb347fc04
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.614285714286
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.614285714286
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..eaf4be419957663e1b48d32421a7020c8e185219
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.528571428571
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7a8b79bfd3a2c1f4a7531f302cacb9a873abffc8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b1b7d9bc14b194df6ffcfcd0d29cd627320eadbe
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 17)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dc3c519a66862291779f5923512c88856c638c5b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.552380952381
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.552380952381
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6bbe21818a569ca1bebb20ba2433686351c54053
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.490476190476
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 10)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 2978
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.490476190476
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
new file mode 100644
index 0000000000000000000000000000000000000000..6055a9f380f8d3c63daeba5147253811e3b5075a
Binary files /dev/null and b/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f514bd751d264316a0deeeba3c4b770eef1bbee8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,215 @@
+		Result for Multiview classification with Mumbo
+
+Average accuracy :
+	-On Train : 74.201183432
+	-On Test : 49.5238095238
+	-On Validation : 52.808988764Dataset info :
+	-Dataset name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : View0 of shape (300, 9), View1 of shape (300, 15), View2 of shape (300, 17), View3 of shape (300, 10)
+	-5 folds
+	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration : 
+	-Used 1 core(s)
+	-Iterations : min 1, max 10, threshold 0.01
+	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:01        0:00:00
+	         Fold 2        0:00:02        0:00:00
+	         Fold 3        0:00:03        0:00:00
+	         Fold 4        0:00:04        0:00:00
+	         Fold 5        0:00:05        0:00:00
+	          Total        0:00:15        0:00:01
+	So a total classification time of 0:00:05.
+
+
+
+Mean average accuracies and stats for each fold :
+	- Fold 0, used 5
+		- On View0 : 
+			- Mean average Accuracy : 0.260946745562
+			- Percentage of time chosen : 0.6
+		- On View1 : 
+			- Mean average Accuracy : 0.26449704142
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.265680473373
+			- Percentage of time chosen : 0.3
+		- On View3 : 
+			- Mean average Accuracy : 0.259763313609
+			- Percentage of time chosen : 0.1
+	- Fold 1, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.198816568047
+			- Percentage of time chosen : 0.9
+		- On View1 : 
+			- Mean average Accuracy : 0.196449704142
+			- Percentage of time chosen : 0.1
+		- On View2 : 
+			- Mean average Accuracy : 0.191715976331
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.189940828402
+			- Percentage of time chosen : 0.0
+	- Fold 2, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.192899408284
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.192899408284
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.208284023669
+			- Percentage of time chosen : 0.2
+		- On View3 : 
+			- Mean average Accuracy : 0.201183431953
+			- Percentage of time chosen : 0.0
+	- Fold 3, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.197041420118
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.191124260355
+			- Percentage of time chosen : 0.2
+		- On View2 : 
+			- Mean average Accuracy : 0.184023668639
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.194082840237
+			- Percentage of time chosen : 0.1
+	- Fold 4, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.181656804734
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.192307692308
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.194674556213
+			- Percentage of time chosen : 0.2
+		- On View3 : 
+			- Mean average Accuracy : 0.187573964497
+			- Percentage of time chosen : 0.0
+
+ For each iteration : 
+	- Iteration 1
+		 Fold 1
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View2
+		 Fold 2
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View1
+		 Fold 3
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View3
+		 Fold 5
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+	- Iteration 2
+		 Fold 1
+			Accuracy on train : 77.5147928994
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.5617977528
+			Selected View : View2
+		 Fold 2
+			Accuracy on train : 71.5976331361
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 76.9230769231
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.808988764
+			Selected View : View2
+		 Fold 4
+			Accuracy on train : 70.4142011834
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View1
+		 Fold 5
+			Accuracy on train : 74.5562130178
+			Accuracy on test : 0.0
+			Accuracy on validation : 46.0674157303
+			Selected View : View2
+	- Iteration 3
+		 Fold 1
+			Accuracy on train : 77.5147928994
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.5617977528
+			Selected View : View2
+		 Fold 2
+			Accuracy on train : 71.5976331361
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 76.9230769231
+			Accuracy on test : 0.0
+			Accuracy on validation : 52.808988764
+			Selected View : View2
+		 Fold 4
+			Accuracy on train : 70.4142011834
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View1
+		 Fold 5
+			Accuracy on train : 74.5562130178
+			Accuracy on test : 0.0
+			Accuracy on validation : 46.0674157303
+			Selected View : View2
+	- Iteration 4
+		 Fold 1
+			Accuracy on train : 77.5147928994
+			Accuracy on test : 0.0
+			Accuracy on validation : 50.5617977528
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
+	- Iteration 5
+		 Fold 1
+			Accuracy on train : 54.4378698225
+			Accuracy on test : 0.0
+			Accuracy on validation : 53.9325842697
+			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..e52630384505f65735f8d8a6002f04345a5e7525
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1570 @@
+2016-09-06 10:37:42,137 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:37:42,137 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.00010290625 Gbytes /!\ 
+2016-09-06 10:37:47,151 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:37:47,154 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:37:47,209 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,210 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:37:47,210 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:47,211 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,211 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,211 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,211 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:47,211 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:37:47,211 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,211 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:47,212 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,213 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,213 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:47,213 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,247 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:47,247 DEBUG: Start:	 Training
+2016-09-06 10:37:47,248 DEBUG: Info:	 Time for Training: 0.0386531352997[s]
+2016-09-06 10:37:47,248 DEBUG: Done:	 Training
+2016-09-06 10:37:47,248 DEBUG: Start:	 Predicting
+2016-09-06 10:37:47,251 DEBUG: Done:	 Predicting
+2016-09-06 10:37:47,251 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:47,252 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:47,252 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.633333333333
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.633333333333
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,253 INFO: Done:	 Result Analysis
+2016-09-06 10:37:47,259 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:47,259 DEBUG: Start:	 Training
+2016-09-06 10:37:47,262 DEBUG: Info:	 Time for Training: 0.0534279346466[s]
+2016-09-06 10:37:47,262 DEBUG: Done:	 Training
+2016-09-06 10:37:47,262 DEBUG: Start:	 Predicting
+2016-09-06 10:37:47,265 DEBUG: Done:	 Predicting
+2016-09-06 10:37:47,265 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:47,267 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:47,267 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.622222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,267 INFO: Done:	 Result Analysis
+2016-09-06 10:37:47,354 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,354 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,354 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:37:47,354 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:37:47,354 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:47,354 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:47,355 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,355 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,355 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,355 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,355 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:47,355 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:47,355 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,355 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,386 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:47,386 DEBUG: Start:	 Training
+2016-09-06 10:37:47,387 DEBUG: Info:	 Time for Training: 0.0330820083618[s]
+2016-09-06 10:37:47,387 DEBUG: Done:	 Training
+2016-09-06 10:37:47,387 DEBUG: Start:	 Predicting
+2016-09-06 10:37:47,391 DEBUG: Done:	 Predicting
+2016-09-06 10:37:47,391 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:47,392 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:47,392 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.67619047619
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,392 INFO: Done:	 Result Analysis
+2016-09-06 10:37:47,513 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:47,513 DEBUG: Start:	 Training
+2016-09-06 10:37:47,534 DEBUG: Info:	 Time for Training: 0.180033922195[s]
+2016-09-06 10:37:47,534 DEBUG: Done:	 Training
+2016-09-06 10:37:47,534 DEBUG: Start:	 Predicting
+2016-09-06 10:37:47,538 DEBUG: Done:	 Predicting
+2016-09-06 10:37:47,538 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:47,539 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:47,539 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.766666666667
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 8, max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.766666666667
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,540 INFO: Done:	 Result Analysis
+2016-09-06 10:37:47,604 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,604 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,604 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:37:47,604 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:37:47,604 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:47,604 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:47,605 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,605 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,605 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,605 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,605 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:47,605 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:47,606 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,606 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,651 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:47,651 DEBUG: Start:	 Training
+2016-09-06 10:37:47,652 DEBUG: Info:	 Time for Training: 0.0492498874664[s]
+2016-09-06 10:37:47,652 DEBUG: Done:	 Training
+2016-09-06 10:37:47,652 DEBUG: Start:	 Predicting
+2016-09-06 10:37:47,662 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:47,662 DEBUG: Start:	 Training
+2016-09-06 10:37:47,663 DEBUG: Done:	 Predicting
+2016-09-06 10:37:47,664 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:47,665 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:47,666 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.622222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,666 INFO: Done:	 Result Analysis
+2016-09-06 10:37:47,682 DEBUG: Info:	 Time for Training: 0.0788521766663[s]
+2016-09-06 10:37:47,682 DEBUG: Done:	 Training
+2016-09-06 10:37:47,682 DEBUG: Start:	 Predicting
+2016-09-06 10:37:47,685 DEBUG: Done:	 Predicting
+2016-09-06 10:37:47,685 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:47,686 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:47,686 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,687 INFO: Done:	 Result Analysis
+2016-09-06 10:37:47,750 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,750 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:37:47,750 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:47,751 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,751 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,751 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,751 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:37:47,751 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:47,751 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:47,751 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,752 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:47,752 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:47,752 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:47,752 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,803 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:47,803 DEBUG: Start:	 Training
+2016-09-06 10:37:47,807 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:47,807 DEBUG: Start:	 Training
+2016-09-06 10:37:47,822 DEBUG: Info:	 Time for Training: 0.0717940330505[s]
+2016-09-06 10:37:47,823 DEBUG: Done:	 Training
+2016-09-06 10:37:47,823 DEBUG: Start:	 Predicting
+2016-09-06 10:37:47,827 DEBUG: Info:	 Time for Training: 0.07728099823[s]
+2016-09-06 10:37:47,827 DEBUG: Done:	 Training
+2016-09-06 10:37:47,827 DEBUG: Start:	 Predicting
+2016-09-06 10:37:47,828 DEBUG: Done:	 Predicting
+2016-09-06 10:37:47,828 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:47,829 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:47,829 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,830 INFO: Done:	 Result Analysis
+2016-09-06 10:37:47,831 DEBUG: Done:	 Predicting
+2016-09-06 10:37:47,831 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:47,833 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:47,833 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.366666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.366666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,833 INFO: Done:	 Result Analysis
+2016-09-06 10:37:47,901 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,901 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:47,902 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:37:47,902 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:37:47,902 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:47,902 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:47,903 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:47,903 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:47,903 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:47,903 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:47,903 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:47,903 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:47,903 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,903 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:47,948 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:47,948 DEBUG: Start:	 Training
+2016-09-06 10:37:47,949 DEBUG: Info:	 Time for Training: 0.0490880012512[s]
+2016-09-06 10:37:47,950 DEBUG: Done:	 Training
+2016-09-06 10:37:47,950 DEBUG: Start:	 Predicting
+2016-09-06 10:37:47,953 DEBUG: Done:	 Predicting
+2016-09-06 10:37:47,953 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:47,955 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:47,955 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.738095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.738095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,956 INFO: Done:	 Result Analysis
+2016-09-06 10:37:47,964 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:47,964 DEBUG: Start:	 Training
+2016-09-06 10:37:47,968 DEBUG: Info:	 Time for Training: 0.067459821701[s]
+2016-09-06 10:37:47,968 DEBUG: Done:	 Training
+2016-09-06 10:37:47,968 DEBUG: Start:	 Predicting
+2016-09-06 10:37:47,971 DEBUG: Done:	 Predicting
+2016-09-06 10:37:47,971 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:47,973 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:47,973 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:47,973 INFO: Done:	 Result Analysis
+2016-09-06 10:37:48,050 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,050 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,050 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:37:48,050 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:37:48,050 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:48,050 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:48,051 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,051 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,051 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,051 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,051 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:48,051 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:48,051 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,051 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,081 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:48,081 DEBUG: Start:	 Training
+2016-09-06 10:37:48,082 DEBUG: Info:	 Time for Training: 0.0320069789886[s]
+2016-09-06 10:37:48,082 DEBUG: Done:	 Training
+2016-09-06 10:37:48,082 DEBUG: Start:	 Predicting
+2016-09-06 10:37:48,087 DEBUG: Done:	 Predicting
+2016-09-06 10:37:48,087 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:48,088 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:48,088 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.657142857143
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.657142857143
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,088 INFO: Done:	 Result Analysis
+2016-09-06 10:37:48,209 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:48,209 DEBUG: Start:	 Training
+2016-09-06 10:37:48,230 DEBUG: Info:	 Time for Training: 0.180283069611[s]
+2016-09-06 10:37:48,230 DEBUG: Done:	 Training
+2016-09-06 10:37:48,230 DEBUG: Start:	 Predicting
+2016-09-06 10:37:48,234 DEBUG: Done:	 Predicting
+2016-09-06 10:37:48,234 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:48,236 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:48,236 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.833333333333
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 8, max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.833333333333
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,236 INFO: Done:	 Result Analysis
+2016-09-06 10:37:48,304 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,304 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,305 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:37:48,305 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:37:48,305 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:48,305 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:48,306 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,306 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,307 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,307 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,307 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:48,307 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:48,307 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,307 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,363 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:48,363 DEBUG: Start:	 Training
+2016-09-06 10:37:48,364 DEBUG: Info:	 Time for Training: 0.0610771179199[s]
+2016-09-06 10:37:48,364 DEBUG: Done:	 Training
+2016-09-06 10:37:48,364 DEBUG: Start:	 Predicting
+2016-09-06 10:37:48,371 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:48,371 DEBUG: Start:	 Training
+2016-09-06 10:37:48,389 DEBUG: Done:	 Predicting
+2016-09-06 10:37:48,390 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:48,391 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:48,391 DEBUG: Info:	 Time for Training: 0.088534116745[s]
+2016-09-06 10:37:48,391 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,391 DEBUG: Done:	 Training
+2016-09-06 10:37:48,392 DEBUG: Start:	 Predicting
+2016-09-06 10:37:48,392 INFO: Done:	 Result Analysis
+2016-09-06 10:37:48,395 DEBUG: Done:	 Predicting
+2016-09-06 10:37:48,395 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:48,397 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:48,397 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.604761904762
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,397 INFO: Done:	 Result Analysis
+2016-09-06 10:37:48,548 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,548 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:37:48,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:37:48,548 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:48,548 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:48,549 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,549 DEBUG: Info:	 Shape X_train:(210, 12), Length of y_train:210
+2016-09-06 10:37:48,549 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,549 DEBUG: Info:	 Shape X_test:(90, 12), Length of y_test:90
+2016-09-06 10:37:48,549 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:48,549 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:48,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,600 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:48,600 DEBUG: Start:	 Training
+2016-09-06 10:37:48,603 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:48,603 DEBUG: Start:	 Training
+2016-09-06 10:37:48,612 DEBUG: Info:	 Time for Training: 0.0646460056305[s]
+2016-09-06 10:37:48,612 DEBUG: Done:	 Training
+2016-09-06 10:37:48,612 DEBUG: Start:	 Predicting
+2016-09-06 10:37:48,616 DEBUG: Done:	 Predicting
+2016-09-06 10:37:48,616 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:48,617 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:48,617 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,618 INFO: Done:	 Result Analysis
+2016-09-06 10:37:48,622 DEBUG: Info:	 Time for Training: 0.0746550559998[s]
+2016-09-06 10:37:48,622 DEBUG: Done:	 Training
+2016-09-06 10:37:48,622 DEBUG: Start:	 Predicting
+2016-09-06 10:37:48,628 DEBUG: Done:	 Predicting
+2016-09-06 10:37:48,628 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:48,629 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:48,629 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,629 INFO: Done:	 Result Analysis
+2016-09-06 10:37:48,696 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,696 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:37:48,696 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:48,696 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,696 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:37:48,697 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:48,697 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:48,697 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:48,697 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:48,697 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:48,697 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,698 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:48,698 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:48,698 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,734 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:48,735 DEBUG: Start:	 Training
+2016-09-06 10:37:48,736 DEBUG: Info:	 Time for Training: 0.0404579639435[s]
+2016-09-06 10:37:48,736 DEBUG: Done:	 Training
+2016-09-06 10:37:48,736 DEBUG: Start:	 Predicting
+2016-09-06 10:37:48,739 DEBUG: Done:	 Predicting
+2016-09-06 10:37:48,739 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:48,740 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:48,741 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.690476190476
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.690476190476
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,741 INFO: Done:	 Result Analysis
+2016-09-06 10:37:48,750 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:48,750 DEBUG: Start:	 Training
+2016-09-06 10:37:48,754 DEBUG: Info:	 Time for Training: 0.059161901474[s]
+2016-09-06 10:37:48,754 DEBUG: Done:	 Training
+2016-09-06 10:37:48,754 DEBUG: Start:	 Predicting
+2016-09-06 10:37:48,757 DEBUG: Done:	 Predicting
+2016-09-06 10:37:48,757 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:48,759 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:48,759 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,760 INFO: Done:	 Result Analysis
+2016-09-06 10:37:48,847 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,847 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:37:48,847 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:48,848 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:48,848 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:48,848 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:48,848 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,848 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:48,848 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:37:48,849 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:48,849 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:48,849 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:48,849 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:48,850 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:48,879 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:48,879 DEBUG: Start:	 Training
+2016-09-06 10:37:48,880 DEBUG: Info:	 Time for Training: 0.0334219932556[s]
+2016-09-06 10:37:48,880 DEBUG: Done:	 Training
+2016-09-06 10:37:48,880 DEBUG: Start:	 Predicting
+2016-09-06 10:37:48,885 DEBUG: Done:	 Predicting
+2016-09-06 10:37:48,885 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:48,887 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:48,887 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.690476190476
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.690476190476
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:48,887 INFO: Done:	 Result Analysis
+2016-09-06 10:37:49,024 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:49,024 DEBUG: Start:	 Training
+2016-09-06 10:37:49,046 DEBUG: Info:	 Time for Training: 0.198237895966[s]
+2016-09-06 10:37:49,046 DEBUG: Done:	 Training
+2016-09-06 10:37:49,046 DEBUG: Start:	 Predicting
+2016-09-06 10:37:49,050 DEBUG: Done:	 Predicting
+2016-09-06 10:37:49,051 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:49,053 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:49,053 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.809523809524
+accuracy_score on test : 0.577777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 8, max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.809523809524
+		- Score on test : 0.577777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,053 INFO: Done:	 Result Analysis
+2016-09-06 10:37:49,200 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,200 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,200 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:37:49,200 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:37:49,200 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:49,200 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:49,201 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:49,201 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:49,201 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:49,201 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:49,202 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:49,202 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:49,202 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,202 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,272 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:49,273 DEBUG: Start:	 Training
+2016-09-06 10:37:49,274 DEBUG: Info:	 Time for Training: 0.0749552249908[s]
+2016-09-06 10:37:49,274 DEBUG: Done:	 Training
+2016-09-06 10:37:49,274 DEBUG: Start:	 Predicting
+2016-09-06 10:37:49,279 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:49,279 DEBUG: Start:	 Training
+2016-09-06 10:37:49,293 DEBUG: Done:	 Predicting
+2016-09-06 10:37:49,293 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:49,295 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:49,295 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.604761904762
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,295 INFO: Done:	 Result Analysis
+2016-09-06 10:37:49,299 DEBUG: Info:	 Time for Training: 0.0997180938721[s]
+2016-09-06 10:37:49,299 DEBUG: Done:	 Training
+2016-09-06 10:37:49,299 DEBUG: Start:	 Predicting
+2016-09-06 10:37:49,302 DEBUG: Done:	 Predicting
+2016-09-06 10:37:49,302 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:49,304 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:49,304 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.580952380952
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,304 INFO: Done:	 Result Analysis
+2016-09-06 10:37:49,450 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,450 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,450 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:37:49,450 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:37:49,450 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:49,450 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:49,451 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:49,451 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:37:49,451 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:49,451 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:37:49,452 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:49,452 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:49,452 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,452 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,501 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:49,501 DEBUG: Start:	 Training
+2016-09-06 10:37:49,501 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:49,502 DEBUG: Start:	 Training
+2016-09-06 10:37:49,515 DEBUG: Info:	 Time for Training: 0.0661950111389[s]
+2016-09-06 10:37:49,516 DEBUG: Done:	 Training
+2016-09-06 10:37:49,516 DEBUG: Start:	 Predicting
+2016-09-06 10:37:49,518 DEBUG: Info:	 Time for Training: 0.0691938400269[s]
+2016-09-06 10:37:49,519 DEBUG: Done:	 Training
+2016-09-06 10:37:49,519 DEBUG: Start:	 Predicting
+2016-09-06 10:37:49,520 DEBUG: Done:	 Predicting
+2016-09-06 10:37:49,520 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:49,522 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:49,522 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,522 INFO: Done:	 Result Analysis
+2016-09-06 10:37:49,525 DEBUG: Done:	 Predicting
+2016-09-06 10:37:49,525 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:49,526 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:49,526 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,526 INFO: Done:	 Result Analysis
+2016-09-06 10:37:49,597 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,598 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:37:49,598 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:49,598 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,598 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,598 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:37:49,598 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:49,598 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,599 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:49,599 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,599 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,599 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,599 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:49,599 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,629 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:49,629 DEBUG: Start:	 Training
+2016-09-06 10:37:49,629 DEBUG: Info:	 Time for Training: 0.0316710472107[s]
+2016-09-06 10:37:49,629 DEBUG: Done:	 Training
+2016-09-06 10:37:49,630 DEBUG: Start:	 Predicting
+2016-09-06 10:37:49,632 DEBUG: Done:	 Predicting
+2016-09-06 10:37:49,632 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:49,634 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:49,634 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.742857142857
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.742857142857
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,634 INFO: Done:	 Result Analysis
+2016-09-06 10:37:49,645 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:49,645 DEBUG: Start:	 Training
+2016-09-06 10:37:49,648 DEBUG: Info:	 Time for Training: 0.0510141849518[s]
+2016-09-06 10:37:49,648 DEBUG: Done:	 Training
+2016-09-06 10:37:49,648 DEBUG: Start:	 Predicting
+2016-09-06 10:37:49,651 DEBUG: Done:	 Predicting
+2016-09-06 10:37:49,651 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:49,653 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:49,653 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,653 INFO: Done:	 Result Analysis
+2016-09-06 10:37:49,750 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,750 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,751 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:37:49,751 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:37:49,751 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:49,751 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:49,751 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,751 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,752 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,752 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,752 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:49,752 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:49,752 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,752 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,784 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:49,784 DEBUG: Start:	 Training
+2016-09-06 10:37:49,785 DEBUG: Info:	 Time for Training: 0.0350430011749[s]
+2016-09-06 10:37:49,785 DEBUG: Done:	 Training
+2016-09-06 10:37:49,785 DEBUG: Start:	 Predicting
+2016-09-06 10:37:49,789 DEBUG: Done:	 Predicting
+2016-09-06 10:37:49,789 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:49,790 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:49,791 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.709523809524
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.709523809524
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,791 INFO: Done:	 Result Analysis
+2016-09-06 10:37:49,912 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:49,912 DEBUG: Start:	 Training
+2016-09-06 10:37:49,932 DEBUG: Info:	 Time for Training: 0.182338953018[s]
+2016-09-06 10:37:49,932 DEBUG: Done:	 Training
+2016-09-06 10:37:49,932 DEBUG: Start:	 Predicting
+2016-09-06 10:37:49,936 DEBUG: Done:	 Predicting
+2016-09-06 10:37:49,936 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:49,938 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:49,938 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.8
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 8, max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.8
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:49,938 INFO: Done:	 Result Analysis
+2016-09-06 10:37:49,997 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,997 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:37:49,998 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:37:49,998 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:37:49,998 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:49,998 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:37:49,999 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,999 DEBUG: Info:	 Shape X_train:(210, 5), Length of y_train:210
+2016-09-06 10:37:49,999 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,999 DEBUG: Info:	 Shape X_test:(90, 5), Length of y_test:90
+2016-09-06 10:37:49,999 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:49,999 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:37:49,999 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:49,999 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:37:50,048 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:50,048 DEBUG: Start:	 Training
+2016-09-06 10:37:50,053 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:37:50,053 DEBUG: Start:	 Training
+2016-09-06 10:37:50,053 DEBUG: Info:	 Time for Training: 0.056587934494[s]
+2016-09-06 10:37:50,054 DEBUG: Done:	 Training
+2016-09-06 10:37:50,054 DEBUG: Start:	 Predicting
+2016-09-06 10:37:50,064 DEBUG: Info:	 Time for Training: 0.0674622058868[s]
+2016-09-06 10:37:50,064 DEBUG: Done:	 Training
+2016-09-06 10:37:50,065 DEBUG: Start:	 Predicting
+2016-09-06 10:37:50,068 DEBUG: Done:	 Predicting
+2016-09-06 10:37:50,068 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:50,069 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:50,069 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.466666666667
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:50,069 INFO: Done:	 Result Analysis
+2016-09-06 10:37:50,075 DEBUG: Done:	 Predicting
+2016-09-06 10:37:50,076 DEBUG: Start:	 Getting Results
+2016-09-06 10:37:50,079 DEBUG: Done:	 Getting Results
+2016-09-06 10:37:50,079 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:37:50,079 INFO: Done:	 Result Analysis
+2016-09-06 10:37:50,290 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:37:50,290 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:37:50,291 INFO: Info:	 Shape of View0 :(300, 5)
+2016-09-06 10:37:50,292 INFO: Info:	 Shape of View1 :(300, 12)
+2016-09-06 10:37:50,292 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:37:50,292 INFO: Info:	 Shape of View2 :(300, 14)
+2016-09-06 10:37:50,292 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:37:50,293 INFO: Info:	 Shape of View3 :(300, 5)
+2016-09-06 10:37:50,293 INFO: Done:	 Read Database Files
+2016-09-06 10:37:50,293 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:37:50,294 INFO: Info:	 Shape of View0 :(300, 5)
+2016-09-06 10:37:50,295 INFO: Info:	 Shape of View1 :(300, 12)
+2016-09-06 10:37:50,295 INFO: Info:	 Shape of View2 :(300, 14)
+2016-09-06 10:37:50,296 INFO: Info:	 Shape of View3 :(300, 5)
+2016-09-06 10:37:50,296 INFO: Done:	 Read Database Files
+2016-09-06 10:37:50,296 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:37:50,299 INFO: Done:	 Determine validation split
+2016-09-06 10:37:50,299 INFO: Start:	 Determine 5 folds
+2016-09-06 10:37:50,301 INFO: Done:	 Determine validation split
+2016-09-06 10:37:50,301 INFO: Start:	 Determine 5 folds
+2016-09-06 10:37:50,308 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:37:50,308 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:37:50,308 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:37:50,308 INFO: Done:	 Determine folds
+2016-09-06 10:37:50,308 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:37:50,308 INFO: Start:	 Classification
+2016-09-06 10:37:50,309 INFO: 	Start:	 Fold number 1
+2016-09-06 10:37:50,310 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:37:50,310 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:37:50,310 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:37:50,310 INFO: Done:	 Determine folds
+2016-09-06 10:37:50,310 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:37:50,310 INFO: Start:	 Classification
+2016-09-06 10:37:50,310 INFO: 	Start:	 Fold number 1
+2016-09-06 10:37:50,340 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:37:50,348 DEBUG: 			View 0 : 0.431952662722
+2016-09-06 10:37:50,355 DEBUG: 			View 1 : 0.431952662722
+2016-09-06 10:37:50,363 DEBUG: 			View 2 : 0.431952662722
+2016-09-06 10:37:50,370 DEBUG: 			View 3 : 0.431952662722
+2016-09-06 10:37:50,371 WARNING: 		WARNING:	All bad for iteration 0
+2016-09-06 10:37:50,403 DEBUG: 			 Best view : 		View0
+2016-09-06 10:37:50,482 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:37:50,489 DEBUG: 			View 0 : 0.721893491124
+2016-09-06 10:37:50,497 DEBUG: 			View 1 : 0.710059171598
+2016-09-06 10:37:50,505 DEBUG: 			View 2 : 0.680473372781
+2016-09-06 10:37:50,513 DEBUG: 			View 3 : 0.739644970414
+2016-09-06 10:37:50,551 DEBUG: 			 Best view : 		View3
+2016-09-06 10:37:50,697 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:37:50,712 DEBUG: 			View 0 : 0.721893491124
+2016-09-06 10:37:50,722 DEBUG: 			View 1 : 0.710059171598
+2016-09-06 10:37:50,730 DEBUG: 			View 2 : 0.680473372781
+2016-09-06 10:37:50,737 DEBUG: 			View 3 : 0.739644970414
+2016-09-06 10:37:50,776 DEBUG: 			 Best view : 		View3
+2016-09-06 10:37:50,980 INFO: 	Start: 	 Classification
+2016-09-06 10:37:51,321 INFO: 	Done: 	 Fold number 1
+2016-09-06 10:37:51,321 INFO: 	Start:	 Fold number 2
+2016-09-06 10:37:51,351 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:37:51,358 DEBUG: 			View 0 : 0.431952662722
+2016-09-06 10:37:51,365 DEBUG: 			View 1 : 0.431952662722
+2016-09-06 10:37:51,372 DEBUG: 			View 2 : 0.431952662722
+2016-09-06 10:37:51,380 DEBUG: 			View 3 : 0.431952662722
+2016-09-06 10:37:51,380 WARNING: 		WARNING:	All bad for iteration 0
+2016-09-06 10:37:51,410 DEBUG: 			 Best view : 		View0
+2016-09-06 10:37:51,489 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:37:51,496 DEBUG: 			View 0 : 0.710059171598
+2016-09-06 10:37:51,504 DEBUG: 			View 1 : 0.644970414201
+2016-09-06 10:37:51,511 DEBUG: 			View 2 : 0.721893491124
+2016-09-06 10:37:51,518 DEBUG: 			View 3 : 0.674556213018
+2016-09-06 10:37:51,555 DEBUG: 			 Best view : 		View2
+2016-09-06 10:37:51,694 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:37:51,701 DEBUG: 			View 0 : 0.710059171598
+2016-09-06 10:37:51,709 DEBUG: 			View 1 : 0.644970414201
+2016-09-06 10:37:51,716 DEBUG: 			View 2 : 0.721893491124
+2016-09-06 10:37:51,723 DEBUG: 			View 3 : 0.674556213018
+2016-09-06 10:37:51,762 DEBUG: 			 Best view : 		View2
+2016-09-06 10:37:51,964 INFO: 	Start: 	 Classification
+2016-09-06 10:37:52,305 INFO: 	Done: 	 Fold number 2
+2016-09-06 10:37:52,305 INFO: 	Start:	 Fold number 3
+2016-09-06 10:37:52,333 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:37:52,340 DEBUG: 			View 0 : 0.414201183432
+2016-09-06 10:37:52,347 DEBUG: 			View 1 : 0.467455621302
+2016-09-06 10:37:52,355 DEBUG: 			View 2 : 0.461538461538
+2016-09-06 10:37:52,361 DEBUG: 			View 3 : 0.514792899408
+2016-09-06 10:37:52,391 DEBUG: 			 Best view : 		View2
+2016-09-06 10:37:52,466 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:37:52,473 DEBUG: 			View 0 : 0.710059171598
+2016-09-06 10:37:52,481 DEBUG: 			View 1 : 0.721893491124
+2016-09-06 10:37:52,489 DEBUG: 			View 2 : 0.704142011834
+2016-09-06 10:37:52,495 DEBUG: 			View 3 : 0.733727810651
+2016-09-06 10:37:52,532 DEBUG: 			 Best view : 		View3
+2016-09-06 10:37:52,672 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:37:52,679 DEBUG: 			View 0 : 0.710059171598
+2016-09-06 10:37:52,686 DEBUG: 			View 1 : 0.721893491124
+2016-09-06 10:37:52,694 DEBUG: 			View 2 : 0.704142011834
+2016-09-06 10:37:52,701 DEBUG: 			View 3 : 0.733727810651
+2016-09-06 10:37:52,739 DEBUG: 			 Best view : 		View3
+2016-09-06 10:37:52,941 INFO: 	Start: 	 Classification
+2016-09-06 10:37:53,279 INFO: 	Done: 	 Fold number 3
+2016-09-06 10:37:53,279 INFO: 	Start:	 Fold number 4
+2016-09-06 10:37:53,308 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:37:53,315 DEBUG: 			View 0 : 0.568047337278
+2016-09-06 10:37:53,322 DEBUG: 			View 1 : 0.568047337278
+2016-09-06 10:37:53,328 DEBUG: 			View 2 : 0.568047337278
+2016-09-06 10:37:53,335 DEBUG: 			View 3 : 0.568047337278
+2016-09-06 10:37:53,365 DEBUG: 			 Best view : 		View0
+2016-09-06 10:37:53,439 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:37:53,446 DEBUG: 			View 0 : 0.662721893491
+2016-09-06 10:37:53,454 DEBUG: 			View 1 : 0.715976331361
+2016-09-06 10:37:53,461 DEBUG: 			View 2 : 0.739644970414
+2016-09-06 10:37:53,468 DEBUG: 			View 3 : 0.692307692308
+2016-09-06 10:37:53,504 DEBUG: 			 Best view : 		View2
+2016-09-06 10:37:53,643 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:37:53,650 DEBUG: 			View 0 : 0.662721893491
+2016-09-06 10:37:53,658 DEBUG: 			View 1 : 0.715976331361
+2016-09-06 10:37:53,665 DEBUG: 			View 2 : 0.739644970414
+2016-09-06 10:37:53,673 DEBUG: 			View 3 : 0.692307692308
+2016-09-06 10:37:53,711 DEBUG: 			 Best view : 		View2
+2016-09-06 10:37:53,914 INFO: 	Start: 	 Classification
+2016-09-06 10:37:54,254 INFO: 	Done: 	 Fold number 4
+2016-09-06 10:37:54,254 INFO: 	Start:	 Fold number 5
+2016-09-06 10:37:54,282 DEBUG: 		Start:	 Iteration 1
+2016-09-06 10:37:54,289 DEBUG: 			View 0 : 0.431952662722
+2016-09-06 10:37:54,296 DEBUG: 			View 1 : 0.431952662722
+2016-09-06 10:37:54,303 DEBUG: 			View 2 : 0.431952662722
+2016-09-06 10:37:54,309 DEBUG: 			View 3 : 0.431952662722
+2016-09-06 10:37:54,310 WARNING: 		WARNING:	All bad for iteration 0
+2016-09-06 10:37:54,340 DEBUG: 			 Best view : 		View0
+2016-09-06 10:37:54,415 DEBUG: 		Start:	 Iteration 2
+2016-09-06 10:37:54,423 DEBUG: 			View 0 : 0.656804733728
+2016-09-06 10:37:54,430 DEBUG: 			View 1 : 0.733727810651
+2016-09-06 10:37:54,437 DEBUG: 			View 2 : 0.692307692308
+2016-09-06 10:37:54,444 DEBUG: 			View 3 : 0.710059171598
+2016-09-06 10:37:54,480 DEBUG: 			 Best view : 		View1
+2016-09-06 10:37:54,620 DEBUG: 		Start:	 Iteration 3
+2016-09-06 10:37:54,627 DEBUG: 			View 0 : 0.656804733728
+2016-09-06 10:37:54,634 DEBUG: 			View 1 : 0.733727810651
+2016-09-06 10:37:54,642 DEBUG: 			View 2 : 0.692307692308
+2016-09-06 10:37:54,649 DEBUG: 			View 3 : 0.710059171598
+2016-09-06 10:37:54,688 DEBUG: 			 Best view : 		View1
+2016-09-06 10:37:54,891 INFO: 	Start: 	 Classification
+2016-09-06 10:37:55,231 INFO: 	Done: 	 Fold number 5
+2016-09-06 10:37:55,231 INFO: Done:	 Classification
+2016-09-06 10:37:55,231 INFO: Info:	 Time for Classification: 4[s]
+2016-09-06 10:37:55,231 INFO: Start:	 Result Analysis for Mumbo
+2016-09-06 10:37:57,067 INFO: 		Result for Multiview classification with Mumbo
+
+Average accuracy :
+	-On Train : 73.2544378698
+	-On Test : 46.1904761905
+	-On Validation : 57.0786516854Dataset info :
+	-Dataset name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : View0 of shape (300, 5), View1 of shape (300, 12), View2 of shape (300, 14), View3 of shape (300, 5)
+	-5 folds
+	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration : 
+	-Used 1 core(s)
+	-Iterations : min 1, max 10, threshold 0.01
+	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:00        0:00:00
+	         Fold 2        0:00:01        0:00:00
+	         Fold 3        0:00:02        0:00:00
+	         Fold 4        0:00:03        0:00:00
+	         Fold 5        0:00:04        0:00:00
+	          Total        0:00:13        0:00:01
+	So a total classification time of 0:00:04.
+
+
+
+Mean average accuracies and stats for each fold :
+	- Fold 0, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.187573964497
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.185207100592
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.179289940828
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.191124260355
+			- Percentage of time chosen : 0.2
+	- Fold 1, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.185207100592
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.172189349112
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.187573964497
+			- Percentage of time chosen : 0.2
+		- On View3 : 
+			- Mean average Accuracy : 0.178106508876
+			- Percentage of time chosen : 0.0
+	- Fold 2, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.183431952663
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.191124260355
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.186982248521
+			- Percentage of time chosen : 0.1
+		- On View3 : 
+			- Mean average Accuracy : 0.198224852071
+			- Percentage of time chosen : 0.2
+	- Fold 3, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.189349112426
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.2
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.204733727811
+			- Percentage of time chosen : 0.2
+		- On View3 : 
+			- Mean average Accuracy : 0.195266272189
+			- Percentage of time chosen : 0.0
+	- Fold 4, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.174556213018
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.189940828402
+			- Percentage of time chosen : 0.2
+		- On View2 : 
+			- Mean average Accuracy : 0.181656804734
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.185207100592
+			- Percentage of time chosen : 0.0
+
+ For each iteration : 
+	- Iteration 1
+		 Fold 1
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View2
+		 Fold 4
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+	- Iteration 2
+		 Fold 1
+			Accuracy on train : 73.9644970414
+			Accuracy on test : 0.0
+			Accuracy on validation : 66.2921348315
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 72.1893491124
+			Accuracy on test : 0.0
+			Accuracy on validation : 48.3146067416
+			Selected View : View2
+		 Fold 3
+			Accuracy on train : 73.3727810651
+			Accuracy on test : 0.0
+			Accuracy on validation : 58.4269662921
+			Selected View : View3
+		 Fold 4
+			Accuracy on train : 73.9644970414
+			Accuracy on test : 0.0
+			Accuracy on validation : 64.0449438202
+			Selected View : View2
+		 Fold 5
+			Accuracy on train : 73.3727810651
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.6853932584
+			Selected View : View1
+	- Iteration 3
+		 Fold 1
+			Accuracy on train : 73.9644970414
+			Accuracy on test : 0.0
+			Accuracy on validation : 66.2921348315
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 72.1893491124
+			Accuracy on test : 0.0
+			Accuracy on validation : 48.3146067416
+			Selected View : View2
+		 Fold 3
+			Accuracy on train : 73.3727810651
+			Accuracy on test : 0.0
+			Accuracy on validation : 58.4269662921
+			Selected View : View3
+		 Fold 4
+			Accuracy on train : 73.3727810651
+			Accuracy on test : 0.0
+			Accuracy on validation : 60.6741573034
+			Selected View : View2
+		 Fold 5
+			Accuracy on train : 73.3727810651
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.6853932584
+			Selected View : View1
+	- Iteration 4
+		 Fold 1
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+2016-09-06 10:37:57,248 INFO: Done:	 Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e15b39c3e5f5cea3fdd23289b5949f993a7578d8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d6b6f1ee26e3a862dded10980056d8ca2df2a65e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.738095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.738095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8033aa1cce1ff5e96eb58b835345d60f300cf98b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.67619047619
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..594945a82902d634a05b6837a381fe4c197cb3e7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.766666666667
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 8, max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.766666666667
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..63c6ae28890236e93e07342595d2185183d08db2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.622222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b456925285c8b84a6ae39dcb7d56c87c5972ceb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..00b45eb3b41d45af390a9e2988b9060090462823
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 0.533333333333
+accuracy_score on test : 0.366666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.533333333333
+		- Score on test : 0.366666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c2d3d627eb0ff0c9f95578b2f1e2f7f3fecfda2b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d49ccc5793b3cd8fba676af3d5aa10f92ca133b8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..96bde0ecd79984fedd5c0621e5bb9c1a1477800f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.690476190476
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.690476190476
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..40e8b33c4b9931566d7f7b265aac1a52fe779064
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.690476190476
+accuracy_score on test : 0.6
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.690476190476
+		- Score on test : 0.6
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..61507bca2f7b4fe9c922d85d643d4cd04e66421b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.833333333333
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 8, max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.833333333333
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f8a94e87a80edb940442bcb95de89bbd46affe90
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8ebd07f45ad022d999bcaab360e9c042b906914d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.604761904762
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cb5ad814a02065b63528f5bf97a304d7c969f422
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e113ccefa2a0093fce2acd39d7a50b70b7c50866
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 12)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d2a1e68014aae7bbae32ed22b874c1ebd6efadae
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d8ec68e9648744b82100ecf7fa6b227e72e3e4cf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.742857142857
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.742857142857
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a85fe8bb54b716f7c83e44ceec69d452d98206b1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.709523809524
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.709523809524
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bd9b6ea7d01b48136ba43d2beb4aaa3686aa4f3b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 0.8
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 8, max_depth : 4
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.8
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0baeab0532f326a580ca28ca5aa8eff0d09cd94c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.604761904762
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.604761904762
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5d435b5632b7c130f5124f2a40617a4872cf4654
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.580952380952
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.580952380952
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..78a5329c012d50a70ad3fb0e480a3d816fab08bf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8f3e16aec05d7d33b3e4961f9de8fb400216927b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c6c250a5484a573060809e38fb1ce64d09cca425
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.588888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : modified_huber, penalty : elasticnet
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.588888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..367caf755d6753a27251dc8eba5d858bdff74b60
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.466666666667
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 5)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 4648
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.466666666667
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
new file mode 100644
index 0000000000000000000000000000000000000000..f5bbc8bd0c286eee40e266058650c7eda94a4b4b
Binary files /dev/null and b/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..65472fd456b000232b9ed53401823cf19a997e61
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
@@ -0,0 +1,209 @@
+		Result for Multiview classification with Mumbo
+
+Average accuracy :
+	-On Train : 73.2544378698
+	-On Test : 46.1904761905
+	-On Validation : 57.0786516854Dataset info :
+	-Dataset name : Fake
+	-Labels : Methyl, MiRNA_, RNASeq, Clinic
+	-Views : View0 of shape (300, 5), View1 of shape (300, 12), View2 of shape (300, 14), View3 of shape (300, 5)
+	-5 folds
+	- Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
+
+
+
+Mumbo configuration : 
+	-Used 1 core(s)
+	-Iterations : min 1, max 10, threshold 0.01
+	-Weak Classifiers : DecisionTree with depth 3,  sub-sampled at 1.0 on View0
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View1
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View2
+		-DecisionTree with depth 3,  sub-sampled at 1.0 on View3
+
+
+
+Computation time on 1 cores : 
+	Database extraction time : 0:00:00
+	                         Learn     Prediction
+	         Fold 1        0:00:00        0:00:00
+	         Fold 2        0:00:01        0:00:00
+	         Fold 3        0:00:02        0:00:00
+	         Fold 4        0:00:03        0:00:00
+	         Fold 5        0:00:04        0:00:00
+	          Total        0:00:13        0:00:01
+	So a total classification time of 0:00:04.
+
+
+
+Mean average accuracies and stats for each fold :
+	- Fold 0, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.187573964497
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.185207100592
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.179289940828
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.191124260355
+			- Percentage of time chosen : 0.2
+	- Fold 1, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.185207100592
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.172189349112
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.187573964497
+			- Percentage of time chosen : 0.2
+		- On View3 : 
+			- Mean average Accuracy : 0.178106508876
+			- Percentage of time chosen : 0.0
+	- Fold 2, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.183431952663
+			- Percentage of time chosen : 0.7
+		- On View1 : 
+			- Mean average Accuracy : 0.191124260355
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.186982248521
+			- Percentage of time chosen : 0.1
+		- On View3 : 
+			- Mean average Accuracy : 0.198224852071
+			- Percentage of time chosen : 0.2
+	- Fold 3, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.189349112426
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.2
+			- Percentage of time chosen : 0.0
+		- On View2 : 
+			- Mean average Accuracy : 0.204733727811
+			- Percentage of time chosen : 0.2
+		- On View3 : 
+			- Mean average Accuracy : 0.195266272189
+			- Percentage of time chosen : 0.0
+	- Fold 4, used 4
+		- On View0 : 
+			- Mean average Accuracy : 0.174556213018
+			- Percentage of time chosen : 0.8
+		- On View1 : 
+			- Mean average Accuracy : 0.189940828402
+			- Percentage of time chosen : 0.2
+		- On View2 : 
+			- Mean average Accuracy : 0.181656804734
+			- Percentage of time chosen : 0.0
+		- On View3 : 
+			- Mean average Accuracy : 0.185207100592
+			- Percentage of time chosen : 0.0
+
+ For each iteration : 
+	- Iteration 1
+		 Fold 1
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View2
+		 Fold 4
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+	- Iteration 2
+		 Fold 1
+			Accuracy on train : 73.9644970414
+			Accuracy on test : 0.0
+			Accuracy on validation : 66.2921348315
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 72.1893491124
+			Accuracy on test : 0.0
+			Accuracy on validation : 48.3146067416
+			Selected View : View2
+		 Fold 3
+			Accuracy on train : 73.3727810651
+			Accuracy on test : 0.0
+			Accuracy on validation : 58.4269662921
+			Selected View : View3
+		 Fold 4
+			Accuracy on train : 73.9644970414
+			Accuracy on test : 0.0
+			Accuracy on validation : 64.0449438202
+			Selected View : View2
+		 Fold 5
+			Accuracy on train : 73.3727810651
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.6853932584
+			Selected View : View1
+	- Iteration 3
+		 Fold 1
+			Accuracy on train : 73.9644970414
+			Accuracy on test : 0.0
+			Accuracy on validation : 66.2921348315
+			Selected View : View3
+		 Fold 2
+			Accuracy on train : 72.1893491124
+			Accuracy on test : 0.0
+			Accuracy on validation : 48.3146067416
+			Selected View : View2
+		 Fold 3
+			Accuracy on train : 73.3727810651
+			Accuracy on test : 0.0
+			Accuracy on validation : 58.4269662921
+			Selected View : View3
+		 Fold 4
+			Accuracy on train : 73.3727810651
+			Accuracy on test : 0.0
+			Accuracy on validation : 60.6741573034
+			Selected View : View2
+		 Fold 5
+			Accuracy on train : 73.3727810651
+			Accuracy on test : 0.0
+			Accuracy on validation : 51.6853932584
+			Selected View : View1
+	- Iteration 4
+		 Fold 1
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 2
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 3
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 4
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
+		 Fold 5
+			Accuracy on train : 56.8047337278
+			Accuracy on test : 0.0
+			Accuracy on validation : 57.3033707865
+			Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..bc6f192d94981023cc7955562900b39081a68607
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1250 @@
+2016-09-06 10:39:29,831 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:39:29,831 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000170875 Gbytes /!\ 
+2016-09-06 10:39:34,843 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:39:34,844 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:39:34,896 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:34,896 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:39:34,896 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:34,897 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:34,897 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:39:34,897 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:34,897 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:34,898 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:34,898 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:34,898 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:34,898 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:34,898 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:34,899 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:34,899 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:34,942 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:34,943 DEBUG: Start:	 Training
+2016-09-06 10:39:34,946 DEBUG: Info:	 Time for Training: 0.0496671199799[s]
+2016-09-06 10:39:34,946 DEBUG: Done:	 Training
+2016-09-06 10:39:34,946 DEBUG: Start:	 Predicting
+2016-09-06 10:39:34,948 DEBUG: Done:	 Predicting
+2016-09-06 10:39:34,949 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:34,950 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:34,950 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:34,950 INFO: Done:	 Result Analysis
+2016-09-06 10:39:34,952 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:34,952 DEBUG: Start:	 Training
+2016-09-06 10:39:34,957 DEBUG: Info:	 Time for Training: 0.0618000030518[s]
+2016-09-06 10:39:34,957 DEBUG: Done:	 Training
+2016-09-06 10:39:34,957 DEBUG: Start:	 Predicting
+2016-09-06 10:39:34,960 DEBUG: Done:	 Predicting
+2016-09-06 10:39:34,961 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:34,962 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:34,963 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:34,963 INFO: Done:	 Result Analysis
+2016-09-06 10:39:35,046 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,046 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,046 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:39:35,046 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:39:35,046 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:35,046 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:35,047 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,047 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,047 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,047 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,047 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:35,047 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:35,047 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,047 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,081 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:35,081 DEBUG: Start:	 Training
+2016-09-06 10:39:35,081 DEBUG: Info:	 Time for Training: 0.0358710289001[s]
+2016-09-06 10:39:35,081 DEBUG: Done:	 Training
+2016-09-06 10:39:35,081 DEBUG: Start:	 Predicting
+2016-09-06 10:39:35,089 DEBUG: Done:	 Predicting
+2016-09-06 10:39:35,089 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:35,091 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:35,091 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 43
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,091 INFO: Done:	 Result Analysis
+2016-09-06 10:39:35,328 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:35,328 DEBUG: Start:	 Training
+2016-09-06 10:39:35,368 DEBUG: Info:	 Time for Training: 0.322957992554[s]
+2016-09-06 10:39:35,369 DEBUG: Done:	 Training
+2016-09-06 10:39:35,369 DEBUG: Start:	 Predicting
+2016-09-06 10:39:35,374 DEBUG: Done:	 Predicting
+2016-09-06 10:39:35,374 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:35,376 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:35,376 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 15, max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.990476190476
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,376 INFO: Done:	 Result Analysis
+2016-09-06 10:39:35,496 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,496 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,496 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:39:35,496 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:39:35,497 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:35,497 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:35,498 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,498 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,498 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,498 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,498 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:35,498 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:35,498 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,498 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,546 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:35,546 DEBUG: Start:	 Training
+2016-09-06 10:39:35,547 DEBUG: Info:	 Time for Training: 0.0519301891327[s]
+2016-09-06 10:39:35,547 DEBUG: Done:	 Training
+2016-09-06 10:39:35,547 DEBUG: Start:	 Predicting
+2016-09-06 10:39:35,553 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:35,554 DEBUG: Start:	 Training
+2016-09-06 10:39:35,565 DEBUG: Done:	 Predicting
+2016-09-06 10:39:35,565 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:35,567 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:35,567 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,567 INFO: Done:	 Result Analysis
+2016-09-06 10:39:35,575 DEBUG: Info:	 Time for Training: 0.0798330307007[s]
+2016-09-06 10:39:35,575 DEBUG: Done:	 Training
+2016-09-06 10:39:35,575 DEBUG: Start:	 Predicting
+2016-09-06 10:39:35,579 DEBUG: Done:	 Predicting
+2016-09-06 10:39:35,579 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:35,581 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:35,581 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,581 INFO: Done:	 Result Analysis
+2016-09-06 10:39:35,643 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,643 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,644 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:39:35,644 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:39:35,644 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:35,644 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:35,645 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,645 DEBUG: Info:	 Shape X_train:(210, 18), Length of y_train:210
+2016-09-06 10:39:35,645 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,646 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:35,646 DEBUG: Info:	 Shape X_test:(90, 18), Length of y_test:90
+2016-09-06 10:39:35,646 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,646 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:35,646 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,698 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:35,699 DEBUG: Start:	 Training
+2016-09-06 10:39:35,701 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:35,701 DEBUG: Start:	 Training
+2016-09-06 10:39:35,716 DEBUG: Info:	 Time for Training: 0.0735998153687[s]
+2016-09-06 10:39:35,717 DEBUG: Done:	 Training
+2016-09-06 10:39:35,717 DEBUG: Start:	 Predicting
+2016-09-06 10:39:35,720 DEBUG: Info:	 Time for Training: 0.0770709514618[s]
+2016-09-06 10:39:35,720 DEBUG: Done:	 Training
+2016-09-06 10:39:35,720 DEBUG: Start:	 Predicting
+2016-09-06 10:39:35,723 DEBUG: Done:	 Predicting
+2016-09-06 10:39:35,723 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:35,724 DEBUG: Done:	 Predicting
+2016-09-06 10:39:35,724 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:35,724 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,724 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:35,724 INFO: Done:	 Result Analysis
+2016-09-06 10:39:35,726 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:35,726 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,726 INFO: Done:	 Result Analysis
+2016-09-06 10:39:35,791 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,791 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,791 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:39:35,791 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:39:35,791 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:35,791 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:35,792 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:35,792 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:35,792 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:35,792 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:35,792 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:35,792 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:35,792 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,792 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,826 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:35,827 DEBUG: Start:	 Training
+2016-09-06 10:39:35,828 DEBUG: Info:	 Time for Training: 0.0375211238861[s]
+2016-09-06 10:39:35,828 DEBUG: Done:	 Training
+2016-09-06 10:39:35,828 DEBUG: Start:	 Predicting
+2016-09-06 10:39:35,831 DEBUG: Done:	 Predicting
+2016-09-06 10:39:35,831 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:35,832 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:35,832 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,833 INFO: Done:	 Result Analysis
+2016-09-06 10:39:35,840 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:35,840 DEBUG: Start:	 Training
+2016-09-06 10:39:35,843 DEBUG: Info:	 Time for Training: 0.0527670383453[s]
+2016-09-06 10:39:35,843 DEBUG: Done:	 Training
+2016-09-06 10:39:35,843 DEBUG: Start:	 Predicting
+2016-09-06 10:39:35,846 DEBUG: Done:	 Predicting
+2016-09-06 10:39:35,846 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:35,848 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:35,848 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:35,848 INFO: Done:	 Result Analysis
+2016-09-06 10:39:35,945 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,945 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:35,945 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:39:35,945 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:39:35,945 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:35,945 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:35,946 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:35,946 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:35,947 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:35,947 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:35,947 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:35,947 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:35,947 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,947 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:35,998 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:35,998 DEBUG: Start:	 Training
+2016-09-06 10:39:35,999 DEBUG: Info:	 Time for Training: 0.0549540519714[s]
+2016-09-06 10:39:35,999 DEBUG: Done:	 Training
+2016-09-06 10:39:35,999 DEBUG: Start:	 Predicting
+2016-09-06 10:39:36,008 DEBUG: Done:	 Predicting
+2016-09-06 10:39:36,008 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:36,011 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:36,011 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.528571428571
+accuracy_score on test : 0.566666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 43
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.528571428571
+		- Score on test : 0.566666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,011 INFO: Done:	 Result Analysis
+2016-09-06 10:39:36,240 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:36,240 DEBUG: Start:	 Training
+2016-09-06 10:39:36,279 DEBUG: Info:	 Time for Training: 0.334888935089[s]
+2016-09-06 10:39:36,279 DEBUG: Done:	 Training
+2016-09-06 10:39:36,279 DEBUG: Start:	 Predicting
+2016-09-06 10:39:36,284 DEBUG: Done:	 Predicting
+2016-09-06 10:39:36,285 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:36,286 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:36,286 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 15, max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,286 INFO: Done:	 Result Analysis
+2016-09-06 10:39:36,394 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,394 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,394 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:39:36,394 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:39:36,394 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:36,394 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:36,395 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:36,395 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:36,395 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:36,395 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:36,395 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:36,395 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,395 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:36,396 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,441 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:36,441 DEBUG: Start:	 Training
+2016-09-06 10:39:36,442 DEBUG: Info:	 Time for Training: 0.0489950180054[s]
+2016-09-06 10:39:36,442 DEBUG: Done:	 Training
+2016-09-06 10:39:36,442 DEBUG: Start:	 Predicting
+2016-09-06 10:39:36,445 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:36,445 DEBUG: Start:	 Training
+2016-09-06 10:39:36,463 DEBUG: Info:	 Time for Training: 0.0693230628967[s]
+2016-09-06 10:39:36,463 DEBUG: Done:	 Training
+2016-09-06 10:39:36,463 DEBUG: Start:	 Predicting
+2016-09-06 10:39:36,466 DEBUG: Done:	 Predicting
+2016-09-06 10:39:36,466 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:36,467 DEBUG: Done:	 Predicting
+2016-09-06 10:39:36,467 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:36,468 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:36,469 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.504761904762
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.504761904762
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,469 INFO: Done:	 Result Analysis
+2016-09-06 10:39:36,469 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:36,469 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.377777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.485714285714
+		- Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,470 INFO: Done:	 Result Analysis
+2016-09-06 10:39:36,547 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,547 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:39:36,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:39:36,548 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:36,548 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:36,550 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:36,550 DEBUG: Info:	 Shape X_train:(210, 8), Length of y_train:210
+2016-09-06 10:39:36,550 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:36,550 DEBUG: Info:	 Shape X_test:(90, 8), Length of y_test:90
+2016-09-06 10:39:36,550 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:36,550 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:36,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,550 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,603 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:36,603 DEBUG: Start:	 Training
+2016-09-06 10:39:36,613 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:36,614 DEBUG: Start:	 Training
+2016-09-06 10:39:36,619 DEBUG: Info:	 Time for Training: 0.0724880695343[s]
+2016-09-06 10:39:36,619 DEBUG: Done:	 Training
+2016-09-06 10:39:36,619 DEBUG: Start:	 Predicting
+2016-09-06 10:39:36,624 DEBUG: Done:	 Predicting
+2016-09-06 10:39:36,624 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:36,626 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:36,626 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,626 INFO: Done:	 Result Analysis
+2016-09-06 10:39:36,631 DEBUG: Info:	 Time for Training: 0.0851111412048[s]
+2016-09-06 10:39:36,631 DEBUG: Done:	 Training
+2016-09-06 10:39:36,632 DEBUG: Start:	 Predicting
+2016-09-06 10:39:36,635 DEBUG: Done:	 Predicting
+2016-09-06 10:39:36,635 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:36,637 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:36,637 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,638 INFO: Done:	 Result Analysis
+2016-09-06 10:39:36,795 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,795 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:39:36,795 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:36,796 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,796 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:36,797 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:39:36,797 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:36,797 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:36,797 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:36,797 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,798 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:36,798 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:36,798 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:36,798 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,840 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:36,840 DEBUG: Start:	 Training
+2016-09-06 10:39:36,844 DEBUG: Info:	 Time for Training: 0.0481259822845[s]
+2016-09-06 10:39:36,844 DEBUG: Done:	 Training
+2016-09-06 10:39:36,844 DEBUG: Start:	 Predicting
+2016-09-06 10:39:36,846 DEBUG: Done:	 Predicting
+2016-09-06 10:39:36,847 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:36,848 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:36,848 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,848 INFO: Done:	 Result Analysis
+2016-09-06 10:39:36,851 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:36,851 DEBUG: Start:	 Training
+2016-09-06 10:39:36,856 DEBUG: Info:	 Time for Training: 0.0621771812439[s]
+2016-09-06 10:39:36,856 DEBUG: Done:	 Training
+2016-09-06 10:39:36,856 DEBUG: Start:	 Predicting
+2016-09-06 10:39:36,859 DEBUG: Done:	 Predicting
+2016-09-06 10:39:36,860 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:36,862 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:36,862 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,862 INFO: Done:	 Result Analysis
+2016-09-06 10:39:36,939 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,939 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:36,940 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:39:36,940 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:39:36,940 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:36,940 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:36,940 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:36,940 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:36,940 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:36,940 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:36,940 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:36,940 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:36,941 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,941 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:36,974 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:36,974 DEBUG: Start:	 Training
+2016-09-06 10:39:36,974 DEBUG: Info:	 Time for Training: 0.0355970859528[s]
+2016-09-06 10:39:36,975 DEBUG: Done:	 Training
+2016-09-06 10:39:36,975 DEBUG: Start:	 Predicting
+2016-09-06 10:39:36,983 DEBUG: Done:	 Predicting
+2016-09-06 10:39:36,983 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:36,984 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:36,984 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 43
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.561904761905
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:36,984 INFO: Done:	 Result Analysis
+2016-09-06 10:39:37,218 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:37,219 DEBUG: Start:	 Training
+2016-09-06 10:39:37,260 DEBUG: Info:	 Time for Training: 0.320749998093[s]
+2016-09-06 10:39:37,260 DEBUG: Done:	 Training
+2016-09-06 10:39:37,260 DEBUG: Start:	 Predicting
+2016-09-06 10:39:37,265 DEBUG: Done:	 Predicting
+2016-09-06 10:39:37,265 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:37,267 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:37,267 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 15, max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,267 INFO: Done:	 Result Analysis
+2016-09-06 10:39:37,387 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,387 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,387 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:39:37,387 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:39:37,387 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:37,387 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:37,388 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:37,388 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:37,388 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:37,388 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:37,388 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:37,388 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:37,388 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,388 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,439 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:37,439 DEBUG: Start:	 Training
+2016-09-06 10:39:37,440 DEBUG: Info:	 Time for Training: 0.0537350177765[s]
+2016-09-06 10:39:37,440 DEBUG: Done:	 Training
+2016-09-06 10:39:37,440 DEBUG: Start:	 Predicting
+2016-09-06 10:39:37,440 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:37,440 DEBUG: Start:	 Training
+2016-09-06 10:39:37,460 DEBUG: Done:	 Predicting
+2016-09-06 10:39:37,461 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:37,464 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:37,464 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,465 INFO: Done:	 Result Analysis
+2016-09-06 10:39:37,469 DEBUG: Info:	 Time for Training: 0.082640171051[s]
+2016-09-06 10:39:37,469 DEBUG: Done:	 Training
+2016-09-06 10:39:37,469 DEBUG: Start:	 Predicting
+2016-09-06 10:39:37,473 DEBUG: Done:	 Predicting
+2016-09-06 10:39:37,473 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:37,474 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:37,474 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,475 INFO: Done:	 Result Analysis
+2016-09-06 10:39:37,532 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,532 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,532 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:39:37,532 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:39:37,532 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:37,532 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:37,533 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:37,533 DEBUG: Info:	 Shape X_train:(210, 19), Length of y_train:210
+2016-09-06 10:39:37,533 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:37,533 DEBUG: Info:	 Shape X_test:(90, 19), Length of y_test:90
+2016-09-06 10:39:37,533 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:37,533 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:37,533 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,533 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,582 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:37,582 DEBUG: Start:	 Training
+2016-09-06 10:39:37,587 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:37,588 DEBUG: Start:	 Training
+2016-09-06 10:39:37,601 DEBUG: Info:	 Time for Training: 0.0697820186615[s]
+2016-09-06 10:39:37,601 DEBUG: Done:	 Training
+2016-09-06 10:39:37,601 DEBUG: Start:	 Predicting
+2016-09-06 10:39:37,607 DEBUG: Info:	 Time for Training: 0.0757231712341[s]
+2016-09-06 10:39:37,607 DEBUG: Done:	 Training
+2016-09-06 10:39:37,607 DEBUG: Start:	 Predicting
+2016-09-06 10:39:37,607 DEBUG: Done:	 Predicting
+2016-09-06 10:39:37,607 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:37,609 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:37,609 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,609 INFO: Done:	 Result Analysis
+2016-09-06 10:39:37,611 DEBUG: Done:	 Predicting
+2016-09-06 10:39:37,611 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:37,613 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:37,613 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,613 INFO: Done:	 Result Analysis
+2016-09-06 10:39:37,681 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,681 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,681 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:39:37,681 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:39:37,682 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:37,682 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:37,682 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:37,682 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:37,683 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:37,683 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:37,683 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:37,683 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,683 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:37,683 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,723 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:37,723 DEBUG: Start:	 Training
+2016-09-06 10:39:37,725 DEBUG: Info:	 Time for Training: 0.0450918674469[s]
+2016-09-06 10:39:37,726 DEBUG: Done:	 Training
+2016-09-06 10:39:37,726 DEBUG: Start:	 Predicting
+2016-09-06 10:39:37,728 DEBUG: Done:	 Predicting
+2016-09-06 10:39:37,728 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:37,730 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:37,730 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,730 INFO: Done:	 Result Analysis
+2016-09-06 10:39:37,739 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:37,739 DEBUG: Start:	 Training
+2016-09-06 10:39:37,743 DEBUG: Info:	 Time for Training: 0.0631110668182[s]
+2016-09-06 10:39:37,744 DEBUG: Done:	 Training
+2016-09-06 10:39:37,744 DEBUG: Start:	 Predicting
+2016-09-06 10:39:37,746 DEBUG: Done:	 Predicting
+2016-09-06 10:39:37,747 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:37,748 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:37,749 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,749 INFO: Done:	 Result Analysis
+2016-09-06 10:39:37,835 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,835 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:37,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:39:37,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:39:37,836 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:37,836 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:37,836 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:37,837 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:37,837 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:37,837 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:37,837 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,837 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:37,837 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:37,837 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:37,875 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:37,875 DEBUG: Start:	 Training
+2016-09-06 10:39:37,876 DEBUG: Info:	 Time for Training: 0.0416429042816[s]
+2016-09-06 10:39:37,876 DEBUG: Done:	 Training
+2016-09-06 10:39:37,877 DEBUG: Start:	 Predicting
+2016-09-06 10:39:37,885 DEBUG: Done:	 Predicting
+2016-09-06 10:39:37,886 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:37,887 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:37,887 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 43
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.52380952381
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:37,887 INFO: Done:	 Result Analysis
+2016-09-06 10:39:38,178 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:38,178 DEBUG: Start:	 Training
+2016-09-06 10:39:38,218 DEBUG: Info:	 Time for Training: 0.383457183838[s]
+2016-09-06 10:39:38,218 DEBUG: Done:	 Training
+2016-09-06 10:39:38,218 DEBUG: Start:	 Predicting
+2016-09-06 10:39:38,224 DEBUG: Done:	 Predicting
+2016-09-06 10:39:38,224 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:38,225 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:38,226 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 15, max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:38,226 INFO: Done:	 Result Analysis
+2016-09-06 10:39:38,289 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:38,289 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:39:38,289 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:39:38,289 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:39:38,289 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:38,289 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:39:38,290 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:38,290 DEBUG: Info:	 Shape X_train:(210, 20), Length of y_train:210
+2016-09-06 10:39:38,291 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:38,291 DEBUG: Info:	 Shape X_test:(90, 20), Length of y_test:90
+2016-09-06 10:39:38,291 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:38,291 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:39:38,291 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:38,291 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:39:38,363 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:38,363 DEBUG: Start:	 Training
+2016-09-06 10:39:38,365 DEBUG: Info:	 Time for Training: 0.076936006546[s]
+2016-09-06 10:39:38,365 DEBUG: Done:	 Training
+2016-09-06 10:39:38,365 DEBUG: Start:	 Predicting
+2016-09-06 10:39:38,371 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:39:38,371 DEBUG: Start:	 Training
+2016-09-06 10:39:38,377 DEBUG: Done:	 Predicting
+2016-09-06 10:39:38,378 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:38,380 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:38,380 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:38,380 INFO: Done:	 Result Analysis
+2016-09-06 10:39:38,396 DEBUG: Info:	 Time for Training: 0.108675003052[s]
+2016-09-06 10:39:38,397 DEBUG: Done:	 Training
+2016-09-06 10:39:38,397 DEBUG: Start:	 Predicting
+2016-09-06 10:39:38,400 DEBUG: Done:	 Predicting
+2016-09-06 10:39:38,401 DEBUG: Start:	 Getting Results
+2016-09-06 10:39:38,402 DEBUG: Done:	 Getting Results
+2016-09-06 10:39:38,402 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.409523809524
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.409523809524
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:39:38,402 INFO: Done:	 Result Analysis
+2016-09-06 10:39:38,680 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:39:38,680 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:39:38,681 INFO: Info:	 Shape of View0 :(300, 18)
+2016-09-06 10:39:38,681 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:39:38,682 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:39:38,682 INFO: Info:	 Shape of View1 :(300, 8)
+2016-09-06 10:39:38,683 INFO: Info:	 Shape of View0 :(300, 18)
+2016-09-06 10:39:38,683 INFO: Info:	 Shape of View2 :(300, 19)
+2016-09-06 10:39:38,684 INFO: Info:	 Shape of View1 :(300, 8)
+2016-09-06 10:39:38,684 INFO: Info:	 Shape of View3 :(300, 20)
+2016-09-06 10:39:38,684 INFO: Done:	 Read Database Files
+2016-09-06 10:39:38,684 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:39:38,684 INFO: Info:	 Shape of View2 :(300, 19)
+2016-09-06 10:39:38,685 INFO: Info:	 Shape of View3 :(300, 20)
+2016-09-06 10:39:38,685 INFO: Done:	 Read Database Files
+2016-09-06 10:39:38,685 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:39:38,691 INFO: Done:	 Determine validation split
+2016-09-06 10:39:38,691 INFO: Start:	 Determine 5 folds
+2016-09-06 10:39:38,691 INFO: Done:	 Determine validation split
+2016-09-06 10:39:38,692 INFO: Start:	 Determine 5 folds
+2016-09-06 10:39:38,707 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:39:38,707 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:39:38,707 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:39:38,707 INFO: Info:	 Length of Learning Sets: 169
+2016-09-06 10:39:38,707 INFO: Done:	 Determine folds
+2016-09-06 10:39:38,707 INFO: Info:	 Length of Testing Sets: 42
+2016-09-06 10:39:38,707 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:39:38,707 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:39:38,708 INFO: Done:	 Determine folds
+2016-09-06 10:39:38,707 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 10:39:38,708 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:39:38,708 DEBUG: 	Start:	 Random search for Adaboost with 30 iterations
+2016-09-06 10:39:38,708 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 10:39:38,708 DEBUG: 	Start:	 Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7c2f9b7f068dc41865f952bd22adf036738981eb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..225bfa229cbfdec4a1a9eb45196312ca594631fa
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ce956518238614b7bdebdc2ea0858a235eba8d6c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e4d36bfb3c501b5adc716ae51fd80d555ceb199e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9d6ed1f4dc03c718e10b09c65b43943246e85fff
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.571428571429
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 43
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.571428571429
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5ce22fa37f74da0d79613287ea2a28f0367ed90e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 15, max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.990476190476
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..00f4dc533a8b52cd78f3ad418f40ac251c3b3109
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cdf61b88a412bc9efb53e31bc431b6ac7629b8e8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a41a5a4353831223af4d37c3d6fd0cdecc76138
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6ccf359244f5b7c816e205a2cf060cecb7090d86
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 18)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..13e6985553abef9ce2250d0156f7c02204751629
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..50dc139eca46ddd727f57817a35d397e477c5b0c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3df2f2764f2a3b6912df0406acf0debe4ff0cee2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.561904761905
+accuracy_score on test : 0.555555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 43
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.561904761905
+		- Score on test : 0.555555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c50be0faaab517f9f476055f93dc8827f4b5fe42
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.444444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 15, max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.444444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9737bc60b6a81ef8306e51bf1768f3370403522c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.504761904762
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.504761904762
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3775503636a828deac35d0d630a88a7e4f7596be
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.485714285714
+accuracy_score on test : 0.377777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.485714285714
+		- Score on test : 0.377777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c60418d9c7280dac254d09a2e03db377d1a60735
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.966666666667
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.966666666667
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..61f4324fe4d1830886a306540ebba36706bd822b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 8)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..291654d0a64ba132f456a08ec9d2eb54e89ff880
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d4f311472f5bcd36461ba35eaf04b948491f1dee
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2f3557c25b5505ac1da96d7cdc8b49e97d45637a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 43
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.52380952381
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..38a3f4d6dbb92e253780af0c629ce97b0b49083d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 15, max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8c91b51953caa589c23da90aae8580f4406fa914
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..006bed3d21a945338e0ed75c8de6b4d081e2bb50
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.519047619048
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.519047619048
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9d41e795b9288b98ba77f6acd82a3dac1be247c4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..482a6b43b8b1122f38971932f15ab11324b5322b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 19)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9df5e00e4c3b91debe6c6b59f12e9b173aa4f000
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 15, max_depth : 11
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..69d5a4ea222aed7c1bae135c20aa3d0b5fabba20
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.495238095238
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.495238095238
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..241a8b39d0fed51e6b032a01cdc8f26f96afe58f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.409523809524
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 20)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 7957
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.409523809524
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..12848a0f76e3885f881bd72b569fe95bb365c8d7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,1250 @@
+2016-09-06 10:40:15,413 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:40:15,414 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.000114625 Gbytes /!\ 
+2016-09-06 10:40:20,424 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:40:20,425 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:40:20,483 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:20,483 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:20,484 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:40:20,484 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:40:20,484 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:20,484 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:20,485 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:20,485 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:20,485 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:20,485 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:20,485 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:20,485 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:20,485 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:20,486 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:20,546 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:20,546 DEBUG: Start:	 Training
+2016-09-06 10:40:20,549 DEBUG: Info:	 Time for Training: 0.0666189193726[s]
+2016-09-06 10:40:20,549 DEBUG: Done:	 Training
+2016-09-06 10:40:20,549 DEBUG: Start:	 Predicting
+2016-09-06 10:40:20,553 DEBUG: Done:	 Predicting
+2016-09-06 10:40:20,553 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:20,556 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:20,556 INFO: Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.933333333333
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.933333333333
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:20,556 INFO: Done:	 Result Analysis
+2016-09-06 10:40:20,558 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:20,558 DEBUG: Start:	 Training
+2016-09-06 10:40:20,565 DEBUG: Info:	 Time for Training: 0.0827748775482[s]
+2016-09-06 10:40:20,565 DEBUG: Done:	 Training
+2016-09-06 10:40:20,566 DEBUG: Start:	 Predicting
+2016-09-06 10:40:20,570 DEBUG: Done:	 Predicting
+2016-09-06 10:40:20,570 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:20,573 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:20,573 INFO: Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:20,574 INFO: Done:	 Result Analysis
+2016-09-06 10:40:20,633 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:20,633 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:20,633 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:40:20,633 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:40:20,633 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:20,633 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:20,634 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:20,634 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:20,634 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:20,634 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:20,634 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:20,634 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:20,634 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:20,634 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:20,688 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:20,688 DEBUG: Start:	 Training
+2016-09-06 10:40:20,689 DEBUG: Info:	 Time for Training: 0.0570049285889[s]
+2016-09-06 10:40:20,689 DEBUG: Done:	 Training
+2016-09-06 10:40:20,689 DEBUG: Start:	 Predicting
+2016-09-06 10:40:20,698 DEBUG: Done:	 Predicting
+2016-09-06 10:40:20,698 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:20,700 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:20,701 INFO: Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:20,701 INFO: Done:	 Result Analysis
+2016-09-06 10:40:21,198 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:21,198 DEBUG: Start:	 Training
+2016-09-06 10:40:21,275 DEBUG: Info:	 Time for Training: 0.643486976624[s]
+2016-09-06 10:40:21,276 DEBUG: Done:	 Training
+2016-09-06 10:40:21,276 DEBUG: Start:	 Predicting
+2016-09-06 10:40:21,285 DEBUG: Done:	 Predicting
+2016-09-06 10:40:21,286 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:21,287 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:21,287 INFO: Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 29, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.990476190476
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,287 INFO: Done:	 Result Analysis
+2016-09-06 10:40:21,386 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,386 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:40:21,386 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:21,387 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,387 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:21,387 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:21,387 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:40:21,388 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:21,388 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:21,388 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,389 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:21,389 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:21,389 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:21,389 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,433 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:21,433 DEBUG: Start:	 Training
+2016-09-06 10:40:21,434 DEBUG: Info:	 Time for Training: 0.0487298965454[s]
+2016-09-06 10:40:21,434 DEBUG: Done:	 Training
+2016-09-06 10:40:21,434 DEBUG: Start:	 Predicting
+2016-09-06 10:40:21,446 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:21,446 DEBUG: Start:	 Training
+2016-09-06 10:40:21,453 DEBUG: Done:	 Predicting
+2016-09-06 10:40:21,453 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:21,455 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:21,455 INFO: Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.609523809524
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.609523809524
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,455 INFO: Done:	 Result Analysis
+2016-09-06 10:40:21,468 DEBUG: Info:	 Time for Training: 0.0820069313049[s]
+2016-09-06 10:40:21,468 DEBUG: Done:	 Training
+2016-09-06 10:40:21,468 DEBUG: Start:	 Predicting
+2016-09-06 10:40:21,472 DEBUG: Done:	 Predicting
+2016-09-06 10:40:21,472 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:21,473 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:21,473 INFO: Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.538095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,473 INFO: Done:	 Result Analysis
+2016-09-06 10:40:21,527 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,527 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,527 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:40:21,527 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:40:21,527 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:21,527 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:21,528 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:21,528 DEBUG: Info:	 Shape X_train:(210, 13), Length of y_train:210
+2016-09-06 10:40:21,528 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:21,528 DEBUG: Info:	 Shape X_test:(90, 13), Length of y_test:90
+2016-09-06 10:40:21,528 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:21,528 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:21,529 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,529 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,580 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:21,580 DEBUG: Start:	 Training
+2016-09-06 10:40:21,590 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:21,590 DEBUG: Start:	 Training
+2016-09-06 10:40:21,597 DEBUG: Info:	 Time for Training: 0.0706040859222[s]
+2016-09-06 10:40:21,597 DEBUG: Done:	 Training
+2016-09-06 10:40:21,597 DEBUG: Start:	 Predicting
+2016-09-06 10:40:21,603 DEBUG: Done:	 Predicting
+2016-09-06 10:40:21,603 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:21,605 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:21,605 INFO: Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,605 INFO: Done:	 Result Analysis
+2016-09-06 10:40:21,610 DEBUG: Info:	 Time for Training: 0.0829930305481[s]
+2016-09-06 10:40:21,610 DEBUG: Done:	 Training
+2016-09-06 10:40:21,610 DEBUG: Start:	 Predicting
+2016-09-06 10:40:21,614 DEBUG: Done:	 Predicting
+2016-09-06 10:40:21,614 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:21,615 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:21,615 INFO: Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,615 INFO: Done:	 Result Analysis
+2016-09-06 10:40:21,678 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,678 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:40:21,678 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:21,678 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,679 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:40:21,679 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:21,679 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:21,679 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:21,679 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:21,679 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:21,679 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:21,679 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,679 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:21,679 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,715 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:21,715 DEBUG: Start:	 Training
+2016-09-06 10:40:21,717 DEBUG: Info:	 Time for Training: 0.0389919281006[s]
+2016-09-06 10:40:21,717 DEBUG: Done:	 Training
+2016-09-06 10:40:21,717 DEBUG: Start:	 Predicting
+2016-09-06 10:40:21,720 DEBUG: Done:	 Predicting
+2016-09-06 10:40:21,720 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:21,721 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:21,721 INFO: Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.814285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.814285714286
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,721 INFO: Done:	 Result Analysis
+2016-09-06 10:40:21,730 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:21,730 DEBUG: Start:	 Training
+2016-09-06 10:40:21,734 DEBUG: Info:	 Time for Training: 0.056380033493[s]
+2016-09-06 10:40:21,734 DEBUG: Done:	 Training
+2016-09-06 10:40:21,734 DEBUG: Start:	 Predicting
+2016-09-06 10:40:21,737 DEBUG: Done:	 Predicting
+2016-09-06 10:40:21,737 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:21,740 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:21,740 INFO: Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,741 INFO: Done:	 Result Analysis
+2016-09-06 10:40:21,832 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,832 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:21,833 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:40:21,833 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:40:21,833 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:21,833 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:21,834 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:21,834 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:21,834 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:21,834 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:21,835 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:21,835 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:21,835 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,835 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:21,881 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:21,882 DEBUG: Start:	 Training
+2016-09-06 10:40:21,883 DEBUG: Info:	 Time for Training: 0.0516860485077[s]
+2016-09-06 10:40:21,883 DEBUG: Done:	 Training
+2016-09-06 10:40:21,883 DEBUG: Start:	 Predicting
+2016-09-06 10:40:21,889 DEBUG: Done:	 Predicting
+2016-09-06 10:40:21,890 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:21,892 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:21,892 INFO: Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.652380952381
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.652380952381
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:21,892 INFO: Done:	 Result Analysis
+2016-09-06 10:40:22,343 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:22,343 DEBUG: Start:	 Training
+2016-09-06 10:40:22,417 DEBUG: Info:	 Time for Training: 0.585797071457[s]
+2016-09-06 10:40:22,417 DEBUG: Done:	 Training
+2016-09-06 10:40:22,417 DEBUG: Start:	 Predicting
+2016-09-06 10:40:22,425 DEBUG: Done:	 Predicting
+2016-09-06 10:40:22,425 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:22,427 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:22,427 INFO: Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 29, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:22,427 INFO: Done:	 Result Analysis
+2016-09-06 10:40:22,574 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,574 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,574 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:40:22,574 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:40:22,574 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:22,574 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:22,575 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:22,575 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:22,575 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:22,575 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:22,575 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:22,575 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:22,575 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:22,575 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:22,619 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:22,619 DEBUG: Start:	 Training
+2016-09-06 10:40:22,620 DEBUG: Info:	 Time for Training: 0.0461812019348[s]
+2016-09-06 10:40:22,620 DEBUG: Done:	 Training
+2016-09-06 10:40:22,620 DEBUG: Start:	 Predicting
+2016-09-06 10:40:22,624 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:22,624 DEBUG: Start:	 Training
+2016-09-06 10:40:22,642 DEBUG: Done:	 Predicting
+2016-09-06 10:40:22,642 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:22,643 DEBUG: Info:	 Time for Training: 0.0691771507263[s]
+2016-09-06 10:40:22,643 DEBUG: Done:	 Training
+2016-09-06 10:40:22,643 DEBUG: Start:	 Predicting
+2016-09-06 10:40:22,644 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:22,644 INFO: Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:22,644 INFO: Done:	 Result Analysis
+2016-09-06 10:40:22,646 DEBUG: Done:	 Predicting
+2016-09-06 10:40:22,647 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:22,648 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:22,648 INFO: Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.52380952381
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:22,648 INFO: Done:	 Result Analysis
+2016-09-06 10:40:22,725 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,725 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,726 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:40:22,726 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:40:22,726 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:22,726 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:22,727 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:22,727 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:22,727 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:22,727 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:22,727 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:22,727 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:22,727 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:22,727 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:22,798 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:22,798 DEBUG: Start:	 Training
+2016-09-06 10:40:22,807 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:22,807 DEBUG: Start:	 Training
+2016-09-06 10:40:22,822 DEBUG: Info:	 Time for Training: 0.0971689224243[s]
+2016-09-06 10:40:22,822 DEBUG: Done:	 Training
+2016-09-06 10:40:22,822 DEBUG: Start:	 Predicting
+2016-09-06 10:40:22,829 DEBUG: Done:	 Predicting
+2016-09-06 10:40:22,829 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:22,831 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:22,831 INFO: Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.622222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:22,832 INFO: Done:	 Result Analysis
+2016-09-06 10:40:22,833 DEBUG: Info:	 Time for Training: 0.108348846436[s]
+2016-09-06 10:40:22,833 DEBUG: Done:	 Training
+2016-09-06 10:40:22,833 DEBUG: Start:	 Predicting
+2016-09-06 10:40:22,836 DEBUG: Done:	 Predicting
+2016-09-06 10:40:22,836 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:22,837 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:22,838 INFO: Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.6
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:22,838 INFO: Done:	 Result Analysis
+2016-09-06 10:40:22,968 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,968 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:22,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:40:22,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:40:22,968 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:22,968 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:22,969 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:22,969 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:22,969 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:22,969 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:22,969 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:22,969 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:22,969 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:22,969 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:23,006 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:23,006 DEBUG: Start:	 Training
+2016-09-06 10:40:23,008 DEBUG: Info:	 Time for Training: 0.0404770374298[s]
+2016-09-06 10:40:23,008 DEBUG: Done:	 Training
+2016-09-06 10:40:23,008 DEBUG: Start:	 Predicting
+2016-09-06 10:40:23,010 DEBUG: Done:	 Predicting
+2016-09-06 10:40:23,011 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:23,012 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:23,012 INFO: Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,012 INFO: Done:	 Result Analysis
+2016-09-06 10:40:23,021 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:23,021 DEBUG: Start:	 Training
+2016-09-06 10:40:23,025 DEBUG: Info:	 Time for Training: 0.0578100681305[s]
+2016-09-06 10:40:23,025 DEBUG: Done:	 Training
+2016-09-06 10:40:23,025 DEBUG: Start:	 Predicting
+2016-09-06 10:40:23,028 DEBUG: Done:	 Predicting
+2016-09-06 10:40:23,028 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:23,030 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:23,030 INFO: Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,031 INFO: Done:	 Result Analysis
+2016-09-06 10:40:23,122 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:23,122 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:23,122 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:40:23,122 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:40:23,123 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:23,123 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:23,124 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:23,124 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:23,124 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:23,124 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:23,124 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:23,124 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:23,124 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:23,124 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:23,174 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:23,175 DEBUG: Start:	 Training
+2016-09-06 10:40:23,175 DEBUG: Info:	 Time for Training: 0.0543978214264[s]
+2016-09-06 10:40:23,176 DEBUG: Done:	 Training
+2016-09-06 10:40:23,176 DEBUG: Start:	 Predicting
+2016-09-06 10:40:23,184 DEBUG: Done:	 Predicting
+2016-09-06 10:40:23,184 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:23,186 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:23,186 INFO: Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.619047619048
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,187 INFO: Done:	 Result Analysis
+2016-09-06 10:40:23,643 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:23,643 DEBUG: Start:	 Training
+2016-09-06 10:40:23,719 DEBUG: Info:	 Time for Training: 0.597998142242[s]
+2016-09-06 10:40:23,719 DEBUG: Done:	 Training
+2016-09-06 10:40:23,719 DEBUG: Start:	 Predicting
+2016-09-06 10:40:23,727 DEBUG: Done:	 Predicting
+2016-09-06 10:40:23,727 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:23,729 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:23,729 INFO: Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 29, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,729 INFO: Done:	 Result Analysis
+2016-09-06 10:40:23,867 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:23,868 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:40:23,868 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:23,869 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:23,869 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:23,869 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:23,869 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:23,873 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:23,873 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:40:23,874 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:23,875 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:23,875 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:23,875 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:23,875 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:23,915 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:23,915 DEBUG: Start:	 Training
+2016-09-06 10:40:23,916 DEBUG: Info:	 Time for Training: 0.0492420196533[s]
+2016-09-06 10:40:23,916 DEBUG: Done:	 Training
+2016-09-06 10:40:23,916 DEBUG: Start:	 Predicting
+2016-09-06 10:40:23,927 DEBUG: Done:	 Predicting
+2016-09-06 10:40:23,928 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:23,929 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:23,929 INFO: Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.62380952381
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,929 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:23,929 INFO: Done:	 Result Analysis
+2016-09-06 10:40:23,929 DEBUG: Start:	 Training
+2016-09-06 10:40:23,950 DEBUG: Info:	 Time for Training: 0.0777189731598[s]
+2016-09-06 10:40:23,950 DEBUG: Done:	 Training
+2016-09-06 10:40:23,950 DEBUG: Start:	 Predicting
+2016-09-06 10:40:23,953 DEBUG: Done:	 Predicting
+2016-09-06 10:40:23,953 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:23,955 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:23,955 INFO: Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:23,955 INFO: Done:	 Result Analysis
+2016-09-06 10:40:24,016 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,016 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,016 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:40:24,016 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:40:24,016 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:24,016 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:24,017 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:24,017 DEBUG: Info:	 Shape X_train:(210, 14), Length of y_train:210
+2016-09-06 10:40:24,017 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:24,017 DEBUG: Info:	 Shape X_test:(90, 14), Length of y_test:90
+2016-09-06 10:40:24,017 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:24,017 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:24,017 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,017 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,065 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:24,065 DEBUG: Start:	 Training
+2016-09-06 10:40:24,072 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:24,072 DEBUG: Start:	 Training
+2016-09-06 10:40:24,082 DEBUG: Info:	 Time for Training: 0.066547870636[s]
+2016-09-06 10:40:24,082 DEBUG: Done:	 Training
+2016-09-06 10:40:24,082 DEBUG: Start:	 Predicting
+2016-09-06 10:40:24,088 DEBUG: Done:	 Predicting
+2016-09-06 10:40:24,088 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:24,090 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:24,090 INFO: Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:24,090 INFO: Done:	 Result Analysis
+2016-09-06 10:40:24,093 DEBUG: Info:	 Time for Training: 0.0777978897095[s]
+2016-09-06 10:40:24,093 DEBUG: Done:	 Training
+2016-09-06 10:40:24,093 DEBUG: Start:	 Predicting
+2016-09-06 10:40:24,099 DEBUG: Done:	 Predicting
+2016-09-06 10:40:24,099 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:24,100 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:24,100 INFO: Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:24,101 INFO: Done:	 Result Analysis
+2016-09-06 10:40:24,162 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,163 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:40:24,163 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:24,163 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,163 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:40:24,163 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:24,164 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:24,164 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:24,164 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:24,164 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:24,164 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:24,164 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:24,164 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,164 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,198 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:24,198 DEBUG: Start:	 Training
+2016-09-06 10:40:24,200 DEBUG: Info:	 Time for Training: 0.0375709533691[s]
+2016-09-06 10:40:24,200 DEBUG: Done:	 Training
+2016-09-06 10:40:24,200 DEBUG: Start:	 Predicting
+2016-09-06 10:40:24,202 DEBUG: Done:	 Predicting
+2016-09-06 10:40:24,202 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:24,204 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:24,204 INFO: Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:24,204 INFO: Done:	 Result Analysis
+2016-09-06 10:40:24,213 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:24,213 DEBUG: Start:	 Training
+2016-09-06 10:40:24,216 DEBUG: Info:	 Time for Training: 0.0543420314789[s]
+2016-09-06 10:40:24,216 DEBUG: Done:	 Training
+2016-09-06 10:40:24,217 DEBUG: Start:	 Predicting
+2016-09-06 10:40:24,220 DEBUG: Done:	 Predicting
+2016-09-06 10:40:24,220 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:24,222 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:24,222 INFO: Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:24,222 INFO: Done:	 Result Analysis
+2016-09-06 10:40:24,312 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,312 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:24,312 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:40:24,312 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:40:24,312 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:24,312 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:24,313 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:24,313 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:24,313 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:24,313 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:24,313 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:24,313 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:24,313 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,313 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:24,346 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:24,346 DEBUG: Start:	 Training
+2016-09-06 10:40:24,346 DEBUG: Info:	 Time for Training: 0.0354690551758[s]
+2016-09-06 10:40:24,347 DEBUG: Done:	 Training
+2016-09-06 10:40:24,347 DEBUG: Start:	 Predicting
+2016-09-06 10:40:24,352 DEBUG: Done:	 Predicting
+2016-09-06 10:40:24,352 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:24,353 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:24,353 INFO: Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.67619047619
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:24,353 INFO: Done:	 Result Analysis
+2016-09-06 10:40:24,910 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:24,910 DEBUG: Start:	 Training
+2016-09-06 10:40:24,992 DEBUG: Info:	 Time for Training: 0.680758953094[s]
+2016-09-06 10:40:24,992 DEBUG: Done:	 Training
+2016-09-06 10:40:24,992 DEBUG: Start:	 Predicting
+2016-09-06 10:40:25,001 DEBUG: Done:	 Predicting
+2016-09-06 10:40:25,001 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:25,003 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:25,003 INFO: Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 29, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:25,003 INFO: Done:	 Result Analysis
+2016-09-06 10:40:25,067 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:25,067 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:40:25,067 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:25,068 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:25,068 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:25,068 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:40:25,068 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:25,068 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:40:25,068 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:25,068 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:40:25,069 DEBUG: Info:	 Shape X_train:(210, 7), Length of y_train:210
+2016-09-06 10:40:25,069 DEBUG: Info:	 Shape X_test:(90, 7), Length of y_test:90
+2016-09-06 10:40:25,069 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:40:25,069 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:40:25,122 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:25,122 DEBUG: Start:	 Training
+2016-09-06 10:40:25,122 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:40:25,123 DEBUG: Start:	 Training
+2016-09-06 10:40:25,123 DEBUG: Info:	 Time for Training: 0.056645154953[s]
+2016-09-06 10:40:25,123 DEBUG: Done:	 Training
+2016-09-06 10:40:25,123 DEBUG: Start:	 Predicting
+2016-09-06 10:40:25,137 DEBUG: Done:	 Predicting
+2016-09-06 10:40:25,137 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:25,139 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:25,139 INFO: Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:25,139 INFO: Done:	 Result Analysis
+2016-09-06 10:40:25,140 DEBUG: Info:	 Time for Training: 0.0731339454651[s]
+2016-09-06 10:40:25,140 DEBUG: Done:	 Training
+2016-09-06 10:40:25,141 DEBUG: Start:	 Predicting
+2016-09-06 10:40:25,143 DEBUG: Done:	 Predicting
+2016-09-06 10:40:25,144 DEBUG: Start:	 Getting Results
+2016-09-06 10:40:25,145 DEBUG: Done:	 Getting Results
+2016-09-06 10:40:25,145 INFO: Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.442857142857
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.442857142857
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
+2016-09-06 10:40:25,145 INFO: Done:	 Result Analysis
+2016-09-06 10:40:25,362 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:40:25,362 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-06 10:40:25,363 INFO: ### Main Programm for Multiview Classification
+2016-09-06 10:40:25,363 INFO: Info:	 Shape of View0 :(300, 13)
+2016-09-06 10:40:25,363 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-06 10:40:25,363 INFO: Info:	 Shape of View1 :(300, 7)
+2016-09-06 10:40:25,364 INFO: Info:	 Shape of View0 :(300, 13)
+2016-09-06 10:40:25,364 INFO: Info:	 Shape of View2 :(300, 14)
+2016-09-06 10:40:25,364 INFO: Info:	 Shape of View1 :(300, 7)
+2016-09-06 10:40:25,364 INFO: Info:	 Shape of View3 :(300, 7)
+2016-09-06 10:40:25,364 INFO: Done:	 Read Database Files
+2016-09-06 10:40:25,365 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:40:25,365 INFO: Info:	 Shape of View2 :(300, 14)
+2016-09-06 10:40:25,365 INFO: Info:	 Shape of View3 :(300, 7)
+2016-09-06 10:40:25,365 INFO: Done:	 Read Database Files
+2016-09-06 10:40:25,365 INFO: Start:	 Determine validation split for ratio 0.7
+2016-09-06 10:40:25,370 INFO: Done:	 Determine validation split
+2016-09-06 10:40:25,370 INFO: Start:	 Determine 5 folds
+2016-09-06 10:40:25,371 INFO: Done:	 Determine validation split
+2016-09-06 10:40:25,371 INFO: Start:	 Determine 5 folds
+2016-09-06 10:40:25,381 INFO: Info:	 Length of Learning Sets: 170
+2016-09-06 10:40:25,382 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 10:40:25,382 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:40:25,382 INFO: Done:	 Determine folds
+2016-09-06 10:40:25,382 INFO: Start:	 Learning with Fusion and 5 folds
+2016-09-06 10:40:25,382 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 10:40:25,382 DEBUG: 	Start:	 Random search for Adaboost with 30 iterations
+2016-09-06 10:40:25,383 INFO: Info:	 Length of Learning Sets: 170
+2016-09-06 10:40:25,383 INFO: Info:	 Length of Testing Sets: 41
+2016-09-06 10:40:25,383 INFO: Info:	 Length of Validation Set: 89
+2016-09-06 10:40:25,383 INFO: Done:	 Determine folds
+2016-09-06 10:40:25,383 INFO: Start:	 Learning with Mumbo and 5 folds
+2016-09-06 10:40:25,384 INFO: Start:	 Randomsearching best settings for monoview classifiers
+2016-09-06 10:40:25,384 DEBUG: 	Start:	 Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3faa796576d558f78a91ea7e1fcc884b4df78b56
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View0 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c74ef47df5a9d17b3ef6810dbfaf5c0f69306bf8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with DecisionTree
+
+accuracy_score on train : 0.933333333333
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.933333333333
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5e8145f8e66ad1c25e8ad743b60b7a89ccb42aec
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with KNN
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.533333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c2e5210e65ea676ff7ab308742343f8291ebe4fa
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View1 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0468fed69893928d9a0e609f6f265c2c932f8cdb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with DecisionTree
+
+accuracy_score on train : 0.814285714286
+accuracy_score on test : 0.544444444444
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.814285714286
+		- Score on test : 0.544444444444
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..879e5454c0541eef300a785b6871747a3bf7b62c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with KNN
+
+accuracy_score on train : 0.652380952381
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.652380952381
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8c5ade6564fa78ae86fb39df42234b8abf5b0899
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with RandomForest
+
+accuracy_score on train : 0.990476190476
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 29, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.990476190476
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0a0d904ea21aad97c00fffecfda172877be03202
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SGD
+
+accuracy_score on train : 0.609523809524
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.609523809524
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..11b4dc01c8191d5d64ae5e5669e61ef2a9f86b9f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMLinear
+
+accuracy_score on train : 0.538095238095
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.538095238095
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bc68c9fa8356059b50fa99b98d626856cb83a143
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bc4d679a3a2252b5604aaaf36013ca93d60429a7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View0 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View0	 View shape : (300, 13)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4fe0cec465267a89c8faffefa17445874d72b701
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.477777777778
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 29, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.477777777778
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5b99d1af360e6d71a2416f27d48c428a2a2aba87
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SGD
+
+accuracy_score on train : 0.547619047619
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.547619047619
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0fa62be2094cdf9cf2ed8abfa1734a60de50397b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMLinear
+
+accuracy_score on train : 0.52380952381
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.52380952381
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9e75865c785a0b71bcd00e7370bab5fea3e396a0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMPoly
+
+accuracy_score on train : 0.6
+accuracy_score on test : 0.511111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.6
+		- Score on test : 0.511111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..20513448dffe5923ca34fc178ba15c868cca2989
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View1 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.622222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View1	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.622222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c9dca6930f629808ed526069e3992b0842cf0e32
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View2 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..262200771f213e3c39ec5f8692ad942a31f4a639
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with DecisionTree
+
+accuracy_score on train : 0.985714285714
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.985714285714
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9a5e6d349a402940e8e5d4c7c827f8df2e989361
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with KNN
+
+accuracy_score on train : 0.619047619048
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.619047619048
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bf83d84c1f07dab88b28134ffe909e977f983cbf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with RandomForest
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.411111111111
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 29, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.411111111111
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..05e8489bcab51ae71edcc8c3f2ad0ee121ba488a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SGD
+
+accuracy_score on train : 0.62380952381
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.62380952381
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c1e2e693e97755134b7a4a6dc3a6f5b6e693c6a5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMLinear
+
+accuracy_score on train : 0.557142857143
+accuracy_score on test : 0.5
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.557142857143
+		- Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8bffe4939db8534a4391137578ffe51b2a204076
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,27 @@
+Classification on Fake database for View3 with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.488888888889
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.488888888889
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f1942cf7f9990b57a25f4c3284ca51a554a3b4e8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with DecisionTree
+
+accuracy_score on train : 0.995238095238
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.995238095238
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0717704089f5c6d64549167b0332d562cbe0cb5c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with KNN
+
+accuracy_score on train : 0.67619047619
+accuracy_score on test : 0.466666666667
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.67619047619
+		- Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cb100076475a0ca7317ca90c36f42e9c7b105095
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 29, max_depth : 9
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f433c885fd1da688509f9c1bac3bebca2241bd4b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.433333333333
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.433333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d0ca9fbd591e2f11dfe0c2792c39610f1feb7946
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View2 with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.422222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View2	 View shape : (300, 14)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.422222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3a1843267da6d3a46cd3f328763f538046af018a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SGD
+
+accuracy_score on train : 0.585714285714
+accuracy_score on test : 0.522222222222
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l2
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.585714285714
+		- Score on test : 0.522222222222
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
new file mode 100644
index 0000000000000000000000000000000000000000..76add94decf94c94beb9f16d628433333aea7465
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
@@ -0,0 +1,24 @@
+Classification on Fake database for View3 with SVMLinear
+
+accuracy_score on train : 0.442857142857
+accuracy_score on test : 0.455555555556
+
+Database configuration : 
+	- Database name : Fake
+	- View name : View3	 View shape : (300, 7)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 1181
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.442857142857
+		- Score on test : 0.455555555556
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104051-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-104051-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..d46207b5fedda8c93bcf7d35dc00292842cb4094
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104051-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,664 @@
+2016-09-06 10:40:51,299 DEBUG: Start:	 Creating 2 temporary datasets for multiprocessing
+2016-09-06 10:40:51,299 WARNING:  WARNING : /!\ This may use a lot of HDD storage space : 0.273145851562 Gbytes /!\ 
+2016-09-06 10:41:01,425 DEBUG: Start:	 Creating datasets for multiprocessing
+2016-09-06 10:41:01,427 INFO: Start:	 Finding all available mono- & multiview algorithms
+2016-09-06 10:41:02,152 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:02,152 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:41:02,152 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:02,204 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:02,204 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:02,204 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:02,204 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:02,825 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:02,825 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:41:02,826 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:02,871 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:02,871 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:02,871 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:02,871 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:12,176 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:12,176 DEBUG: Start:	 Training
+2016-09-06 10:41:13,078 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:13,078 DEBUG: Start:	 Training
+2016-09-06 10:41:14,378 DEBUG: Info:	 Time for Training: 12.8995170593[s]
+2016-09-06 10:41:14,378 DEBUG: Done:	 Training
+2016-09-06 10:41:14,378 DEBUG: Start:	 Predicting
+2016-09-06 10:41:14,451 DEBUG: Done:	 Predicting
+2016-09-06 10:41:14,452 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:14,454 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:14,454 INFO: Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.895238095238
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 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 : 0.895238095238
+
+
+ Classification took 0:00:12
+2016-09-06 10:41:14,466 INFO: Done:	 Result Analysis
+2016-09-06 10:41:15,257 DEBUG: Info:	 Time for Training: 13.7781460285[s]
+2016-09-06 10:41:15,257 DEBUG: Done:	 Training
+2016-09-06 10:41:15,257 DEBUG: Start:	 Predicting
+2016-09-06 10:41:15,271 DEBUG: Done:	 Predicting
+2016-09-06 10:41:15,271 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:15,273 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:15,273 INFO: Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.885714285714
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.885714285714
+
+
+ Classification took 0:00:13
+2016-09-06 10:41:15,273 INFO: Done:	 Result Analysis
+2016-09-06 10:41:15,379 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:15,379 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:41:15,379 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:15,380 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:15,381 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:41:15,381 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:15,405 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:15,406 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:15,406 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:15,406 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:15,407 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:15,407 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:15,407 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:15,408 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:16,396 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:16,396 DEBUG: Start:	 Training
+2016-09-06 10:41:16,569 DEBUG: Info:	 Time for Training: 1.2121629715[s]
+2016-09-06 10:41:16,569 DEBUG: Done:	 Training
+2016-09-06 10:41:16,569 DEBUG: Start:	 Predicting
+2016-09-06 10:41:16,587 DEBUG: Done:	 Predicting
+2016-09-06 10:41:16,587 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:16,589 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:16,589 INFO: Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 0.99173553719
+accuracy_score on test : 0.885714285714
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 9, 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 : 0.99173553719
+		- Score on test : 0.885714285714
+
+
+ Classification took 0:00:01
+2016-09-06 10:41:16,599 INFO: Done:	 Result Analysis
+2016-09-06 10:41:18,706 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:18,706 DEBUG: Start:	 Training
+2016-09-06 10:41:18,881 DEBUG: Info:	 Time for Training: 3.52481889725[s]
+2016-09-06 10:41:18,882 DEBUG: Done:	 Training
+2016-09-06 10:41:18,882 DEBUG: Start:	 Predicting
+2016-09-06 10:41:23,403 DEBUG: Done:	 Predicting
+2016-09-06 10:41:23,403 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:23,405 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:23,405 INFO: Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.834710743802
+accuracy_score on test : 0.933333333333
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 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 : 0.834710743802
+		- Score on test : 0.933333333333
+
+
+ Classification took 0:00:03
+2016-09-06 10:41:23,405 INFO: Done:	 Result Analysis
+2016-09-06 10:41:23,544 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:23,545 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:41:23,545 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:23,546 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:23,546 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:41:23,546 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:23,571 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:23,571 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:23,572 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:23,572 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:23,573 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:23,573 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:23,573 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:23,573 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:26,298 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:26,298 DEBUG: Start:	 Training
+2016-09-06 10:41:26,749 DEBUG: Info:	 Time for Training: 3.22519683838[s]
+2016-09-06 10:41:26,749 DEBUG: Done:	 Training
+2016-09-06 10:41:26,749 DEBUG: Start:	 Predicting
+2016-09-06 10:41:26,811 DEBUG: Done:	 Predicting
+2016-09-06 10:41:26,812 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:26,814 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:26,814 INFO: Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.780952380952
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.710743801653
+		- Score on test : 0.780952380952
+
+
+ Classification took 0:00:03
+2016-09-06 10:41:26,814 INFO: Done:	 Result Analysis
+2016-09-06 10:41:30,732 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:30,732 DEBUG: Start:	 Training
+2016-09-06 10:41:37,215 DEBUG: Info:	 Time for Training: 13.6910610199[s]
+2016-09-06 10:41:37,215 DEBUG: Done:	 Training
+2016-09-06 10:41:37,215 DEBUG: Start:	 Predicting
+2016-09-06 10:41:38,558 DEBUG: Done:	 Predicting
+2016-09-06 10:41:38,558 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:38,560 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:38,560 INFO: Classification on MultiOmic database for Methyl with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.961904761905
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.961904761905
+
+
+ Classification took 0:00:13
+2016-09-06 10:41:38,583 INFO: Done:	 Result Analysis
+2016-09-06 10:41:38,729 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:38,730 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:41:38,730 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:38,730 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:38,731 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:41:38,731 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:38,766 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:38,766 DEBUG: Info:	 Shape X_train:(242, 25978), Length of y_train:242
+2016-09-06 10:41:38,766 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:38,766 DEBUG: Info:	 Shape X_test:(105, 25978), Length of y_test:105
+2016-09-06 10:41:38,766 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:38,766 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:38,767 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:38,767 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:45,735 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:45,735 DEBUG: Start:	 Training
+2016-09-06 10:41:47,508 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:47,508 DEBUG: Start:	 Training
+2016-09-06 10:41:52,200 DEBUG: Info:	 Time for Training: 13.4952919483[s]
+2016-09-06 10:41:52,200 DEBUG: Done:	 Training
+2016-09-06 10:41:52,201 DEBUG: Start:	 Predicting
+2016-09-06 10:41:54,059 DEBUG: Done:	 Predicting
+2016-09-06 10:41:54,059 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:54,061 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:54,061 INFO: Classification on MultiOmic database for Methyl with SVMPoly
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.780952380952
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.710743801653
+		- Score on test : 0.780952380952
+
+
+ Classification took 0:00:13
+2016-09-06 10:41:54,061 INFO: Done:	 Result Analysis
+2016-09-06 10:41:55,637 DEBUG: Info:	 Time for Training: 16.932240963[s]
+2016-09-06 10:41:55,637 DEBUG: Done:	 Training
+2016-09-06 10:41:55,637 DEBUG: Start:	 Predicting
+2016-09-06 10:41:57,044 DEBUG: Done:	 Predicting
+2016-09-06 10:41:57,044 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:57,045 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:57,045 INFO: Classification on MultiOmic database for Methyl with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.961904761905
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.961904761905
+
+
+ Classification took 0:00:16
+2016-09-06 10:41:57,058 INFO: Done:	 Result Analysis
+2016-09-06 10:41:57,256 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:57,256 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:41:57,256 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:57,258 DEBUG: Info:	 Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:57,258 DEBUG: Info:	 Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:57,258 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:57,259 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:57,267 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:57,267 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:41:57,267 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:57,269 DEBUG: Info:	 Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:57,269 DEBUG: Info:	 Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:57,269 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:57,269 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:57,528 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:57,528 DEBUG: Start:	 Training
+2016-09-06 10:41:57,534 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:57,534 DEBUG: Start:	 Training
+2016-09-06 10:41:57,581 DEBUG: Info:	 Time for Training: 0.376816987991[s]
+2016-09-06 10:41:57,582 DEBUG: Done:	 Training
+2016-09-06 10:41:57,582 DEBUG: Start:	 Predicting
+2016-09-06 10:41:57,585 DEBUG: Done:	 Predicting
+2016-09-06 10:41:57,585 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:57,586 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:57,586 INFO: Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.819047619048
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- 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 : 0.819047619048
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:57,587 INFO: Done:	 Result Analysis
+2016-09-06 10:41:57,591 DEBUG: Info:	 Time for Training: 0.386079072952[s]
+2016-09-06 10:41:57,591 DEBUG: Done:	 Training
+2016-09-06 10:41:57,591 DEBUG: Start:	 Predicting
+2016-09-06 10:41:57,594 DEBUG: Done:	 Predicting
+2016-09-06 10:41:57,594 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:57,596 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:57,596 INFO: Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.838095238095
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:57,607 INFO: Done:	 Result Analysis
+2016-09-06 10:41:57,755 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:57,755 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:57,755 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-06 10:41:57,755 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-06 10:41:57,756 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:57,756 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:57,758 DEBUG: Info:	 Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:57,758 DEBUG: Info:	 Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:57,759 DEBUG: Info:	 Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:57,759 DEBUG: Info:	 Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:57,759 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:57,759 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:57,759 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:57,759 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:57,949 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:57,949 DEBUG: Start:	 Training
+2016-09-06 10:41:57,957 DEBUG: Info:	 Time for Training: 0.203402996063[s]
+2016-09-06 10:41:57,957 DEBUG: Done:	 Training
+2016-09-06 10:41:57,957 DEBUG: Start:	 Predicting
+2016-09-06 10:41:58,094 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:58,094 DEBUG: Start:	 Training
+2016-09-06 10:41:58,145 DEBUG: Info:	 Time for Training: 0.391664028168[s]
+2016-09-06 10:41:58,145 DEBUG: Done:	 Training
+2016-09-06 10:41:58,145 DEBUG: Start:	 Predicting
+2016-09-06 10:41:58,151 DEBUG: Done:	 Predicting
+2016-09-06 10:41:58,152 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:58,154 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:58,154 INFO: Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 9, 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 : 0.866666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:58,154 INFO: Done:	 Result Analysis
+2016-09-06 10:41:58,180 DEBUG: Done:	 Predicting
+2016-09-06 10:41:58,180 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:58,182 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:58,182 INFO: Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.789256198347
+accuracy_score on test : 0.866666666667
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 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 : 0.789256198347
+		- Score on test : 0.866666666667
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:58,199 INFO: Done:	 Result Analysis
+2016-09-06 10:41:58,303 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:58,303 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:58,303 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-06 10:41:58,303 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-06 10:41:58,303 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:58,303 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:58,305 DEBUG: Info:	 Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:58,305 DEBUG: Info:	 Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:58,306 DEBUG: Info:	 Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:58,306 DEBUG: Info:	 Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:58,306 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:58,306 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:58,306 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:58,306 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:58,464 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:58,464 DEBUG: Start:	 Training
+2016-09-06 10:41:58,477 DEBUG: Info:	 Time for Training: 0.175203084946[s]
+2016-09-06 10:41:58,477 DEBUG: Done:	 Training
+2016-09-06 10:41:58,477 DEBUG: Start:	 Predicting
+2016-09-06 10:41:58,481 DEBUG: Done:	 Predicting
+2016-09-06 10:41:58,481 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:58,483 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:58,483 INFO: Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.752380952381
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.760330578512
+		- Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:58,483 INFO: Done:	 Result Analysis
+2016-09-06 10:41:58,563 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:58,563 DEBUG: Start:	 Training
+2016-09-06 10:41:58,747 DEBUG: Info:	 Time for Training: 0.445248126984[s]
+2016-09-06 10:41:58,747 DEBUG: Done:	 Training
+2016-09-06 10:41:58,747 DEBUG: Start:	 Predicting
+2016-09-06 10:41:58,788 DEBUG: Done:	 Predicting
+2016-09-06 10:41:58,789 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:58,790 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:58,790 INFO: Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.776859504132
+accuracy_score on test : 0.8
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.776859504132
+		- Score on test : 0.8
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:58,790 INFO: Done:	 Result Analysis
+2016-09-06 10:41:58,853 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:58,853 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:41:58,853 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-06 10:41:58,853 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-06 10:41:58,854 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:58,854 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:41:58,856 DEBUG: Info:	 Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:58,856 DEBUG: Info:	 Shape X_train:(242, 1046), Length of y_train:242
+2016-09-06 10:41:58,856 DEBUG: Info:	 Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:58,856 DEBUG: Info:	 Shape X_test:(105, 1046), Length of y_test:105
+2016-09-06 10:41:58,856 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:58,856 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:41:58,857 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:58,857 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:41:58,930 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:58,930 DEBUG: Start:	 Training
+2016-09-06 10:41:58,941 DEBUG: Info:	 Time for Training: 0.0891480445862[s]
+2016-09-06 10:41:58,941 DEBUG: Done:	 Training
+2016-09-06 10:41:58,941 DEBUG: Start:	 Predicting
+2016-09-06 10:41:58,947 DEBUG: Done:	 Predicting
+2016-09-06 10:41:58,947 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:58,949 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:58,949 INFO: Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.289256198347
+accuracy_score on test : 0.219047619048
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.289256198347
+		- Score on test : 0.219047619048
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:58,949 INFO: Done:	 Result Analysis
+2016-09-06 10:41:59,282 DEBUG: Done:	 RandomSearch best settings
+2016-09-06 10:41:59,282 DEBUG: Start:	 Training
+2016-09-06 10:41:59,624 DEBUG: Info:	 Time for Training: 0.773069143295[s]
+2016-09-06 10:41:59,625 DEBUG: Done:	 Training
+2016-09-06 10:41:59,625 DEBUG: Start:	 Predicting
+2016-09-06 10:41:59,728 DEBUG: Done:	 Predicting
+2016-09-06 10:41:59,728 DEBUG: Start:	 Getting Results
+2016-09-06 10:41:59,729 DEBUG: Done:	 Getting Results
+2016-09-06 10:41:59,729 INFO: Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
+2016-09-06 10:41:59,729 INFO: Done:	 Result Analysis
+2016-09-06 10:42:02,068 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:42:02,068 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-06 10:42:02,068 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:42:02,117 DEBUG: ### Main Programm for Classification MonoView
+2016-09-06 10:42:02,117 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-06 10:42:02,117 DEBUG: Start:	 Determine Train/Test split
+2016-09-06 10:42:02,161 DEBUG: Info:	 Shape X_train:(242, 73599), Length of y_train:242
+2016-09-06 10:42:02,161 DEBUG: Info:	 Shape X_test:(105, 73599), Length of y_test:105
+2016-09-06 10:42:02,161 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:42:02,161 DEBUG: Start:	 RandomSearch best settings with 1 iterations
+2016-09-06 10:42:02,210 DEBUG: Info:	 Shape X_train:(242, 73599), Length of y_train:242
+2016-09-06 10:42:02,210 DEBUG: Info:	 Shape X_test:(105, 73599), Length of y_test:105
+2016-09-06 10:42:02,210 DEBUG: Done:	 Determine Train/Test split
+2016-09-06 10:42:02,211 DEBUG: Start:	 RandomSearch best settings with 1 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104114Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104114Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4d429abf5f1d505cccd287504e4779f5508b1c7d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104114Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.895238095238
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Decision Tree with max_depth : 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 : 0.895238095238
+
+
+ Classification took 0:00:12
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104115Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104115Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..70c256f20976e1e872ef2e7200d96aae3b627d0c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104115Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.885714285714
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.885714285714
+
+
+ Classification took 0:00:13
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104116Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104116Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..49c842a82a7507196aa5dbb29bd77c8ae61dfa85
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104116Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 0.99173553719
+accuracy_score on test : 0.885714285714
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 9, 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 : 0.99173553719
+		- Score on test : 0.885714285714
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104123Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104123Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f601216405ece76fd47cfbf79e844b2a09db7d6f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104123Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.834710743802
+accuracy_score on test : 0.933333333333
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 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 : 0.834710743802
+		- Score on test : 0.933333333333
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104126Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104126Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b94f721827310de6d70466576cb0ed663b991af6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104126Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.780952380952
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.710743801653
+		- Score on test : 0.780952380952
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104138Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104138Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ff7fb5c54ce48b27c0fe6361fb588da7856a8097
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104138Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.961904761905
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.961904761905
+
+
+ Classification took 0:00:13
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104154Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104154Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8c4d7034c559eba55719c8e1a13aef8394227576
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104154Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMPoly
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.780952380952
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.710743801653
+		- Score on test : 0.780952380952
+
+
+ Classification took 0:00:13
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6044fc66f8ff7bfbc07fa6e9da17935bfe23b33f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
+            min_samples_split=2, min_weight_fraction_leaf=0.0,
+            presort=False, random_state=None, splitter='best')
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.838095238095
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..68547d67f591211d81c05720ac2365263b08190a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.819047619048
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- 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 : 0.819047619048
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c553126f3412173b6bbb273698d1d15baf801fc7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104157Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.961904761905
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : Methyl	 View shape : (347, 25978)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.961904761905
+
+
+ Classification took 0:00:16
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7d687935926f90871fdcf69bd20ce28c0d7f61c8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.789256198347
+accuracy_score on test : 0.866666666667
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- K nearest Neighbors with  n_neighbors: 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 : 0.789256198347
+		- Score on test : 0.866666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..57c8d5f7b3fd11bcb40dd71ae09e69bc849702ed
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- Random Forest with num_esimators : 9, 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 : 0.866666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c1d0d088136eb6ca2b6dda73a58872ab16e3aa55
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.752380952381
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SGDClassifier with loss : log, penalty : l1
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.760330578512
+		- Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4a2f9c3fb93fe47f36ba1e5e8bcd58234bb428d7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.776859504132
+accuracy_score on test : 0.8
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.776859504132
+		- Score on test : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1e70623c2b4ff2d600ee326b657770ddebfb66a6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104158Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.289256198347
+accuracy_score on test : 0.219047619048
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 0.289256198347
+		- Score on test : 0.219047619048
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104159Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104159Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a092ae6a41595667eb982a36ec5b40813e1802a3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160906-104159Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration : 
+	- Database name : MultiOmic
+	- View name : MiRNA_	 View shape : (347, 1046)
+	- Learning Rate : 0.7
+	- Labels used : Non, Oui
+	- Number of cross validation folds : 5
+
+Classifier configuration : 
+	- SVM Linear with C : 6019
+	- Executed on 1 core(s) 
+	- Got configuration using randomized search with 1 iterations 
+
+
+	For Accuracy score using None as sample_weights (higher is better) : 
+		- Score on train : 1.0
+		- Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file