From 2855895c8617dfd643e6377b7aef83959cdc44f6 Mon Sep 17 00:00:00 2001
From: bbauvin <baptiste.bauvin@centrale-marseille.fr>
Date: Wed, 24 Aug 2016 17:30:06 -0400
Subject: [PATCH] Added some monoview classifiers (diff. svm and adaboost),
Added iteration gridsearch, added optimisation of dataset, simplified
classifiers, and probably more
---
.../ExecClassif.py | 218 +-
.../FeatExtraction/DBCrawl.py | 0
.../FeatExtraction/ExecFeatExtraction.py | 0
.../FeatExtraction/ExecFeatParaOpt.py | 0
.../FeatExtraction/FeatExtraction.py | 0
.../FeatExtraction/FeatParaOpt.py | 0
..._24-FE-Caltech-ClassLabels-Description.csv | 0
.../2016_03_24-FE-Caltech-ClassLabels.csv | 0
...rientaions_8-nbClusters_20-Maxiter_100.csv | 0
...Caltech-HSV-Bins_[16,16,16]-Norm_Distr.csv | 0
...ltech-RGB-Bins_16-MaxCI_256-Norm_Distr.csv | 0
...4-FE-Caltech-RGB_HSV_SIFT_SURF_HOG-LOG.log | 0
...-FE-Caltech-SIFT-Cluster_35-Norm_Distr.csv | 0
...-FE-Caltech-SURF-Cluster_30-Norm_Distr.csv | 0
.../2016_03_19-FeatParaOpt-HOG.csv | 0
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.../FeatExtraction/__init__.py | 0
.../FeatExtraction/hog_extraction.py | 0
.../hog_extraction_parallelized.py | 0
.../Monoview/ClassifMonoView.py | 25 +-
.../Monoview/ExecClassifMonoView.py | 123 +-
.../Monoview/ExecPlot.py | 0
.../Monoview/ExportResults.py | 0
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1487 files changed, 214026 insertions(+), 340 deletions(-)
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rename Code/{ => MonoMutliViewClassifiers}/Multiview/Results/Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-Methyl-MiRNA-RNASEQ-Clinical-learnRate4.0-nbIter2-Fake.txt (100%)
rename Code/{ => MonoMutliViewClassifiers}/Multiview/Results/Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-Methyl-MiRNA-RNASEQ-Clinical-learnRate4.0-nbIter2-FakeaccuracyByIteration.png (100%)
rename Code/{ => MonoMutliViewClassifiers}/Multiview/Results/Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-No-Yes-learnRate4.0-nbIter2-MultiOmic.txt (100%)
rename Code/{ => MonoMutliViewClassifiers}/Multiview/Results/Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-No-Yes-learnRate4.0-nbIter2-MultiOmicaccuracyByIteration.png (100%)
rename Code/{ => MonoMutliViewClassifiers}/Multiview/Results/Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-No-Yes-learnRate5.0-nbIter100-MultiOmic-accuracyByIteration.png (100%)
rename Code/{ => MonoMutliViewClassifiers}/Multiview/Results/Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-No-Yes-learnRate5.0-nbIter100-MultiOmic.txt (100%)
rename Code/{ => MonoMutliViewClassifiers}/Multiview/Results/Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-No-Yes-learnRate5.0-nbIter100-MultiOmicaccuracyByIteration.png (100%)
rename Code/{ => MonoMutliViewClassifiers}/Multiview/__init__.py (100%)
rename Code/{ => MonoMutliViewClassifiers}/Multiview/profile (100%)
rename Code/{ => MonoMutliViewClassifiers}/Multiview/res (100%)
rename Code/{ => MonoMutliViewClassifiers}/Multiview/run.py (60%)
rename Code/{ => MonoMutliViewClassifiers}/ResultAnalysis.py (100%)
rename Code/{Results/20160822-132504-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log => MonoMutliViewClassifiers/Results/20160822-132214-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log} (100%)
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160822-132504-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-132643-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-133011-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-133020-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-133220-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-133238-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-133410-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-133436-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-133458-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-133530-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-133639-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-133846-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-134037-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-134211-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-134319Results-Mumbo-Methyl-MiRNA_-RNASeq-Clinic-Yes-No-learnRate0.9-MultiOmic-accuracyByIteration.png (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-134319Results-Mumbo-Methyl-MiRNA_-RNASeq-Clinic-Yes-No-learnRate0.9-MultiOmic.txt (100%)
rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-140630-CMultiV-Benchmark-RGB_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
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rename Code/{ => MonoMutliViewClassifiers}/Results/20160822-141037-CMultiV-Benchmark-Methyl_HOG_SIFT_HOG_MHOG-MultiOmic-LOG.log (100%)
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create mode 100644 Code/MonoMutliViewClassifiers/Results/20160823-112139Results-Mumbo-Methyl-MiRNA_-RNASeq-Clinic-Yes-No-learnRate0.9-MultiOmic-accuracyByIteration.png
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160823-112139Results-Mumbo-Methyl-MiRNA_-RNASeq-Clinic-Yes-No-learnRate0.9-MultiOmic.txt
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160824-150940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
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create mode 100644 Code/MonoMutliViewClassifiers/Results/20160824-171252-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
rename Code/{ => MonoMutliViewClassifiers}/Versions.py (100%)
create mode 100644 Code/MonoMutliViewClassifiers/__init__.py
delete mode 100644 Code/Multiview/Fusion/Methods/MonoviewClassifiers/DecisionTree.py
delete mode 100644 Code/Multiview/Fusion/Methods/MonoviewClassifiers/KNN.py
delete mode 100644 Code/Multiview/Fusion/Methods/MonoviewClassifiers/RandomForest.py
delete mode 100644 Code/Multiview/Fusion/Methods/MonoviewClassifiers/SGD.py
delete mode 100644 Code/Multiview/Fusion/Methods/MonoviewClassifiers/SVC.py
diff --git a/Code/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
similarity index 62%
rename from Code/ExecClassif.py
rename to Code/MonoMutliViewClassifiers/ExecClassif.py
index dab41d2a..2a02e7dd 100644
--- a/Code/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -11,6 +11,8 @@ import logging
from joblib import Parallel, delayed
from ResultAnalysis import resultAnalysis
import numpy as np
+import MonoviewClassifiers
+
parser = argparse.ArgumentParser(
description='This file is used to benchmark the accuracies fo multiple classification algorithm on multiview data.',
@@ -62,12 +64,26 @@ groupRF = parser.add_argument_group('Random Forest arguments')
groupRF.add_argument('--CL_RF_trees', metavar='STRING', action='store', help='GridSearch: Determine the trees',
default='25 75 125 175')
-groupSVC = parser.add_argument_group('SVC arguments')
-groupSVC.add_argument('--CL_SVC_kernel', metavar='STRING', action='store', help='GridSearch : Kernels used',
- default='linear')
-groupSVC.add_argument('--CL_SVC_C', metavar='STRING', action='store', help='GridSearch : Penalty parameters used',
+groupSVMLinear = parser.add_argument_group('Linear SVM arguments')
+groupSVMLinear.add_argument('--CL_SVML_C', metavar='STRING', action='store', help='GridSearch : Penalty parameters used',
default='1:10:100:1000')
+groupSVMRBF = parser.add_argument_group('SVW-RBF arguments')
+groupSVMRBF.add_argument('--CL_SVMR_C', metavar='STRING', action='store', help='GridSearch : Penalty parameters used',
+ default='1:10:100:1000')
+
+groupSVMPoly = parser.add_argument_group('Poly SVM arguments')
+groupSVMPoly.add_argument('--CL_SVMP_C', metavar='STRING', action='store', help='GridSearch : Penalty parameters used',
+ default='1:10:100:1000')
+groupSVMPoly.add_argument('--CL_SVMP_deg', metavar='STRING', action='store', help='GridSearch : Degree parameters used',
+ default='1:2:5:10')
+
+groupAdaboost = parser.add_argument_group('Adaboost arguments')
+groupAdaboost.add_argument('--CL_Ada_n_est', metavar='STRING', action='store', help='GridSearch : Penalty parameters used',
+ default='1:10:100:1000')
+groupAdaboost.add_argument('--CL_Ada_b_est', metavar='STRING', action='store', help='GridSearch : Degree parameters used',
+ default='DecisionTreeClassifier')
+
groupRF = parser.add_argument_group('Decision Trees arguments')
groupRF.add_argument('--CL_DT_depth', metavar='STRING', action='store',
help='GridSearch: Determine max depth for Decision Trees', default='1:3:5:7')
@@ -90,8 +106,9 @@ groupMumbo.add_argument('--MU_types', metavar='STRING', action='store',
groupMumbo.add_argument('--MU_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the monoview classifier in Mumbo',
default=['3:1.0', '3:1.0', '3:1.0','3:1.0'])
-groupMumbo.add_argument('--MU_iter', metavar='INT', action='store',
- help='Number of iterations in Mumbos learning process', type=int, default=5)
+groupMumbo.add_argument('--MU_iter', metavar='INT', action='store', nargs=3,
+ help='Max number of iteration, min number of iteration, convergence threshold', type=float,
+ default=[1000, 300, 0.0005])
groupFusion = parser.add_argument_group('Fusion arguments')
groupFusion.add_argument('--FU_types', metavar='STRING', action='store',
@@ -151,16 +168,17 @@ if args.CL_type.split(":")==["Benchmark"]:
for fusionModulesName, fusionModule in zip(fusionModulesNames, fusionModules)]
fusionMethods = dict((fusionModulesName, [subclass.__name__ for subclass in fusionClasse.__subclasses__() ])
for fusionModulesName, fusionClasse in zip(fusionModulesNames, fusionClasses))
- fusionMonoviewClassifiers = [name for _, name, isPackage in
- pkgutil.iter_modules(['Multiview/Fusion/Methods/MonoviewClassifiers'])
- if not isPackage ]
+ allMonoviewAlgos = [name for _, name, isPackage in
+ pkgutil.iter_modules(['MonoviewClassifiers'])
+ if not isPackage]
+ fusionMonoviewClassifiers = allMonoviewAlgos
allFusionAlgos = {"Methods": fusionMethods, "Classifiers": fusionMonoviewClassifiers}
allMumboAlgos = [name for _, name, isPackage in
pkgutil.iter_modules(['Multiview/Mumbo/Classifiers'])
if not isPackage and not name in ["SubSampling", "ModifiedMulticlass", "Kover"]]
allMultiviewAlgos = {"Fusion": allFusionAlgos, "Mumbo": allMumboAlgos}
- allMonoviewAlgos = [key[15:] for key in dir(Monoview.ClassifMonoView) if key[:15] == "MonoviewClassif"]
benchmark = {"Monoview": allMonoviewAlgos, "Multiview" : allMultiviewAlgos}
+
if "Multiview" in args.CL_type.strip(":"):
benchmark["Multiview"] = {}
if "Mumbo" in args.CL_algos_multiview.split(":"):
@@ -178,138 +196,98 @@ if "Multiview" in args.CL_type.strip(":"):
if "Monoview" in args.CL_type.strip(":"):
benchmark["Monoview"] = args.CL_algos_monoview.split(":")
+
classifierTable = "a"
fusionClassifierConfig = "a"
fusionMethodConfig = "a"
mumboNB_ITER = 2
mumboClassifierConfig = "a"
mumboclassifierNames = "a"
-RandomForestKWARGS = {"classifier__n_estimators":map(int, args.CL_RF_trees.split())}
-SVCKWARGS = {"classifier__kernel":args.CL_SVC_kernel.split(":"), "classifier__C":map(int,args.CL_SVC_C.split(":"))}
-DecisionTreeKWARGS = {"classifier__max_depth":map(int,args.CL_DT_depth.split(":"))}
-SGDKWARGS = {"classifier__alpha" : map(float,args.CL_SGD_alpha.split(":")), "classifier__loss":args.CL_SGD_loss.split(":"),
- "classifier__penalty":args.CL_SGD_penalty.split(":")}
-KNNKWARGS = {"classifier__n_neighbors": map(float,args.CL_KNN_neigh.split(":"))}
-
-
-argumentDictionaries = {"Monoview":{}, "Multiview":[]}
-# if benchmark["Monoview"]:
-# for view in args.views.split(":"):
-# argumentDictionaries["Monoview"][str(view)] = []
-# for classifier in benchmark["Monoview"]:
-# arguments = {classifier+"KWARGS": globals()[classifier+"KWARGS"], "feat":view, "fileFeat": args.fileFeat,
-# "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": classifier,
-# classifier+"KWARGS": globals()[classifier+"KWARGS"]}
-# argumentDictionaries["Monoview"][str(view)].append(arguments)
-#
-# bestClassifiers = []
-# bestClassifiersConfigs = []
-# for viewIndex, viewArguments in enumerate(argumentDictionaries["Monoview"].values()):
-# resultsMonoview = Parallel(n_jobs=nbCores)(
-# delayed(ExecMonoview)(DATASET.get("View"+str(viewIndex)).value, DATASET.get("labels").value, args.name,
-# args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF, gridSearch=True,
-# **arguments)
-# for arguments in viewArguments)
-# accuracies = [result[1] for result in resultsMonoview]
-# classifiersNames = [result[0] for result in resultsMonoview]
-# classifiersConfigs = [result[2] for result in resultsMonoview]
-# bestClassifiers.append(classifiersNames[np.argmax(np.array(accuracies))])
-# bestClassifiersConfigs.append(classifiersConfigs[np.argmax(np.array(accuracies))])
-bestClassifiers = ["DecisionTree", "DecisionTree", "DecisionTree", "DecisionTree"]
-bestClassifiersConfigs = [["1"],["1"],["1"],["1"]]
+
+RandomForestKWARGS = {"0":map(int, args.CL_RF_trees.split())}
+SVMLinearKWARGS = {"0":map(int, args.CL_SVML_C.split(":"))}
+SVMRBFKWARGS = {"0":map(int, args.CL_SVMR_C.split(":"))}
+SVMPolyKWARGS = {"0":map(int, args.CL_SVMP_C.split(":")), '1':map(int, args.CL_SVMP_deg.split(":"))}
+DecisionTreeKWARGS = {"0":map(int, args.CL_DT_depth.split(":"))}
+SGDKWARGS = {"0": map(float, args.CL_SGD_alpha.split(":")), "1":args.CL_SGD_loss.split(":"),
+ "2": args.CL_SGD_penalty.split(":")}
+KNNKWARGS = {"0": map(float, args.CL_KNN_neigh.split(":"))}
+AdaboostKWARGS = {"0": args.CL_Ada_n_est.split(":"), "1": args.CL_Ada_b_est.split(":")}
+
+
+argumentDictionaries = {"Monoview": {}, "Multiview": []}
+if benchmark["Monoview"]:
+ for view in args.views.split(":"):
+ argumentDictionaries["Monoview"][str(view)] = []
+ for classifier in benchmark["Monoview"]:
+
+ arguments = {classifier+"KWARGS": globals()[classifier+"KWARGS"], "feat":view, "fileFeat": args.fileFeat,
+ "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": classifier}
+
+ argumentDictionaries["Monoview"][str(view)].append(arguments)
+bestClassifiers = []
+bestClassifiersConfigs = []
+for viewIndex, viewArguments in enumerate(argumentDictionaries["Monoview"].values()):
+ resultsMonoview = Parallel(n_jobs=nbCores)(
+ delayed(ExecMonoview)(DATASET.get("View"+str(viewIndex)).value, DATASET.get("labels").value, args.name,
+ args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF, gridSearch=True,
+ **arguments)
+ for arguments in viewArguments)
+
+ accuracies = [result[1] for result in resultsMonoview]
+ classifiersNames = [result[0] for result in resultsMonoview]
+ classifiersConfigs = [result[2] for result in resultsMonoview]
+ bestClassifiers.append(classifiersNames[np.argmax(np.array(accuracies))])
+ bestClassifiersConfigs.append(classifiersConfigs[np.argmax(np.array(accuracies))])
+# bestClassifiers = ["DecisionTree", "DecisionTree", "DecisionTree", "DecisionTree"]
+# bestClassifiersConfigs = [["1"],["1"],["1"],["1"]]
+
if benchmark["Multiview"]:
+ if benchmark["Multiview"]["Mumbo"]:
+ for classifier in benchmark["Multiview"]["Mumbo"]:
+ arguments = {"CL_type": "Mumbo",
+ "views": args.views.split(":"),
+ "NB_VIEW": len(args.views.split(":")),
+ "NB_CLASS": len(args.CL_classes.split(":")),
+ "LABELS_NAMES": args.CL_classes.split(":"),
+ "MumboKWARGS": {"classifiersNames": ["DecisionTree", "DecisionTree", "DecisionTree",
+ "DecisionTree"],
+ "maxIter":int(args.MU_iter[0]), "minIter":int(args.MU_iter[1]),
+ "threshold":args.MU_iter[2]}}
+ argumentDictionaries["Multiview"].append(arguments)
if benchmark["Multiview"]["Fusion"]:
if benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"] and benchmark["Multiview"]["Fusion"]["Classifiers"]:
for method in benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"]:
- arguments = {"CL_type": "Fusion",
- "views": args.views.split(":"),
- "NB_VIEW": len(args.views.split(":")),
- "NB_CLASS": len(args.CL_classes.split(":")),
- "LABELS_NAMES": args.CL_classes.split(":"),
- "FusionKWARGS": {"fusionType":"LateFusion", "fusionMethod":method,
- "classifiersNames": bestClassifiers,
- "classifiersConfigs": bestClassifiersConfigs,
- 'fusionMethodConfig': fusionMethodConfig},
- "MumboKWARGS":""}
- argumentDictionaries["Multiview"].append(arguments)
+ arguments = {"CL_type": "Fusion",
+ "views": args.views.split(":"),
+ "NB_VIEW": len(args.views.split(":")),
+ "NB_CLASS": len(args.CL_classes.split(":")),
+ "LABELS_NAMES": args.CL_classes.split(":"),
+ "FusionKWARGS": {"fusionType":"LateFusion", "fusionMethod":method,
+ "classifiersNames": bestClassifiers,
+ "classifiersConfigs": bestClassifiersConfigs,
+ 'fusionMethodConfig': fusionMethodConfig}}
+ argumentDictionaries["Multiview"].append(arguments)
if benchmark["Multiview"]["Fusion"]["Methods"]["EarlyFusion"] and benchmark["Multiview"]["Fusion"]["Classifiers"]:
for method in benchmark["Multiview"]["Fusion"]["Methods"]["EarlyFusion"]:
for classifier in benchmark["Multiview"]["Fusion"]["Classifiers"]:
arguments = {"CL_type": "Fusion",
- "views": args.views.split(":"),
- "NB_VIEW": len(args.views.split(":")),
- "NB_CLASS": len(args.CL_classes.split(":")),
- "LABELS_NAMES": args.CL_classes.split(":"),
- "FusionKWARGS": {"fusionType":"EarlyFusion", "fusionMethod":method,
- "classifiersNames": classifier,
- "classifiersConfigs": fusionClassifierConfig,
- 'fusionMethodConfig': fusionMethodConfig},
- "MumboKWARGS":""}
+ "views": args.views.split(":"),
+ "NB_VIEW": len(args.views.split(":")),
+ "NB_CLASS": len(args.CL_classes.split(":")),
+ "LABELS_NAMES": args.CL_classes.split(":"),
+ "FusionKWARGS": {"fusionType":"EarlyFusion", "fusionMethod":method,
+ "classifiersNames": classifier,
+ "classifiersConfigs": fusionClassifierConfig,
+ 'fusionMethodConfig': fusionMethodConfig}}
argumentDictionaries["Multiview"].append(arguments)
- if benchmark["Multiview"]["Mumbo"]:
- #for classifier in benchmark["Multiview"]["Mumbo"]:
- for i in range(int(np.power(len(args.views.split(":")), len(benchmark["Multiview"]["Mumbo"])))):
- arguments = {"CL_type": "Mumbo",
- "views": args.views.split(":"),
- "NB_VIEW": len(args.views.split(":")),
- "NB_CLASS": len(args.CL_classes.split(":")),
- "LABELS_NAMES": args.CL_classes.split(":"),
- "MumboKWARGS": {"classifiersConfigs": mumboClassifierConfig,"NB_ITER": mumboNB_ITER,
- "classifiersNames": ["DecisionTree", "DecisionTree", "DecisionTree", "DecisionTree"]},
- "FusionKWARGS": ""}
- argumentDictionaries["Multiview"].append(arguments)
resultsMultiview = Parallel(n_jobs=nbCores)(
delayed(ExecMultiview)(DATASET, args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
LABELS_DICTIONARY, gridSearch=True, **arguments)
for arguments in argumentDictionaries["Multiview"])
-# for classifierType, argumentsList in argumentDictionaries.iteritems():
-# executionMethod = globals()["Exec"+classifierType]
-# results[classifierType] = Parallel(n_jobs=args.CL_cores)(delayed(executionMethod)
-# (args.name, args.CL_split,args.CL_nbFolds, 1, args.type,
-# args.pathF, **arguments)
-# for arguments in argumentsList)
resultAnalysis(benchmark, results)
print len(argumentDictionaries["Multiview"]), len(argumentDictionaries["Monoview"])
-
-# views = args.views.split(":")
-# dataBaseType = args.type
-# NB_VIEW = len(views)
-# mumboClassifierConfig = [argument.split(':') for argument in args.MU_config]
-#
-# LEARNING_RATE = args.CL_split
-# nbFolds = args.CL_nbFolds
-# NB_CLASS = args.CL_nb_class
-# LABELS_NAMES = args.CL_classes.split(":")
-# mumboclassifierNames = args.MU_type.split(':')
-# mumboNB_ITER = args.MU_iter
-# NB_CORES = args.CL_cores
-# fusionClassifierNames = args.FU_cl_names.split(":")
-# fusionClassifierConfig = [argument.split(':') for argument in args.FU_cl_config]
-# fusionMethodConfig = [argument.split(':') for argument in args.FU_method_config]
-# FusionKWARGS = {"fusionType":args.FU_type, "fusionMethod":args.FU_method,
-# "monoviewClassifiersNames":fusionClassifierNames, "monoviewClassifiersConfigs":fusionClassifierConfig,
-# 'fusionMethodConfig':fusionMethodConfig}
-# MumboKWARGS = {"classifiersConfigs":mumboClassifierConfig, "NB_ITER":mumboNB_ITER, "classifiersNames":mumboclassifierNames}
-# directory = os.path.dirname(os.path.abspath(__file__)) + "/Results/"
-# logFileName = time.strftime("%Y%m%d-%H%M%S") + "-CMultiV-" + args.CL_type + "-" + "_".join(views) + "-" + args.name + \
-# "-LOG"
-# logFile = directory + logFileName
-# if os.path.isfile(logFile + ".log"):
-# for i in range(1, 20):
-# testFileName = logFileName + "-" + str(i) + ".log"
-# if not (os.path.isfile(directory + testFileName)):
-# logfile = directory + testFileName
-# break
-# else:
-# logFile += ".log"
-# logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', filename=logFile, level=logging.DEBUG,
-# filemode='w')
-# if args.log:
-# logging.getLogger().addHandler(logging.StreamHandler())
-#
-# ExecMultiview(views, dataBaseType, args, NB_VIEW, LEARNING_RATE, nbFolds, NB_CLASS, LABELS_NAMES, NB_CORES,
-# MumboKWARGS, FusionKWARGS)
\ No newline at end of file
diff --git a/Code/FeatExtraction/DBCrawl.py b/Code/MonoMutliViewClassifiers/FeatExtraction/DBCrawl.py
similarity index 100%
rename from Code/FeatExtraction/DBCrawl.py
rename to Code/MonoMutliViewClassifiers/FeatExtraction/DBCrawl.py
diff --git a/Code/FeatExtraction/ExecFeatExtraction.py b/Code/MonoMutliViewClassifiers/FeatExtraction/ExecFeatExtraction.py
similarity index 100%
rename from Code/FeatExtraction/ExecFeatExtraction.py
rename to Code/MonoMutliViewClassifiers/FeatExtraction/ExecFeatExtraction.py
diff --git a/Code/FeatExtraction/ExecFeatParaOpt.py b/Code/MonoMutliViewClassifiers/FeatExtraction/ExecFeatParaOpt.py
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rename from Code/FeatExtraction/ExecFeatParaOpt.py
rename to Code/MonoMutliViewClassifiers/FeatExtraction/ExecFeatParaOpt.py
diff --git a/Code/FeatExtraction/FeatExtraction.py b/Code/MonoMutliViewClassifiers/FeatExtraction/FeatExtraction.py
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rename from Code/FeatExtraction/FeatExtraction.py
rename to Code/MonoMutliViewClassifiers/FeatExtraction/FeatExtraction.py
diff --git a/Code/FeatExtraction/FeatParaOpt.py b/Code/MonoMutliViewClassifiers/FeatExtraction/FeatParaOpt.py
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rename from Code/FeatExtraction/FeatParaOpt.py
rename to Code/MonoMutliViewClassifiers/FeatExtraction/FeatParaOpt.py
diff --git a/Code/FeatExtraction/Results-FeatExtr/2016_03_24-FE-Caltech-ClassLabels-Description.csv b/Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatExtr/2016_03_24-FE-Caltech-ClassLabels-Description.csv
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diff --git a/Code/FeatExtraction/Results-FeatExtr/2016_03_24-FE-Caltech-ClassLabels.csv b/Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatExtr/2016_03_24-FE-Caltech-ClassLabels.csv
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diff --git a/Code/FeatExtraction/Results-FeatExtr/2016_03_24-FE-Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_20-Maxiter_100.csv b/Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatExtr/2016_03_24-FE-Caltech-HOG-CellDimension_5-nbOrientaions_8-nbClusters_20-Maxiter_100.csv
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diff --git a/Code/FeatExtraction/Results-FeatExtr/2016_03_24-FE-Caltech-HSV-Bins_[16,16,16]-Norm_Distr.csv b/Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatExtr/2016_03_24-FE-Caltech-HSV-Bins_[16,16,16]-Norm_Distr.csv
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diff --git a/Code/FeatExtraction/Results-FeatExtr/2016_03_24-FE-Caltech-RGB_HSV_SIFT_SURF_HOG-LOG.log b/Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatExtr/2016_03_24-FE-Caltech-RGB_HSV_SIFT_SURF_HOG-LOG.log
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rename from Code/FeatExtraction/Results-FeatParaOpt/SURF-2-40-8/2016_03_23-FPO-Caltech-SURF-SURF_Cluster-TotalTime.png
rename to Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatParaOpt/SURF-2-40-8/2016_03_23-FPO-Caltech-SURF-SURF_Cluster-TotalTime.png
diff --git a/Code/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/2016_01_28-Results-SURF.csv b/Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/2016_01_28-Results-SURF.csv
similarity index 100%
rename from Code/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/2016_01_28-Results-SURF.csv
rename to Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/2016_01_28-Results-SURF.csv
diff --git a/Code/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/Console-Output.txt b/Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/Console-Output.txt
similarity index 100%
rename from Code/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/Console-Output.txt
rename to Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/Console-Output.txt
diff --git a/Code/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreClassificationTime.png b/Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreClassificationTime.png
similarity index 100%
rename from Code/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreClassificationTime.png
rename to Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreClassificationTime.png
diff --git a/Code/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreFeatExtractionTime.png b/Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreFeatExtractionTime.png
similarity index 100%
rename from Code/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreFeatExtractionTime.png
rename to Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreFeatExtractionTime.png
diff --git a/Code/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreTotalTime.png b/Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreTotalTime.png
similarity index 100%
rename from Code/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreTotalTime.png
rename to Code/MonoMutliViewClassifiers/FeatExtraction/Results-FeatParaOpt/SURF-50-200-4/ScoreTotalTime.png
diff --git a/Code/FeatExtraction/__init__.py b/Code/MonoMutliViewClassifiers/FeatExtraction/__init__.py
similarity index 100%
rename from Code/FeatExtraction/__init__.py
rename to Code/MonoMutliViewClassifiers/FeatExtraction/__init__.py
diff --git a/Code/FeatExtraction/hog_extraction.py b/Code/MonoMutliViewClassifiers/FeatExtraction/hog_extraction.py
similarity index 100%
rename from Code/FeatExtraction/hog_extraction.py
rename to Code/MonoMutliViewClassifiers/FeatExtraction/hog_extraction.py
diff --git a/Code/FeatExtraction/hog_extraction_parallelized.py b/Code/MonoMutliViewClassifiers/FeatExtraction/hog_extraction_parallelized.py
similarity index 100%
rename from Code/FeatExtraction/hog_extraction_parallelized.py
rename to Code/MonoMutliViewClassifiers/FeatExtraction/hog_extraction_parallelized.py
diff --git a/Code/Monoview/ClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ClassifMonoView.py
similarity index 90%
rename from Code/Monoview/ClassifMonoView.py
rename to Code/MonoMutliViewClassifiers/Monoview/ClassifMonoView.py
index d5b246b1..ebbad0fc 100644
--- a/Code/Monoview/ClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ClassifMonoView.py
@@ -168,16 +168,27 @@ def MonoviewClassifRandomForest(X_train, y_train, nbFolds=4, nbCores=1, **kwargs
return description, rf_detector
-def MonoviewClassifSVC(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
- pipeline_SVC = Pipeline([('classifier', sklearn.svm.SVC())])
- param_SVC = kwargs
+def MonoviewClassifSVMLinear(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
+ pipeline_SVMLinear = Pipeline([('classifier', sklearn.svm.SVC())])
+ param_SVMLinear = kwargs
- grid_SVC = GridSearchCV(pipeline_SVC, param_grid=param_SVC, refit=True, n_jobs=nbCores, scoring='accuracy',
+ grid_SVMLinear = GridSearchCV(pipeline_SVMLinear, param_grid=param_SVMLinear, refit=True, n_jobs=nbCores, scoring='accuracy',
cv=nbFolds)
- SVC_detector = grid_SVC.fit(X_train, y_train)
- desc_params = [SVC_detector.best_params_["classifier__C"], SVC_detector.best_params_["classifier__kernel"]]
+ SVMLinear_detector = grid_SVMLinear.fit(X_train, y_train)
+ desc_params = [SVMLinear_detector.best_params_["classifier__C"]]
description = "Classif_" + "SVC" + "-" + "CV_" + str(nbFolds) + "-" + "-".join(map(str,desc_params))
- return description, SVC_detector
+ return description, SVMLinear_detector
+
+def MonoviewClassifSVMRBF(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
+ pipeline_SVMRBF = Pipeline([('classifier', sklearn.svm.SVC())])
+ param_SVMRBF = kwargs
+
+ grid_SVMRBF = GridSearchCV(pipeline_SVMRBF, param_grid=param_SVMRBF, refit=True, n_jobs=nbCores, scoring='accuracy',
+ cv=nbFolds)
+ SVMRBF_detector = grid_SVMRBF.fit(X_train, y_train)
+ desc_params = [SVMRBF_detector.best_params_["classifier__C"]]
+ description = "Classif_" + "SVC" + "-" + "CV_" + str(nbFolds) + "-" + "-".join(map(str,desc_params))
+ return description, SVMRBF_detector
def MonoviewClassifDecisionTree(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
diff --git a/Code/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
similarity index 62%
rename from Code/Monoview/ExecClassifMonoView.py
rename to Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index 04a271e7..55d4e86f 100644
--- a/Code/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -19,7 +19,7 @@ import h5py
# Import own modules
import ClassifMonoView # Functions for classification
import ExportResults # Functions to render results
-
+import MonoviewClassifiers
# Author-Info
__author__ = "Nikolas Huelsmann, Baptiste BAUVIN"
@@ -58,10 +58,10 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
logging.debug("Start:\t Classification")
- classifierFunction = getattr(ClassifMonoView, "MonoviewClassif"+CL_type)
+ classifierModule = getattr(MonoviewClassifiers, CL_type)
+ classifierFunction = getattr(classifierModule, "fit_gridsearch")
- cl_desc, cl_res = classifierFunction(X_train, y_train, nbFolds=nbFolds, nbCores=nbCores,
- **classifierKWARGS)
+ cl_desc, cl_res = classifierFunction(X_train, y_train, nbFolds=nbFolds, nbCores=nbCores,**classifierKWARGS)
t_end = time.time() - t_start
# Add result to Results DF
@@ -91,37 +91,38 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
#Accuracy classification score
accuracy_score = ExportResults.accuracy_score(y_test, y_test_pred)
-
- # 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"
- logging.debug("\n" + str(metrics.classification_report(y_test, y_test_pred, labels = range(0,len(classLabelsDesc.name)), target_names=classLabelsNamesList)))
- scores_df = ExportResults.classification_report_df(directory, filename, y_test, y_test_pred, range(0, len(classLabelsDesc.name)), classLabelsNamesList)
-
- # Create some useful statistcs
- logging.debug("Info:\t Statistics:")
- filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-Stats"
- stats_df = ExportResults.classification_stats(directory, filename, scores_df, accuracy_score)
- logging.debug("\n" + stats_df.to_string())
-
- # Confusion Matrix
- logging.debug("Info:\t Calculate Confusionmatrix")
- filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-ConfMatrix"
- df_conf_norm = ExportResults.confusion_matrix_df(directory, filename, y_test, y_test_pred, classLabelsNamesList)
- filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-ConfMatrixImg"
- ExportResults.plot_confusion_matrix(directory, filename, df_conf_norm)
-
- logging.debug("Done:\t Statistic Results")
-
-
- # Plot Result
- logging.debug("Start:\t Plot Result")
- np_score = ExportResults.calcScorePerClass(y_test, cl_res.predict(X_test).astype(int))
- ### directory and filename the same as CSV Export
- filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-Score"
- ExportResults.showResults(directory, filename, name, feat, np_score)
- logging.debug("Done:\t Plot Result")
- return [CL_type, accuracy_score, cl_desc]
+ logging.info("Accuracy :" +str(accuracy_score))
+
+ # # 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"
+ # logging.debug("\n" + str(metrics.classification_report(y_test, y_test_pred, labels = range(0,len(classLabelsDesc.name)), target_names=classLabelsNamesList)))
+ # scores_df = ExportResults.classification_report_df(directory, filename, y_test, y_test_pred, range(0, len(classLabelsDesc.name)), classLabelsNamesList)
+ #
+ # # Create some useful statistcs
+ # logging.debug("Info:\t Statistics:")
+ # filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-Stats"
+ # stats_df = ExportResults.classification_stats(directory, filename, scores_df, accuracy_score)
+ # logging.debug("\n" + stats_df.to_string())
+ #
+ # # Confusion Matrix
+ # logging.debug("Info:\t Calculate Confusionmatrix")
+ # filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-ConfMatrix"
+ # df_conf_norm = ExportResults.confusion_matrix_df(directory, filename, y_test, y_test_pred, classLabelsNamesList)
+ # filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-ConfMatrixImg"
+ # ExportResults.plot_confusion_matrix(directory, filename, df_conf_norm)
+ #
+ # logging.debug("Done:\t Statistic Results")
+ #
+ #
+ # # Plot Result
+ # logging.debug("Start:\t Plot Result")
+ # np_score = ExportResults.calcScorePerClass(y_test, cl_res.predict(X_test).astype(int))
+ # ### directory and filename the same as CSV Export
+ # filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-Score"
+ # ExportResults.showResults(directory, filename, name, feat, np_score)
+ # logging.debug("Done:\t Plot Result")
+ # return [CL_type, accuracy_score, cl_desc]
if __name__=='__main__':
@@ -131,7 +132,7 @@ if __name__=='__main__':
groupStandard = parser.add_argument_group('Standard arguments')
groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
- groupStandard.add_argument('--type', metavar='STRING', action='store', help='Type of Dataset', default="hdf5")
+ groupStandard.add_argument('--type', metavar='STRING', action='store', help='Type of Dataset', default=".hdf5")
groupStandard.add_argument('--name', metavar='STRING', action='store', help='Name of Database (default: %(default)s)', default='DB')
groupStandard.add_argument('--feat', metavar='STRING', action='store', help='Name of Feature for Classification (default: %(default)s)', default='RGB')
groupStandard.add_argument('--pathF', metavar='STRING', action='store', help='Path to the views (default: %(default)s)', default='Results-FeatExtr/')
@@ -147,34 +148,39 @@ if __name__=='__main__':
groupClass.add_argument('--CL_split', metavar='FLOAT', action='store', help='Split ratio for train and test', type=float, default=0.9)
- groupRF = parser.add_argument_group('Random Forest arguments')
- groupRF.add_argument('--CL_RF_trees', metavar='STRING', action='store', help='GridSearch: Determine the trees', default='25 75 125 175')
-
- groupSVC = parser.add_argument_group('SVC arguments')
- groupSVC.add_argument('--CL_SVC_kernel', metavar='STRING', action='store', help='GridSearch : Kernels used', default='linear')
- groupSVC.add_argument('--CL_SVC_C', metavar='STRING', action='store', help='GridSearch : Penalty parameters used', default='1:10:100:1000')
+ groupClassifier = parser.add_argument_group('Classifier Config')
+ groupClassifier.add_argument('--CL_config', metavar='STRING', nargs="+", action='store', help='GridSearch: Determine the trees', default=['25:75:125:175'])
- groupRF = parser.add_argument_group('Decision Trees arguments')
- groupRF.add_argument('--CL_DT_depth', metavar='STRING', action='store', help='GridSearch: Determine max depth for Decision Trees', default='1:3:5:7')
-
- groupSGD = parser.add_argument_group('SGD arguments')
- groupSGD.add_argument('--CL_SGD_alpha', metavar='STRING', action='store', help='GridSearch: Determine alpha for SGDClassifier', default='0.1:0.2:0.5:0.9')
- groupSGD.add_argument('--CL_SGD_loss', metavar='STRING', action='store', help='GridSearch: Determine loss for SGDClassifier', default='log')
- groupSGD.add_argument('--CL_SGD_penalty', metavar='STRING', action='store', help='GridSearch: Determine penalty for SGDClassifier', default='l2')
+ # groupSVMLinear = parser.add_argument_group('SVC arguments')
+ # groupSVMLinear.add_argument('--CL_SVML_C', metavar='STRING', action='store', help='GridSearch : Penalty parameters used', default='1:10:100:1000')
+ #
+ # groupSVMRBF = parser.add_argument_group('SVC arguments')
+ # groupSVMRBF.add_argument('--CL_SVMR_C', metavar='STRING', action='store', help='GridSearch : Penalty parameters used', default='1:10:100:1000')
+ #
+ # groupRF = parser.add_argument_group('Decision Trees arguments')
+ # groupRF.add_argument('--CL_DT_depth', metavar='STRING', action='store', help='GridSearch: Determine max depth for Decision Trees', default='1:3:5:7')
+ #
+ # groupSGD = parser.add_argument_group('SGD')
+ # groupSGD.add_argument('--CL_SGD_alpha', metavar='STRING', action='store', help='GridSearch: Determine alpha for SGDClassifier', default='0.1:0.2:0.5:0.9')
+ # groupSGD.add_argument('--CL_SGD_loss', metavar='STRING', action='store', help='GridSearch: Determine loss for SGDClassifier', default='log')
+ # groupSGD.add_argument('--CL_SGD_penalty', metavar='STRING', action='store', help='GridSearch: Determine penalty for SGDClassifier', default='l2')
args = parser.parse_args()
- RandomForestKWARGS = {"classifier__n_estimators":map(int, args.CL_RF_trees.split())}
- SVCKWARGS = {"classifier__kernel":args.CL_SVC_kernel.split(":"), "classifier__C":map(int,args.CL_SVC_C.split(":"))}
- DecisionTreeKWARGS = {"classifier__max_depth":map(int,args.CL_DT_depth.split(":"))}
- SGDKWARGS = {"classifier__alpha" : map(float,args.CL_SGD_alpha.split(":")), "classifier__loss":args.CL_SGD_loss.split(":"),
- "classifier__penalty":args.CL_SGD_penalty.split(":")}
+
+ # RandomForestKWARGS = {"classifier__n_estimators":map(int, args.CL_RF_trees.split())}
+ # SVMLinearKWARGS = {"classifier__C":map(int,args.CL_SVML_C.split(":"))}
+ # SVMRBFKWARGS = {"classifier__C":map(int,args.CL_SVMR_C.split(":"))}
+ # DecisionTreeKWARGS = {"classifier__max_depth":map(int,args.CL_DT_depth.split(":"))}
+ # SGDKWARGS = {"classifier__alpha" : map(float,args.CL_SGD_alpha.split(":")), "classifier__loss":args.CL_SGD_loss.split(":"),
+ # "classifier__penalty":args.CL_SGD_penalty.split(":")}
+ classifierKWARGS = dict((key, value) for key, value in enumerate([arg.split(":") for arg in args.CL_config]))
### Main Programm
# Configure Logger
directory = os.path.dirname(os.path.abspath(__file__)) + "/Results-ClassMonoView/"
- logfilename= datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + args.name + "-" + args.feat + "-LOG"
+ logfilename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + args.name + "-" + args.feat + "-LOG"
logfile = directory + logfilename
if os.path.isfile(logfile + ".log"):
for i in range(1,20):
@@ -194,7 +200,7 @@ if __name__=='__main__':
# Read the features
logging.debug("Start:\t Read " + args.type + " Files")
- if args.databaseType == ".csv":
+ if args.type == ".csv":
X = np.genfromtxt(args.pathF + args.fileFeat, delimiter=';')
Y = np.genfromtxt(args.pathF + args.fileCL, delimiter=';')
elif args.type == ".hdf5":
@@ -206,7 +212,6 @@ if __name__=='__main__':
logging.debug("Info:\t Shape of Feature:" + str(X.shape) + ", Length of classLabels vector:" + str(Y.shape))
logging.debug("Done:\t Read CSV Files")
- arguments = {"RandomForestKWARGS": RandomForestKWARGS, "SVCKWARGS": SVCKWARGS,
- "DecisionTreeKWARGS": DecisionTreeKWARGS, "SGDKWARGS": SGDKWARGS, "feat":args.feat,
- "fileFeat": args.fileFeat, "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": args.CL_type}
+ arguments = {args.CL_type+"KWARGS": classifierKWARGS, "feat":args.feat,"fileFeat": args.fileFeat,
+ "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": args.CL_type}
ExecMonoview(X, Y, args.name, args.CL_split, args.CL_CV, args.CL_Cores, args.type, args.pathF, **arguments)
diff --git a/Code/Monoview/ExecPlot.py b/Code/MonoMutliViewClassifiers/Monoview/ExecPlot.py
similarity index 100%
rename from Code/Monoview/ExecPlot.py
rename to Code/MonoMutliViewClassifiers/Monoview/ExecPlot.py
diff --git a/Code/Monoview/ExportResults.py b/Code/MonoMutliViewClassifiers/Monoview/ExportResults.py
similarity index 100%
rename from Code/Monoview/ExportResults.py
rename to Code/MonoMutliViewClassifiers/Monoview/ExportResults.py
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-1.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-1.log
similarity index 100%
rename from Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-1.log
rename to Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-1.log
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-2.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-2.log
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rename from Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-2.log
rename to Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-2.log
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-3.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-3.log
similarity index 100%
rename from Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-3.log
rename to Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-3.log
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-4.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-4.log
similarity index 100%
rename from Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-4.log
rename to Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-4.log
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-5.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-5.log
similarity index 100%
rename from Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-5.log
rename to Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-5.log
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-6.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-6.log
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rename from Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-6.log
rename to Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-6.log
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-7.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-7.log
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rename from Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-7.log
rename to Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG-7.log
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG.log
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rename from Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG.log
rename to Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq-LOG.log
diff --git a/Code/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_19-CMV-MultiOmicDataset-RNASeq.csv
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similarity index 100%
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-1.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-1.csv
new file mode 100644
index 00000000..ed7e4d76
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+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-1.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.714285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.5
+4;Mean of F1-Score of top 10 classes by F1-Score;0.41666666666666663
+5;Mean of F1-Score of top 20 classes by F1-Score;0.41666666666666663
+6;Mean of F1-Score of top 30 classes by F1-Score;0.41666666666666663
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-2.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-2.csv
new file mode 100644
index 00000000..1c2dbd1e
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+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-2.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.514285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.42176870748299317
+5;Mean of F1-Score of top 20 classes by F1-Score;0.42176870748299317
+6;Mean of F1-Score of top 30 classes by F1-Score;0.42176870748299317
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-3.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-3.csv
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-4.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-4.csv
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-5.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-5.csv
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+;Statistic;Values
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-6.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-6.csv
new file mode 100644
index 00000000..d62c8a0e
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@@ -0,0 +1,8 @@
+;Statistic;Values
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-7.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-7.csv
new file mode 100644
index 00000000..81997a60
--- /dev/null
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@@ -0,0 +1,8 @@
+;Statistic;Values
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+1;Top 10 classes by F1-Score;['Non', 'Oui']
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-8.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-8.csv
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-9.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats-9.csv
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Clinic-Stats.csv
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Score.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Score.png
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-1.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-1.csv
new file mode 100644
index 00000000..ed7e4d76
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-1.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.714285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.5
+4;Mean of F1-Score of top 10 classes by F1-Score;0.41666666666666663
+5;Mean of F1-Score of top 20 classes by F1-Score;0.41666666666666663
+6;Mean of F1-Score of top 30 classes by F1-Score;0.41666666666666663
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-2.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-2.csv
new file mode 100644
index 00000000..fb3b57b1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-2.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.685714285714
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.5
+4;Mean of F1-Score of top 10 classes by F1-Score;0.4067796610169492
+5;Mean of F1-Score of top 20 classes by F1-Score;0.4067796610169492
+6;Mean of F1-Score of top 30 classes by F1-Score;0.4067796610169492
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-3.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-3.csv
new file mode 100644
index 00000000..12e7af6d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-3.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.885714285714
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7666666666666666
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7666666666666666
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7666666666666666
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-4.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-4.csv
new file mode 100644
index 00000000..27f52192
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-4.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.314285714286
+1;Top 10 classes by F1-Score;['Oui', 'Non']
+2;Worst 10 classes by F1-Score;['Non', 'Oui']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.3092105263157895
+5;Mean of F1-Score of top 20 classes by F1-Score;0.3092105263157895
+6;Mean of F1-Score of top 30 classes by F1-Score;0.3092105263157895
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-5.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-5.csv
new file mode 100644
index 00000000..410f8862
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-5.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.828571428571
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.6982758620689655
+5;Mean of F1-Score of top 20 classes by F1-Score;0.6982758620689655
+6;Mean of F1-Score of top 30 classes by F1-Score;0.6982758620689655
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-6.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-6.csv
new file mode 100644
index 00000000..af2aaa26
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-6.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.714285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.5949074074074074
+5;Mean of F1-Score of top 20 classes by F1-Score;0.5949074074074074
+6;Mean of F1-Score of top 30 classes by F1-Score;0.5949074074074074
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-7.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-7.csv
new file mode 100644
index 00000000..cfd6be91
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats-7.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.657142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.5
+4;Mean of F1-Score of top 10 classes by F1-Score;0.39655172413793105
+5;Mean of F1-Score of top 20 classes by F1-Score;0.39655172413793105
+6;Mean of F1-Score of top 30 classes by F1-Score;0.39655172413793105
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats.csv
new file mode 100644
index 00000000..27249616
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-Methyl-Stats.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.942857142857
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.9388111888111887
+5;Mean of F1-Score of top 20 classes by F1-Score;0.9388111888111887
+6;Mean of F1-Score of top 30 classes by F1-Score;0.9388111888111887
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-1.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-1.csv
new file mode 100644
index 00000000..a13e8201
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-1.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.458333333333;0.0909090909091;0.342857142857
+Oui;0.0625;0.363636363636;0.157142857143
+All;0.520833333333;0.454545454545;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-10.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-10.csv
new file mode 100644
index 00000000..23380cfc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-10.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.431034482759;0.333333333333;0.414285714286
+Oui;0.0172413793103;0.416666666667;0.0857142857143
+All;0.448275862069;0.75;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-11.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-11.csv
new file mode 100644
index 00000000..ec20de4e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-11.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.451612903226;0.375;0.442857142857
+Oui;0.0161290322581;0.375;0.0571428571429
+All;0.467741935484;0.75;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-12.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-12.csv
new file mode 100644
index 00000000..0d9e7845
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-12.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.454545454545;0.0769230769231;0.314285714286
+Oui;0.0681818181818;0.384615384615;0.185714285714
+All;0.522727272727;0.461538461538;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-13.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-13.csv
new file mode 100644
index 00000000..29d47356
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-13.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.466666666667;0.2;0.428571428571
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-14.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-14.csv
new file mode 100644
index 00000000..edbbae5e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-14.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.410714285714;0.357142857143;0.4
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-15.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-15.csv
new file mode 100644
index 00000000..9027b6be
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-15.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.428571428571;0.285714285714;0.4
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-16.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-16.csv
new file mode 100644
index 00000000..07d5e788
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-16.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-17.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-17.csv
new file mode 100644
index 00000000..404ee277
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-17.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.333333333333;0.5625;0.385714285714
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-18.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-18.csv
new file mode 100644
index 00000000..ee02a26e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrix-18.csv
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrixImg-7.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrixImg-7.png
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrixImg-9.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-ConfMatrixImg-9.png
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Score-19.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Score-19.png
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Score-5.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Score-5.png
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Score-6.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Score-6.png
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Score.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Score.png
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-1.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-1.csv
new file mode 100644
index 00000000..81f0fd88
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-1.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.857142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8299319727891157
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8299319727891157
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8299319727891157
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-10.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-10.csv
new file mode 100644
index 00000000..133d2853
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-10.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.857142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7878787878787878
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7878787878787878
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7878787878787878
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-11.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-11.csv
new file mode 100644
index 00000000..12e7af6d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-11.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.885714285714
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7666666666666666
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7666666666666666
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7666666666666666
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-12.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-12.csv
new file mode 100644
index 00000000..bf630270
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-12.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.857142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8444444444444446
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8444444444444446
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8444444444444446
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-13.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-13.csv
new file mode 100644
index 00000000..b0824652
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-13.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.914285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8382126348228043
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8382126348228043
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8382126348228043
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-14.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-14.csv
new file mode 100644
index 00000000..9f142c0d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-14.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.857142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8194014447884417
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8194014447884417
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8194014447884417
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-15.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-15.csv
new file mode 100644
index 00000000..441e24d8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-15.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.828571428571
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7569444444444445
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7569444444444445
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7569444444444445
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-16.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-16.csv
new file mode 100644
index 00000000..43c2dad0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-16.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.8
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7733580018501388
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7733580018501388
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7733580018501388
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-17.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-17.csv
new file mode 100644
index 00000000..211b411f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-17.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.742857142857
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.72
+5;Mean of F1-Score of top 20 classes by F1-Score;0.72
+6;Mean of F1-Score of top 30 classes by F1-Score;0.72
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-18.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-18.csv
new file mode 100644
index 00000000..11c46e22
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-18.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.828571428571
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7569444444444444
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7569444444444444
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7569444444444444
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-19.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-19.csv
new file mode 100644
index 00000000..9f142c0d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-19.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.857142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8194014447884417
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8194014447884417
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8194014447884417
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-2.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-2.csv
new file mode 100644
index 00000000..5dbd75e8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-2.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.828571428571
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8011363636363638
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8011363636363638
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8011363636363638
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-3.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-3.csv
new file mode 100644
index 00000000..4d47e5bf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-3.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.8
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7619047619047619
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7619047619047619
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7619047619047619
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-4.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-4.csv
new file mode 100644
index 00000000..c557de1f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-4.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.885714285714
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8776223776223775
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8776223776223775
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8776223776223775
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-5.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-5.csv
new file mode 100644
index 00000000..2e90b7ca
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-5.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.942857142857
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8994252873563219
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8994252873563219
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8994252873563219
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-6.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-6.csv
new file mode 100644
index 00000000..9ac22152
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-6.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.914285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8916408668730651
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8916408668730651
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8916408668730651
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-7.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-7.csv
new file mode 100644
index 00000000..bb35623c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-7.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.771428571429
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7008547008547008
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7008547008547008
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7008547008547008
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-8.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-8.csv
new file mode 100644
index 00000000..2711eaca
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-8.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.8
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7822222222222223
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7822222222222223
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7822222222222223
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-9.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-9.csv
new file mode 100644
index 00000000..3cfd0b10
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats-9.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.942857142857
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8994252873563218
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8994252873563218
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8994252873563218
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats.csv
new file mode 100644
index 00000000..bf630270
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-MiRNA_-Stats.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.857142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8444444444444446
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8444444444444446
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8444444444444446
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-1.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-1.csv
new file mode 100644
index 00000000..3ab1dc38
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-1.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.481481481481;0.0625;0.385714285714
+Oui;0.0185185185185;0.4375;0.114285714286
+All;0.5;0.5;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-10.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-10.csv
new file mode 100644
index 00000000..8d80fe42
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-10.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.452380952381;0.0714285714286;0.3
+Oui;0.166666666667;0.25;0.2
+All;0.619047619048;0.321428571429;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-11.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-11.csv
new file mode 100644
index 00000000..509b1a44
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-11.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.452380952381;0.0714285714286;0.3
+Oui;0.0714285714286;0.392857142857;0.2
+All;0.52380952381;0.464285714286;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-12.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-12.csv
new file mode 100644
index 00000000..16efe97d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-12.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.413793103448;0.416666666667;0.414285714286
+Oui;0.0344827586207;0.333333333333;0.0857142857143
+All;0.448275862069;0.75;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-13.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-13.csv
new file mode 100644
index 00000000..1505ccf2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-13.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.5;;0.4
+Oui;0.125;;0.1
+All;0.625;;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-2.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-2.csv
new file mode 100644
index 00000000..fdf750bb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-2.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.48275862069;0.0833333333333;0.414285714286
+Oui;0.0344827586207;0.333333333333;0.0857142857143
+All;0.51724137931;0.416666666667;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-3.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-3.csv
new file mode 100644
index 00000000..67a20bf2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-3.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.3;0.5;0.357142857143
+Oui;0.0;0.5;0.142857142857
+All;0.3;1.0;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-4.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-4.csv
new file mode 100644
index 00000000..f344179c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-4.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.366666666667;0.8;0.428571428571
+Oui;0.0333333333333;0.3;0.0714285714286
+All;0.4;1.1;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-5.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-5.csv
new file mode 100644
index 00000000..695d8c1e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-5.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.5;;0.357142857143
+Oui;0.2;;0.142857142857
+All;0.7;;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-6.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-6.csv
new file mode 100644
index 00000000..18266185
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-6.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.4375;0.136363636364;0.342857142857
+Oui;0.0833333333333;0.318181818182;0.157142857143
+All;0.520833333333;0.454545454545;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-7.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-7.csv
new file mode 100644
index 00000000..509a2121
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-7.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.46;0.1;0.357142857143
+Oui;0.08;0.3;0.142857142857
+All;0.54;0.4;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-8.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-8.csv
new file mode 100644
index 00000000..e42443f5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-8.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.462962962963;0.125;0.385714285714
+Oui;0.0925925925926;0.1875;0.114285714286
+All;0.555555555556;0.3125;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-9.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-9.csv
new file mode 100644
index 00000000..853c41ef
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix-9.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
+Non;0.480769230769;0.0555555555556;0.371428571429
+Oui;0.0384615384615;0.388888888889;0.128571428571
+All;0.519230769231;0.444444444444;0.5
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix.csv
new file mode 100644
index 00000000..af24be5b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrix.csv
@@ -0,0 +1,4 @@
+;Non;Oui;All
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+Oui;0.104166666667;0.272727272727;0.157142857143
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrixImg-1.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrixImg-1.png
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrixImg-4.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrixImg-4.png
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrixImg-8.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-ConfMatrixImg-8.png
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Score-12.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Score-12.png
new file mode 100644
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diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-1.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-1.csv
new file mode 100644
index 00000000..8ac6cb3b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-1.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.942857142857
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.9189814814814814
+5;Mean of F1-Score of top 20 classes by F1-Score;0.9189814814814814
+6;Mean of F1-Score of top 30 classes by F1-Score;0.9189814814814814
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-10.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-10.csv
new file mode 100644
index 00000000..a3cebc60
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-10.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.742857142857
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7086031452358927
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7086031452358927
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7086031452358927
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-11.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-11.csv
new file mode 100644
index 00000000..cd139990
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-11.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.857142857143
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8492678725236864
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8492678725236864
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8492678725236864
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-12.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-12.csv
new file mode 100644
index 00000000..cadf36d5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-12.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.8
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.703030303030303
+5;Mean of F1-Score of top 20 classes by F1-Score;0.703030303030303
+6;Mean of F1-Score of top 30 classes by F1-Score;0.703030303030303
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-13.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-13.csv
new file mode 100644
index 00000000..32d51bf0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-13.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.8
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.5
+4;Mean of F1-Score of top 10 classes by F1-Score;0.4444444444444445
+5;Mean of F1-Score of top 20 classes by F1-Score;0.4444444444444445
+6;Mean of F1-Score of top 30 classes by F1-Score;0.4444444444444445
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-2.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-2.csv
new file mode 100644
index 00000000..b0824652
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-2.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.914285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8382126348228043
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8382126348228043
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8382126348228043
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-3.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-3.csv
new file mode 100644
index 00000000..6084f669
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-3.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.714285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7083333333333333
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7083333333333333
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7083333333333333
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-4.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-4.csv
new file mode 100644
index 00000000..e0b61f24
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-4.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.714285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.5949074074074073
+5;Mean of F1-Score of top 20 classes by F1-Score;0.5949074074074073
+6;Mean of F1-Score of top 30 classes by F1-Score;0.5949074074074073
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-5.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-5.csv
new file mode 100644
index 00000000..ed7e4d76
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-5.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.714285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.5
+4;Mean of F1-Score of top 10 classes by F1-Score;0.41666666666666663
+5;Mean of F1-Score of top 20 classes by F1-Score;0.41666666666666663
+6;Mean of F1-Score of top 30 classes by F1-Score;0.41666666666666663
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-6.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-6.csv
new file mode 100644
index 00000000..4d47e5bf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-6.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.8
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7619047619047619
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7619047619047619
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7619047619047619
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-7.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-7.csv
new file mode 100644
index 00000000..e0555824
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-7.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.828571428571
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7756410256410255
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7756410256410255
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7756410256410255
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-8.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-8.csv
new file mode 100644
index 00000000..b5d750fc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-8.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.8
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.6693657219973009
+5;Mean of F1-Score of top 20 classes by F1-Score;0.6693657219973009
+6;Mean of F1-Score of top 30 classes by F1-Score;0.6693657219973009
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-9.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-9.csv
new file mode 100644
index 00000000..b80ac1fd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats-9.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.914285714286
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.8834628190899001
+5;Mean of F1-Score of top 20 classes by F1-Score;0.8834628190899001
+6;Mean of F1-Score of top 30 classes by F1-Score;0.8834628190899001
diff --git a/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats.csv
new file mode 100644
index 00000000..aaf58c08
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/2016_08_24-CMV-MultiOmic-RNASeq-Stats.csv
@@ -0,0 +1,8 @@
+;Statistic;Values
+0;Accuracy score on test;0.8
+1;Top 10 classes by F1-Score;['Non', 'Oui']
+2;Worst 10 classes by F1-Score;['Oui', 'Non']
+3;Ratio of classes with F1-Score==0 of all classes;0.0
+4;Mean of F1-Score of top 10 classes by F1-Score;0.7471620227038183
+5;Mean of F1-Score of top 20 classes by F1-Score;0.7471620227038183
+6;Mean of F1-Score of top 30 classes by F1-Score;0.7471620227038183
diff --git a/Code/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-ConfMatrix.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-ConfMatrix.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-ConfMatrixImg.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-ConfMatrixImg.png
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diff --git a/Code/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-LOG.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-LOG.log
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diff --git a/Code/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-Report.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-Report.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-Score.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-Score.png
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diff --git a/Code/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-Stats.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG-Stats.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HOG-Cluster_20/2016_03_24-CMV-Caltech-HOG.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-ConfMatrix.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-ConfMatrix.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-ConfMatrixImg.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-ConfMatrixImg.png
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diff --git a/Code/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-LOG.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-LOG.log
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diff --git a/Code/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-Report.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-Report.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-Score.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-Score.png
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diff --git a/Code/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-Stats.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV-Stats.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/HSV-Bins_[16,16,16]/2016_03_24-CMV-Caltech-HSV.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-ConfMatrix.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-ConfMatrix.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-ConfMatrixImg.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-ConfMatrixImg.png
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Report.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Report.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Score.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Score.png
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Stats.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Stats.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-3classes-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-ConfMatrix.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-ConfMatrix.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-ConfMatrixImg.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-ConfMatrixImg.png
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-LOG.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-LOG.log
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Report.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Report.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Score.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Score.png
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Stats.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB-Stats.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/RGB-Bins_16-CI_256/2016_03_24-CMV-Caltech-RGB.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/SIFT-Cluster_35/2016_03_24-CMV-Caltech-SIFT-ConfMatrix.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SIFT-Cluster_35/2016_03_24-CMV-Caltech-SIFT-ConfMatrix.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/SIFT-Cluster_35/2016_03_24-CMV-Caltech-SIFT-ConfMatrixImg.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SIFT-Cluster_35/2016_03_24-CMV-Caltech-SIFT-ConfMatrixImg.png
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diff --git a/Code/Monoview/Results-ClassMonoView/SIFT-Cluster_35/2016_03_24-CMV-Caltech-SIFT-Report.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SIFT-Cluster_35/2016_03_24-CMV-Caltech-SIFT-Report.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/SIFT-Cluster_35/2016_03_24-CMV-Caltech-SIFT-Score.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SIFT-Cluster_35/2016_03_24-CMV-Caltech-SIFT-Score.png
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diff --git a/Code/Monoview/Results-ClassMonoView/SIFT-Cluster_35/2016_03_24-CMV-Caltech-SIFT-Stats.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SIFT-Cluster_35/2016_03_24-CMV-Caltech-SIFT-Stats.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF-ConfMatrix.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF-ConfMatrix.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF-LOG.log b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF-LOG.log
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diff --git a/Code/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF-Report.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF-Report.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF-Score.png b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF-Score.png
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diff --git a/Code/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF-Stats.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF-Stats.csv
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diff --git a/Code/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF.csv b/Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF.csv
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rename from Code/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF.csv
rename to Code/MonoMutliViewClassifiers/Monoview/Results-ClassMonoView/SURF-Cluster_30/2016_03_24-CMV-Caltech-SURF.csv
diff --git a/Code/Monoview/__init__.py b/Code/MonoMutliViewClassifiers/Monoview/__init__.py
similarity index 100%
rename from Code/Monoview/__init__.py
rename to Code/MonoMutliViewClassifiers/Monoview/__init__.py
diff --git a/Code/Monoview/run.py b/Code/MonoMutliViewClassifiers/Monoview/run.py
similarity index 100%
rename from Code/Monoview/run.py
rename to Code/MonoMutliViewClassifiers/Monoview/run.py
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py
new file mode 100644
index 00000000..d1c45281
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py
@@ -0,0 +1,28 @@
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.grid_search import GridSearchCV
+from sklearn.tree import DecisionTreeClassifier
+
+
+def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
+ num_estimators = int(kwargs['0'])
+ base_estimators = int(kwargs['1'])
+ classifier = AdaBoostClassifier(n_estimators=num_estimators, base_estimator=base_estimators)
+ classifier.fit(DATASET, CLASS_LABELS)
+ return classifier
+
+
+def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
+
+ pipeline = Pipeline([('classifier', AdaBoostClassifier())])
+ param= {"classifier__n_estimators": map(int, kwargs['0']),
+ "classifier__base_estimator": [DecisionTreeClassifier() for arg in kwargs["1"]]}
+ grid = GridSearchCV(pipeline,param_grid=param,refit=True,n_jobs=nbCores,scoring='accuracy',cv=nbFolds)
+ detector = grid.fit(X_train, y_train)
+ desc_estimators = [detector.best_params_["classifier__n_estimators"]]
+ description = "Classif_" + "RF" + "-" + "CV_" + str(nbFolds) + "-" + "Trees_" + str(map(str,desc_estimators))
+ return description, detector
+
+
+def getConfig(config):
+ return "\n\t\t- Adaboost with num_esimators : "+config[0]+", base_estimators : "+config[1]
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/DecisionTree.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/DecisionTree.py
new file mode 100644
index 00000000..8fe4de8d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/DecisionTree.py
@@ -0,0 +1,26 @@
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.pipeline import Pipeline # Pipelining in classification
+from sklearn.grid_search import GridSearchCV
+
+
+def fit(DATASET, CLASS_LABELS, NB_CORES=1, **kwargs):
+ maxDepth = int(kwargs['0'])
+ classifier = DecisionTreeClassifier(max_depth=maxDepth)
+ classifier.fit(DATASET, CLASS_LABELS)
+ return classifier
+
+
+def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
+ pipeline_DT = Pipeline([('classifier', DecisionTreeClassifier())])
+ param_DT = {"classifier__max_depth":map(int, kwargs['0'])}
+
+ grid_DT = GridSearchCV(pipeline_DT, param_grid=param_DT, refit=True, n_jobs=nbCores, scoring='accuracy',
+ cv=nbFolds)
+ DT_detector = grid_DT.fit(X_train, y_train)
+ desc_params = [DT_detector.best_params_["classifier__max_depth"]]
+ description = "Classif_" + "DT" + "-" + "CV_" + str(nbFolds) + "-" + "-".join(map(str,desc_params))
+ return description, DT_detector
+
+
+def getConfig(config):
+ return "\n\t\t- Decision Tree with max_depth : "+config[0]
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py
new file mode 100644
index 00000000..ae03c355
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py
@@ -0,0 +1,26 @@
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.pipeline import Pipeline # Pipelining in classification
+from sklearn.grid_search import GridSearchCV
+
+
+def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
+ nNeighbors = int(kwargs['0'])
+ classifier = KNeighborsClassifier(n_neighbors=nNeighbors)
+ classifier.fit(DATASET, CLASS_LABELS)
+ return classifier
+
+
+def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
+ pipeline_KNN = Pipeline([('classifier', KNeighborsClassifier())])
+ param_KNN = {"classifier__n_neighbors": map(int, kwargs['0'])}
+ grid_KNN = GridSearchCV(pipeline_KNN, param_grid=param_KNN, refit=True, n_jobs=nbCores, scoring='accuracy',
+ cv=nbFolds)
+ KNN_detector = grid_KNN.fit(X_train, y_train)
+ desc_params = [KNN_detector.best_params_["classifier__n_neighbors"]]
+ description = "Classif_" + "Lasso" + "-" + "CV_" + str(nbFolds) + "-" + "-".join(map(str,desc_params))
+ return description, KNN_detector
+
+
+
+def getConfig(config):
+ return "\n\t\t- K nearest Neighbors with n_neighbors: "+config[0]
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py
new file mode 100644
index 00000000..968d83d2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py
@@ -0,0 +1,46 @@
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.grid_search import GridSearchCV
+
+
+def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
+ num_estimators = int(kwargs['0'])
+ maxDepth = int(kwargs['1'])
+ classifier = RandomForestClassifier(n_estimators=num_estimators, max_depth=maxDepth, n_jobs=NB_CORES)
+ classifier.fit(DATASET, CLASS_LABELS)
+ return classifier
+
+
+def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
+
+ # PipeLine with RandomForest classifier
+ pipeline_rf = Pipeline([('classifier', RandomForestClassifier())])
+
+ # Parameters for GridSearch: Number of Trees
+ # can be extended with: oob_score, min_samples_leaf, max_features
+ param_rf = {"classifier__n_estimators": map(int, kwargs['0'])}
+
+ # pipeline: Gridsearch avec le pipeline comme estimator
+ # param: pour obtenir le meilleur model il va essayer tous les possiblites
+ # refit: pour utiliser le meilleur model apres girdsearch
+ # n_jobs: Nombre de CPU (Mon ordi a des problemes avec -1 (Bug Python 2.7 sur Windows))
+ # scoring: scoring...
+ # cv: Nombre de K-Folds pour CV
+ grid_rf = GridSearchCV(
+ pipeline_rf,
+ param_grid=param_rf,
+ refit=True,
+ n_jobs=nbCores,
+ scoring='accuracy',
+ cv=nbFolds,
+ )
+
+ rf_detector = grid_rf.fit(X_train, y_train)
+
+ desc_estimators = [rf_detector.best_params_["classifier__n_estimators"]]
+ description = "Classif_" + "RF" + "-" + "CV_" + str(nbFolds) + "-" + "Trees_" + str(map(str,desc_estimators))
+ return description, rf_detector
+
+
+def getConfig(config):
+ return "\n\t\t- Random Forest with num_esimators : "+config[0]+", max_depth : "+config[1]
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py
new file mode 100644
index 00000000..3a2bc27f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py
@@ -0,0 +1,32 @@
+from sklearn.linear_model import SGDClassifier
+from sklearn.pipeline import Pipeline # Pipelining in classification
+from sklearn.grid_search import GridSearchCV
+
+
+def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
+ loss = kwargs['0']
+ penalty = kwargs['1']
+ try:
+ alpha = int(kwargs['2'])
+ except:
+ alpha = 0.15
+ classifier = SGDClassifier(loss=loss, penalty=penalty, alpha=alpha)
+ classifier.fit(DATASET, CLASS_LABELS)
+ return classifier
+
+
+def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
+ pipeline_SGD = Pipeline([('classifier', SGDClassifier())])
+ param_SGD = {"classifier__loss": kwargs['1'], "classifier__penalty": kwargs['2'],
+ "classifier__alpha": map(float, kwargs['0'])}
+ grid_SGD = GridSearchCV(pipeline_SGD, param_grid=param_SGD, refit=True, n_jobs=nbCores, scoring='accuracy',
+ cv=nbFolds)
+ SGD_detector = grid_SGD.fit(X_train, y_train)
+ desc_params = [SGD_detector.best_params_["classifier__loss"], SGD_detector.best_params_["classifier__penalty"],
+ SGD_detector.best_params_["classifier__alpha"]]
+ description = "Classif_" + "Lasso" + "-" + "CV_" + str(nbFolds) + "-" + "-".join(map(str,desc_params))
+ return description, SGD_detector
+
+
+def getConfig(config):
+ return "\n\t\t- SGDClassifier with loss : "+config[0]+", penalty : "+config[1]
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py
new file mode 100644
index 00000000..568badb4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py
@@ -0,0 +1,25 @@
+from sklearn.svm import SVC
+from sklearn.pipeline import Pipeline # Pipelining in classification
+from sklearn.grid_search import GridSearchCV
+
+
+def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
+ C = int(kwargs['0'])
+ classifier = SVC(C=C, kernel='linear', probability=True)
+ classifier.fit(DATASET, CLASS_LABELS)
+ return classifier
+
+
+def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
+ pipeline_SVMLinear = Pipeline([('classifier', SVC(kernel="linear"))])
+ param_SVMLinear = {"classifier__C": map(int, kwargs['0'])}
+ grid_SVMLinear = GridSearchCV(pipeline_SVMLinear, param_grid=param_SVMLinear, refit=True, n_jobs=nbCores, scoring='accuracy',
+ cv=nbFolds)
+ SVMLinear_detector = grid_SVMLinear.fit(X_train, y_train)
+ desc_params = [SVMLinear_detector.best_params_["classifier__C"]]
+ description = "Classif_" + "SVC" + "-" + "CV_" + str(nbFolds) + "-" + "-".join(map(str,desc_params))
+ return description, SVMLinear_detector
+
+
+def getConfig(config):
+ return "\n\t\t- SVM with C : "+config[0]+", kernel : "+config[1]
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py
new file mode 100644
index 00000000..9f43f0b9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py
@@ -0,0 +1,26 @@
+from sklearn.svm import SVC
+from sklearn.pipeline import Pipeline # Pipelining in classification
+from sklearn.grid_search import GridSearchCV
+
+
+def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
+ C = int(kwargs['0'])
+ degree = int(kwargs['1'])
+ classifier = SVC(C=C, kernel='poly', degree=degree, probability=True)
+ classifier.fit(DATASET, CLASS_LABELS)
+ return classifier
+
+
+def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
+ pipeline_SVMLinear = Pipeline([('classifier', SVC(kernel="linear"))])
+ param_SVMLinear = {"classifier__C": map(int, kwargs['0']), "classifier__degree": map(int, kwargs["1"])}
+ grid_SVMLinear = GridSearchCV(pipeline_SVMLinear, param_grid=param_SVMLinear, refit=True, n_jobs=nbCores, scoring='accuracy',
+ cv=nbFolds)
+ SVMLinear_detector = grid_SVMLinear.fit(X_train, y_train)
+ desc_params = [SVMLinear_detector.best_params_["classifier__C"], SVMLinear_detector.best_params_["classifier__degree"]]
+ description = "Classif_" + "SVC" + "-" + "CV_" + str(nbFolds) + "-" + "-".join(map(str,desc_params))
+ return description, SVMLinear_detector
+
+
+def getConfig(config):
+ return "\n\t\t- SVM with C : "+config[0]+", kernel : "+config[1]
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py
new file mode 100644
index 00000000..202cc076
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py
@@ -0,0 +1,25 @@
+from sklearn.svm import SVC
+from sklearn.pipeline import Pipeline # Pipelining in classification
+from sklearn.grid_search import GridSearchCV
+
+
+def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
+ C = int(kwargs['0'])
+ classifier = SVC(C=C, kernel='rbf', probability=True)
+ classifier.fit(DATASET, CLASS_LABELS)
+ return classifier
+
+
+def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, **kwargs):
+ pipeline_SVMRBF = Pipeline([('classifier', SVC(kernel="rbf"))])
+ param_SVMRBF = {"classifier__C": map(int, kwargs['0'])}
+ grid_SVMRBF = GridSearchCV(pipeline_SVMRBF, param_grid=param_SVMRBF, refit=True, n_jobs=nbCores, scoring='accuracy',
+ cv=nbFolds)
+ SVMRBF_detector = grid_SVMRBF.fit(X_train, y_train)
+ desc_params = [SVMRBF_detector.best_params_["classifier__C"]]
+ description = "Classif_" + "SVC" + "-" + "CV_" + str(nbFolds) + "-" + "-".join(map(str,desc_params))
+ return description, SVMRBF_detector
+
+
+def getConfig(config):
+ return "\n\t\t- SVM with C : "+config[0]+", kernel : "+config[1]
\ No newline at end of file
diff --git a/Code/Multiview/Fusion/Methods/MonoviewClassifiers/__init__.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/__init__.py
similarity index 100%
rename from Code/Multiview/Fusion/Methods/MonoviewClassifiers/__init__.py
rename to Code/MonoMutliViewClassifiers/MonoviewClassifiers/__init__.py
diff --git a/Code/Multiview/ExecMultiview.py b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
similarity index 93%
rename from Code/Multiview/ExecMultiview.py
rename to Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
index ebc61a5b..cffd6f01 100644
--- a/Code/Multiview/ExecMultiview.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
@@ -15,6 +15,7 @@ import logging
import time
+
def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, path, LABELS_DICTIONARY, gridSearch=False, **kwargs):
datasetLength = DATASET.get("Metadata").attrs["datasetLength"]
@@ -26,8 +27,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
views = kwargs["views"]
NB_VIEW = kwargs["NB_VIEW"]
LABELS_NAMES = kwargs["LABELS_NAMES"]
- MumboKWARGS = kwargs["MumboKWARGS"]
- FusionKWARGS = kwargs["FusionKWARGS"]
+ classificationKWARGS = kwargs[CL_type+"KWARGS"]
t_start = time.time()
logging.info("### Main Programm for Multiview Classification")
@@ -78,8 +78,8 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
if gridSearch:
logging.info("Start:\t Gridsearching best settings for monoview classifiers")
- bestSettings = classifierGridSearch(DATASET, initKWARGS["classifiersNames"])
- initKWARGS["classifiersConfigs"] = bestSettings
+ bestSettings = classifierGridSearch(DATASET, classificationKWARGS["classifiersNames"])
+ classificationKWARGS["classifiersConfigs"] = bestSettings
logging.info("Done:\t Gridsearching best settings for monoview classifiers")
# Begin Classification
@@ -89,7 +89,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
logging.info("\tStart:\t Fold number " + str(foldIdx + 1))
trainIndices = [index for index in range(datasetLength) if index not in fold]
DATASET_LENGTH = len(trainIndices)
- classifier = classifierClass(NB_VIEW, DATASET_LENGTH, DATASET.get("labels").value, NB_CORES=nbCores, **initKWARGS)
+ classifier = classifierClass(NB_VIEW, DATASET_LENGTH, DATASET.get("labels").value, NB_CORES=nbCores, **classificationKWARGS)
classifier.fit_hdf5(DATASET, trainIndices=trainIndices)
kFoldClassifier.append(classifier)
@@ -149,6 +149,9 @@ if __name__=='__main__':
parser = argparse.ArgumentParser(
description='This file is used to classifiy multiview data thanks to three methods : Fusion (early & late), Multiview Machines, Mumbo.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ # create the top-level parser
+
+
groupStandard = parser.add_argument_group('Standard arguments')
groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
@@ -183,12 +186,14 @@ if __name__=='__main__':
help='Determine which monoview classifier to use with Mumbo',
default='DecisionTree:DecisionTree:DecisionTree:DecisionTree')
groupMumbo.add_argument('--MU_config', metavar='STRING', action='store', nargs='+',
- help='Configuration for the monoview classifier in Mumbo', default=['1:0.02', '1:0.018', '1:0.1',
+ help='Configuration for the monoview classifier in Mumbo', default=['1:0.02', '1:0.018',
+ '1:0.1',
'2:0.09'])
- groupMumbo.add_argument('--MU_iter', metavar='INT', action='store',
- help='Number of iterations in Mumbos learning process', type=int, default=5)
+ groupMumbo.add_argument('--MU_iter', metavar='INT', action='store', nargs=3,
+ help='Max number of iteration, min number of iteration, convergeance threshold', type=float,
+ default=[1000,300,0.0005])
- groupFusion = parser.add_argument_group('Fusion arguments')
+ groupFusion = parser.add_argument_group('Fusion', "poulet")
groupFusion.add_argument('--FU_type', metavar='STRING', action='store',
help='Determine which type of fusion to use', default='LateFusion')
groupFusion.add_argument('--FU_method', metavar='STRING', action='store',
@@ -201,8 +206,9 @@ if __name__=='__main__':
groupFusion.add_argument('--FU_cl_config', metavar='STRING', action='store', nargs='+',
help='Configuration for the monoview classifiers used', default=['3:4', 'log:l2', '10:linear',
'4'])
-
+ print parser
args = parser.parse_args()
+ print args
views = args.views.split(":")
dataBaseType = args.type
NB_VIEW = len(views)
@@ -213,7 +219,6 @@ if __name__=='__main__':
NB_CLASS = args.CL_nb_class
LABELS_NAMES = args.CL_classes.split(":")
mumboclassifierNames = args.MU_type.split(':')
- mumboNB_ITER = args.MU_iter
NB_CORES = args.CL_cores
fusionClassifierNames = args.FU_cl_names.split(":")
fusionClassifierConfig = [argument.split(':') for argument in args.FU_cl_config]
@@ -221,7 +226,9 @@ if __name__=='__main__':
FusionKWARGS = {"fusionType":args.FU_type, "fusionMethod":args.FU_method,
"classifiersNames":fusionClassifierNames, "classifiersConfigs":fusionClassifierConfig,
'fusionMethodConfig':fusionMethodConfig}
- MumboKWARGS = {"classifiersConfigs":mumboClassifierConfig, "NB_ITER":mumboNB_ITER, "classifiersNames":mumboclassifierNames}
+ MumboKWARGS = {"classifiersConfigs":mumboClassifierConfig,
+ "classifiersNames":mumboclassifierNames, "maxIter":int(args.MU_iter[0]),
+ "minIter":int(args.MU_iter[1]), "threshold":args.MU_iter[2]}
dir = os.path.dirname(os.path.abspath(__file__)) + "/Results/"
logFileName = time.strftime("%Y%m%d-%H%M%S") + "-CMultiV-" + args.CL_type + "-" + "_".join(views) + "-" + args.name + \
"-LOG"
@@ -252,10 +259,10 @@ if __name__=='__main__':
DATASET, LABELS_DICTIONARY = getDatabase(views, args.pathF, args.name, NB_CLASS, LABELS_NAMES)
logging.info("Info:\t Labels used: " + ", ".join(LABELS_DICTIONARY.values()))
- logging.info("Info:\t Length of dataset:" + str(DATASET.get("Metadata").attrs["datasetlength"]))
+ logging.info("Info:\t Length of dataset:" + str(DATASET.get("Metadata").attrs["datasetLength"]))
ExecMultiview(DATASET, args.name, args.CL_split, args.CL_nbFolds, args.CL_cores, args.type, args.pathF,
- LABELS_DICTIONARY, gridSearch=True, **arguments)
+ LABELS_DICTIONARY, gridSearch=False, **arguments)
diff --git a/Code/Multiview/Fusion/Fusion.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
similarity index 100%
rename from Code/Multiview/Fusion/Fusion.py
rename to Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
diff --git a/Code/Multiview/Fusion/Methods/EarlyFusion.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusion.py
similarity index 97%
rename from Code/Multiview/Fusion/Methods/EarlyFusion.py
rename to Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusion.py
index 761d1b4c..dd86041d 100644
--- a/Code/Multiview/Fusion/Methods/EarlyFusion.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusion.py
@@ -1,10 +1,10 @@
#!/usr/bin/env python
# -*- encoding: utf-8
-import MonoviewClassifiers
-
import numpy as np
+import MonoviewClassifiers
+
class EarlyFusionClassifier(object):
def __init__(self, monoviewClassifiersNames, monoviewClassifiersConfigs, NB_CORES=1):
@@ -56,7 +56,7 @@ class WeightedLinear(EarlyFusionClassifier):
def getConfig(self, fusionMethodConfig ,monoviewClassifiersNames, monoviewClassifiersConfigs):
configString = "with weighted concatenation, using weights : "+", ".join(map(str, self.weights))+\
" with monoview classifier : "
- monoviewClassifierModule = getattr(MonoviewClassifiers, monoviewClassifiersNames[0])
+ monoviewClassifierModule = getattr(poulet, monoviewClassifiersNames[0])
configString += monoviewClassifierModule.getConfig(monoviewClassifiersConfigs[0])
return configString
diff --git a/Code/Multiview/Fusion/Methods/LateFusion.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusion.py
similarity index 99%
rename from Code/Multiview/Fusion/Methods/LateFusion.py
rename to Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusion.py
index 962f51b0..9222d733 100644
--- a/Code/Multiview/Fusion/Methods/LateFusion.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusion.py
@@ -2,11 +2,11 @@
# -*- encoding: utf-8
import numpy as np
-import sys
-from sklearn.svm import SVC
+from joblib import Parallel, delayed
from sklearn.multiclass import OneVsOneClassifier
+from sklearn.svm import SVC
+
import MonoviewClassifiers
-from joblib import Parallel, delayed
# Our method in multiclass classification will be One-vs-One or One-vs-All
diff --git a/Code/Multiview/Fusion/Methods/__init__.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/__init__.py
similarity index 100%
rename from Code/Multiview/Fusion/Methods/__init__.py
rename to Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/__init__.py
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/poulet/__init__.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/poulet/__init__.py
new file mode 100644
index 00000000..9bbd76fb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/poulet/__init__.py
@@ -0,0 +1,7 @@
+import os
+for module in os.listdir(os.path.dirname(os.path.realpath(__file__))):
+ if module == '__init__.py' or module[-3:] != '.py':
+ continue
+ __import__(module[:-3], locals(), globals())
+del module
+del os
\ No newline at end of file
diff --git a/Code/Multiview/Fusion/__init__.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/__init__.py
similarity index 100%
rename from Code/Multiview/Fusion/__init__.py
rename to Code/MonoMutliViewClassifiers/Multiview/Fusion/__init__.py
diff --git a/Code/Multiview/Fusion/analyzeResults.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
similarity index 100%
rename from Code/Multiview/Fusion/analyzeResults.py
rename to Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
diff --git a/Code/Multiview/GetMultiviewDb.py b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
similarity index 99%
rename from Code/Multiview/GetMultiviewDb.py
rename to Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
index 8a9dfa82..b839496f 100644
--- a/Code/Multiview/GetMultiviewDb.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
@@ -145,7 +145,6 @@ def getKFoldIndices(nbFolds, CLASS_LABELS, NB_CLASS, learningIndices):
for foldIndex, fold in enumerate(nbTrainingExamples):
trainingExamplesIndices.append([])
while fold != [0 for i in range(NB_CLASS)]:
- print fold
index = random.randint(0, len(learningIndices)-1)
if learningIndices[index] not in usedIndices:
isUseFull, fold = isUseful(fold, learningIndices[index], CLASS_LABELS, labelDict)
@@ -327,7 +326,7 @@ def getMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
rnaseqData = np.genfromtxt(path+"matching_rnaseq.csv", delimiter=',')
rnaseqDset = datasetFile.create_dataset("View2", rnaseqData.shape)
rnaseqDset[...] = rnaseqData
- rnaseqDset.attrs["name"]="RANSeq"
+ rnaseqDset.attrs["name"]="RNASeq"
logging.debug("Done:\t Getting RNASeq Data")
logging.debug("Start:\t Getting Clinical Data")
@@ -376,7 +375,7 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
rnaseqData = np.genfromtxt(path+"matching_rnaseq.csv", delimiter=',')
rnaseqDset = datasetFile.create_dataset("View2", rnaseqData.shape)
rnaseqDset[...] = rnaseqData
- rnaseqDset.attrs["name"]="RANSeq_"
+ rnaseqDset.attrs["name"]="RNASeq_"
logging.debug("Done:\t Getting RNASeq Data")
logging.debug("Start:\t Getting Clinical Data")
diff --git a/Code/Multiview/Mumbo/Classifiers/DecisionTree.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/DecisionTree.py
similarity index 95%
rename from Code/Multiview/Mumbo/Classifiers/DecisionTree.py
rename to Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/DecisionTree.py
index 8a087b76..2981081d 100644
--- a/Code/Multiview/Mumbo/Classifiers/DecisionTree.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/DecisionTree.py
@@ -3,6 +3,7 @@ from sklearn.metrics import precision_recall_fscore_support, accuracy_score
import numpy as np
from ModifiedMulticlass import OneVsRestClassifier
from SubSampling import subSample
+import logging
# Add weights
def DecisionTree(data, labels, arg, weights):
@@ -105,8 +106,11 @@ def getBestSetting(bestSettings, bestResults):
diffTo52 = 100.0
bestSettingsIndex = 0
for resultIndex, result in enumerate(bestResults):
- if abs(52.5-result)<diffTo52:
+ if abs(0.55-result) < diffTo52:
+ diffTo52 = abs(0.55-result)
+ bestResult = result
bestSettingsIndex = resultIndex
+ logging.debug("\t\tInfo:\t Best Reslut : "+str(result))
return map(lambda p: round(p, 4), bestSettings[bestSettingsIndex])
-# return map(round(,4), bestSettings[bestSettingsIndex])
\ No newline at end of file
+ # return map(round(,4), bestSettings[bestSettingsIndex])
\ No newline at end of file
diff --git a/Code/Multiview/Mumbo/Classifiers/Kover.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/Kover.py
similarity index 100%
rename from Code/Multiview/Mumbo/Classifiers/Kover.py
rename to Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/Kover.py
diff --git a/Code/Multiview/Mumbo/Classifiers/ModifiedMulticlass.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/ModifiedMulticlass.py
similarity index 100%
rename from Code/Multiview/Mumbo/Classifiers/ModifiedMulticlass.py
rename to Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/ModifiedMulticlass.py
diff --git a/Code/Multiview/Mumbo/Classifiers/SubSampling.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/SubSampling.py
similarity index 100%
rename from Code/Multiview/Mumbo/Classifiers/SubSampling.py
rename to Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/SubSampling.py
diff --git a/Code/Multiview/Mumbo/Classifiers/__init__.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/__init__.py
similarity index 100%
rename from Code/Multiview/Mumbo/Classifiers/__init__.py
rename to Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/__init__.py
diff --git a/Code/Multiview/Mumbo/Mumbo.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py
similarity index 84%
rename from Code/Multiview/Mumbo/Mumbo.py
rename to Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py
index 912d6eba..20e1e961 100644
--- a/Code/Multiview/Mumbo/Mumbo.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py
@@ -2,7 +2,10 @@ import numpy as np
import math
from joblib import Parallel, delayed
from Classifiers import *
+import time
import logging
+import matplotlib.pyplot as plt
+from sklearn.metrics import accuracy_score
# Data shape : ((Views, Examples, Corrdinates))
@@ -23,8 +26,7 @@ def trainWeakClassifier(classifierName, monoviewDataset, CLASS_LABELS,
weights = computeWeights(DATASET_LENGTH, iterIndex, viewIndice, CLASS_LABELS, costMatrices)
classifierModule = globals()[classifierName] # Permet d'appeler une fonction avec une string
classifierMethod = getattr(classifierModule, classifierName)
- classifier, classes, isBad, pTr = classifierMethod(monoviewDataset, CLASS_LABELS, classifier_config, weights)
- averageAccuracy = np.mean(pTr)
+ classifier, classes, isBad, averageAccuracy = classifierMethod(monoviewDataset, CLASS_LABELS, classifier_config, weights)
logging.debug("\t\t\tView " + str(viewIndice) + " : " + str(averageAccuracy))
return classifier, classes, isBad, averageAccuracy
@@ -33,18 +35,19 @@ def trainWeakClassifier_hdf5(classifierName, monoviewDataset, CLASS_LABELS, DATA
weights = computeWeights(DATASET_LENGTH, iterIndex, viewIndice, CLASS_LABELS, costMatrices)
classifierModule = globals()[classifierName] # Permet d'appeler une fonction avec une string
classifierMethod = getattr(classifierModule, classifierName)
- classifier, classes, isBad, pTr = classifierMethod(monoviewDataset, CLASS_LABELS, classifier_config, weights)
- averageAccuracy = np.mean(pTr)
+ classifier, classes, isBad, averageAccuracy = classifierMethod(monoviewDataset, CLASS_LABELS, classifier_config, weights)
logging.debug("\t\t\tView " + str(viewIndice) + " : " + str(averageAccuracy))
return classifier, classes, isBad, averageAccuracy
def gridSearch_hdf5(DATASET, classifiersNames):
bestSettings = []
for classifierIndex, classifierName in enumerate(classifiersNames):
+ logging.debug("\tStart:\t Gridsearch for "+classifierName+" on "+DATASET.get("View"+str(classifierIndex)).attrs["name"])
classifierModule = globals()[classifierName] # Permet d'appeler une fonction avec une string
classifierMethod = getattr(classifierModule, "gridSearch")
bestSettings.append(classifierMethod(DATASET.get("View"+str(classifierIndex))[...],
DATASET.get("labels")[...]))
+ logging.debug("\tDone:\t Gridsearch for "+classifierName)
return bestSettings
@@ -54,43 +57,45 @@ def gridSearch_hdf5(DATASET, classifiersNames):
class Mumbo:
def __init__(self, NB_VIEW, DATASET_LENGTH, CLASS_LABELS, NB_CORES=1,**kwargs):
- self.nbIter = kwargs["NB_ITER"]
+ self.maxIter = kwargs["maxIter"]
+ self.minIter = kwargs["minIter"]
+ self.threshold = kwargs["threshold"]
self.classifiersNames = kwargs["classifiersNames"]
self.classifiersConfigs = kwargs["classifiersConfigs"]
nbClass = len(set(CLASS_LABELS))
- self.nbIter = kwargs["NB_ITER"]
self.costMatrices = np.array([
- np.array([
- np.array([
- np.array([1 if CLASS_LABELS[exampleIndice] != classe
- else -(nbClass - 1)
- for classe in range(nbClass)
- ]) for exampleIndice in range(DATASET_LENGTH)
- ]) for viewIndice in range(NB_VIEW)])
- if iteration == 0
- else np.zeros((NB_VIEW, DATASET_LENGTH, nbClass))
- for iteration in range(self.nbIter + 1)
- ])
+ np.array([
+ np.array([
+ np.array([1 if CLASS_LABELS[exampleIndice] != classe
+ else -(nbClass - 1)
+ for classe in range(nbClass)
+ ]) for exampleIndice in range(DATASET_LENGTH)
+ ]) for viewIndice in range(NB_VIEW)])
+ if iteration == 0
+ else np.zeros((NB_VIEW, DATASET_LENGTH, nbClass))
+ for iteration in range(self.maxIter + 1)
+ ])
self.generalCostMatrix = np.array([
np.array([
np.array([1 if CLASS_LABELS[exampleIndice] != classe
else -(nbClass - 1)
for classe in range(nbClass)
]) for exampleIndice in range(DATASET_LENGTH)
- ]) for iteration in range(self.nbIter)
+ ]) for iteration in range(self.maxIter)
])
- self.fs = np.zeros((self.nbIter, NB_VIEW, DATASET_LENGTH, nbClass))
- self.ds = np.zeros((self.nbIter, NB_VIEW, DATASET_LENGTH))
- self.edges = np.zeros((self.nbIter, NB_VIEW))
- self.alphas = np.zeros((self.nbIter, NB_VIEW))
- self.predictions = np.zeros((self.nbIter, NB_VIEW, DATASET_LENGTH))
- self.generalAlphas = np.zeros(self.nbIter)
- self.generalFs = np.zeros((self.nbIter, DATASET_LENGTH, nbClass))
+ self.fs = np.zeros((self.maxIter, NB_VIEW, DATASET_LENGTH, nbClass))
+ self.ds = np.zeros((self.maxIter, NB_VIEW, DATASET_LENGTH))
+ self.edges = np.zeros((self.maxIter, NB_VIEW))
+ self.alphas = np.zeros((self.maxIter, NB_VIEW))
+ self.predictions = np.zeros((self.maxIter, NB_VIEW, DATASET_LENGTH))
+ self.generalAlphas = np.zeros(self.maxIter)
+ self.generalFs = np.zeros((self.maxIter, DATASET_LENGTH, nbClass))
self.nbCores = NB_CORES
self.iterIndex = 0
self.bestClassifiers = []
- self.bestViews = np.zeros(self.nbIter, dtype=int)
- self.averageAccuracies = np.zeros((self.nbIter, NB_VIEW))
+ self.bestViews = np.zeros(self.maxIter, dtype=int)
+ self.averageAccuracies = np.zeros((self.maxIter, NB_VIEW))
+ self.iterAccuracies = np.zeros(self.maxIter)
# costMatrices = np.array([
# np.array([
# np.array([
@@ -134,17 +139,23 @@ class Mumbo:
# predictions, generalAlphas, generalFs = initialize(NB_CLASS, NB_VIEW,
# NB_ITER, DATASET_LENGTH,
# LABELS[trainIndices])
- bestViews = np.zeros(self.nbIter)
+ bestViews = np.zeros(self.maxIter)
bestClassifiers = []
# Learning
+ isStabilized=False
self.iterIndex = 0
- for i in range(self.nbIter):
+ while not isStabilized or self.iterIndex >= self.maxIter:
+ if self.iterIndex > self.minIter:
+ coeffs = np.polyfit(np.log(np.arange(self.iterIndex)+0.00001), self.iterAccuracies[:self.iterIndex], 1)
+ if coeffs[0]/self.iterIndex < self.threshold:
+ isStabilized = True
+
logging.debug('\t\tStart:\t Iteration ' + str(self.iterIndex + 1))
classifiers, predictedLabels, areBad = self.trainWeakClassifiers_hdf5(DATASET, trainIndices, NB_CLASS,
DATASET_LENGTH, NB_VIEW)
if areBad.all():
- logging.warning("All bad for iteration " + str(self.iterIndex))
+ logging.warning("WARNING:\tAll bad for iteration " + str(self.iterIndex))
self.predictions[self.iterIndex] = predictedLabels
@@ -169,6 +180,10 @@ class Mumbo:
self.bestClassifiers.append(classifiers[bestView])
self.updateGeneralFs(DATASET_LENGTH, NB_CLASS, bestView)
self.updateGeneralCostMatrix(DATASET_LENGTH, NB_CLASS,LABELS)
+ predictedLabels = self.predict_hdf5(DATASET, usedIndices=trainIndices)
+ accuracy = accuracy_score(DATASET.get("labels")[trainIndices], predictedLabels)
+ self.iterAccuracies[self.iterIndex] = accuracy
+
self.iterIndex += 1
# finalFs = computeFinalFs(DATASET_LENGTH, NB_CLASS, generalAlphas, predictions, bestViews, LABELS, NB_ITER)
@@ -184,7 +199,7 @@ class Mumbo:
for labelIndex, exampleIndex in enumerate(usedIndices):
votes = np.zeros(NB_CLASS)
for classifier, alpha, view in zip(self.bestClassifiers, self.alphas, self.bestViews):
- data = DATASET["/View"+str(int(view))+"/matrix"][exampleIndex, :]
+ data = DATASET["/View"+str(int(view))][exampleIndex, :]
votes[int(classifier.predict(np.array([data])))] += alpha[view]
predictedLabels[labelIndex] = np.argmax(votes)
else:
@@ -204,10 +219,10 @@ class Mumbo:
classifiersNames = self.classifiersNames
iterIndex = self.iterIndex
trainedClassifiersAndLabels = Parallel(n_jobs=NB_JOBS)(
- delayed(trainWeakClassifier)(classifiersNames[viewIndice], DATASET[viewIndice], CLASS_LABELS,
- DATASET_LENGTH, viewIndice, classifiersConfigs[viewIndice], iterIndex,
- costMatrices)
- for viewIndice in range(NB_VIEW))
+ delayed(trainWeakClassifier)(classifiersNames[viewIndice], DATASET[viewIndice], CLASS_LABELS,
+ DATASET_LENGTH, viewIndice, classifiersConfigs[viewIndice], iterIndex,
+ costMatrices)
+ for viewIndice in range(NB_VIEW))
for viewIndex, (classifier, labelsArray, isBad, averageAccuracy) in enumerate(trainedClassifiersAndLabels):
self.averageAccuracies[self.iterIndex, viewIndex] = averageAccuracy
@@ -217,7 +232,7 @@ class Mumbo:
return np.array(trainedClassifiers), np.array(labelsMatrix), np.array(areBad)
def trainWeakClassifiers_hdf5(self, DATASET, trainIndices, NB_CLASS,
- DATASET_LENGTH, NB_VIEW):
+ DATASET_LENGTH, NB_VIEW):
trainedClassifiers = []
labelsMatrix = []
areBad = []
@@ -230,13 +245,13 @@ class Mumbo:
classifiersNames = self.classifiersNames
iterIndex = self.iterIndex
trainedClassifiersAndLabels = Parallel(n_jobs=NB_JOBS)(
- delayed(trainWeakClassifier_hdf5)(classifiersNames[viewIndex],
- DATASET.get("View"+str(viewIndex))[trainIndices, :],
- DATASET.get("labels")[trainIndices],
- DATASET_LENGTH,
- viewIndex, classifiersConfigs[viewIndex],
- DATASET.get("View"+str(viewIndex)).attrs["name"], iterIndex, costMatrices)
- for viewIndex in range(NB_VIEW))
+ delayed(trainWeakClassifier_hdf5)(classifiersNames[viewIndex],
+ DATASET.get("View"+str(viewIndex))[trainIndices, :],
+ DATASET.get("labels")[trainIndices],
+ DATASET_LENGTH,
+ viewIndex, classifiersConfigs[viewIndex],
+ DATASET.get("View"+str(viewIndex)).attrs["name"], iterIndex, costMatrices)
+ for viewIndex in range(NB_VIEW))
for viewIndex, (classifier, labelsArray, isBad, averageAccuracy) in enumerate(trainedClassifiersAndLabels):
self.averageAccuracies[self.iterIndex, viewIndex] = averageAccuracy
@@ -250,11 +265,11 @@ class Mumbo:
costMatrix = self.costMatrices[self.iterIndex, viewIndex]
# return np.sum(np.array([np.sum(predictionMatrix*costMatrix[:,classIndice]) for classIndice in range(NB_CLASS)]))
cCost = float(np.sum(np.array(
- [costMatrix[exampleIndice, int(predictionMatrix[exampleIndice])] for exampleIndice in
- range(DATASET_LENGTH)])))
+ [costMatrix[exampleIndice, int(predictionMatrix[exampleIndice])] for exampleIndice in
+ range(DATASET_LENGTH)])))
tCost = float(np.sum(
- np.array([-costMatrix[exampleIndice, CLASS_LABELS[exampleIndice]] for exampleIndice in
- range(DATASET_LENGTH)])))
+ np.array([-costMatrix[exampleIndice, CLASS_LABELS[exampleIndice]] for exampleIndice in
+ range(DATASET_LENGTH)])))
if tCost == 0.:
self.edges[self.iterIndex, viewIndex] = -cCost
else:
@@ -283,13 +298,13 @@ class Mumbo:
== \
CLASS_LABELS[exampleIndice] \
or self.allViewsClassifyWell(self.predictions, pastIterIndice,
- NB_VIEW, CLASS_LABELS[exampleIndice],
- exampleIndice):
+ NB_VIEW, CLASS_LABELS[exampleIndice],
+ exampleIndice):
self.ds[pastIterIndice, viewIndice, exampleIndice] = 1
else:
self.ds[pastIterIndice, viewIndice, exampleIndice] = 0
- #return ds
+ #return ds
def updateFs(self, NB_VIEW, DATASET_LENGTH, NB_CLASS):
for viewIndice in range(NB_VIEW):
@@ -299,14 +314,14 @@ class Mumbo:
= np.sum(np.array([self.alphas[pastIterIndice, viewIndice]
* self.ds[pastIterIndice, viewIndice, exampleIndice]
if self.predictions[pastIterIndice, viewIndice,
- exampleIndice]
+ exampleIndice]
==
classe
else 0
for pastIterIndice in range(self.iterIndex)]))
if np.amax(np.absolute(self.fs)) != 0:
self.fs /= np.amax(np.absolute(self.fs))
- #return fs
+ #return fs
def updateCostmatrices(self, NB_VIEW, DATASET_LENGTH, NB_CLASS, CLASS_LABELS):
for viewIndice in range(NB_VIEW):
@@ -336,8 +351,8 @@ class Mumbo:
self.generalFs[self.iterIndex, exampleIndice, classe] \
= np.sum(np.array([self.generalAlphas[pastIterIndice]
if self.predictions[pastIterIndice,
- bestView,
- exampleIndice]
+ bestView,
+ exampleIndice]
==
classe
else 0
@@ -346,7 +361,7 @@ class Mumbo:
)
if np.amax(np.absolute(self.generalFs)) != 0:
self.generalFs /= np.amax(np.absolute(self.generalFs))
- #return generalFs
+ #return generalFs
def updateGeneralCostMatrix(self, DATASET_LENGTH, NB_CLASS, CLASS_LABELS):
for exampleIndice in range(DATASET_LENGTH):
@@ -359,8 +374,8 @@ class Mumbo:
self.generalCostMatrix[self.iterIndex, exampleIndice, classe] \
= -1 * np.sum(np.exp(self.generalFs[self.iterIndex, exampleIndice] -
self.generalFs[self.iterIndex, exampleIndice, classe]))
- # if np.amax(np.absolute(generalCostMatrix)) != 0:
- # generalCostMatrix = generalCostMatrix/np.amax(np.absolute(generalCostMatrix))
+ # if np.amax(np.absolute(generalCostMatrix)) != 0:
+ # generalCostMatrix = generalCostMatrix/np.amax(np.absolute(generalCostMatrix))
def fit(self, DATASET, CLASS_LABELS, **kwargs):
# Initialization
@@ -372,11 +387,11 @@ class Mumbo:
# predictions, generalAlphas, generalFs = initialize(NB_CLASS, NB_VIEW,
# NB_ITER, DATASET_LENGTH,
# CLASS_LABELS)
- bestViews = np.zeros(self.nbIter)
+ bestViews = np.zeros(self.maxIter)
bestClassifiers = []
# Learning
- for i in range(self.nbIter):
+ for i in range(self.maxIter):
logging.debug('\t\tStart:\t Iteration ' + str(self.iterIndex + 1))
classifiers, predictedLabels, areBad = self.trainWeakClassifiers(DATASET, CLASS_LABELS, NB_CLASS,
DATASET_LENGTH, NB_VIEW)
@@ -391,7 +406,7 @@ class Mumbo:
self.alphas[self.iterIndex, viewIndice] = 0.
else:
self.alphas[self.iterIndex, viewIndice] = self.computeAlpha(self.edges[self.iterIndex,
- viewIndice])
+ viewIndice])
self.updateDs(CLASS_LABELS, NB_VIEW, DATASET_LENGTH)
self.updateFs(NB_VIEW, DATASET_LENGTH, NB_CLASS)
@@ -406,7 +421,7 @@ class Mumbo:
self.updateGeneralFs(DATASET_LENGTH, NB_CLASS, bestView)
self.updateGeneralCostMatrix(DATASET_LENGTH, NB_CLASS, CLASS_LABELS)
- # finalFs = computeFinalFs(DATASET_LENGTH, NB_CLASS, generalAlphas, predictions, bestViews, LABELS, NB_ITER)
+ # finalFs = computeFinalFs(DATASET_LENGTH, NB_CLASS, generalAlphas, predictions, bestViews, LABELS, NB_ITER)
def predict(self, DATASET, NB_CLASS=2):
DATASET_LENGTH = len(DATASET[0])
@@ -442,7 +457,7 @@ class Mumbo:
usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
if usedIndices:
DATASET_LENGTH = len(usedIndices)
- predictedLabels = np.zeros((DATASET_LENGTH, self.nbIter))
+ predictedLabels = np.zeros((DATASET_LENGTH, self.maxIter))
votes = np.zeros((DATASET_LENGTH, NB_CLASS))
for iterIndex, (classifier, alpha, view) in enumerate(zip(self.bestClassifiers, self.alphas, self.bestViews)):
@@ -455,7 +470,7 @@ class Mumbo:
predictedLabels[usedExampleIndex, iterIndex] = np.argmax(votes[usedExampleIndex])
else:
predictedLabels = []
- for i in range(self.nbIter):
+ for i in range(self.maxIter):
predictedLabels.append([])
return np.transpose(predictedLabels)
diff --git a/Code/Multiview/Mumbo/__init__.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/__init__.py
similarity index 100%
rename from Code/Multiview/Mumbo/__init__.py
rename to Code/MonoMutliViewClassifiers/Multiview/Mumbo/__init__.py
diff --git a/Code/Multiview/Mumbo/analyzeResults.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
similarity index 99%
rename from Code/Multiview/Mumbo/analyzeResults.py
rename to Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
index 8d32066b..ebca5333 100644
--- a/Code/Multiview/Mumbo/analyzeResults.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
@@ -21,7 +21,7 @@ def plotAccuracyByIter(trainAccuracy, testAccuracy, validationAccuracy, NB_ITER,
figure = plt.figure()
ax1 = figure.add_subplot(111)
axes = figure.gca()
- axes.set_ylim([0,100])
+ axes.set_ylim([40,100])
titleString = ""
for view, classifierConfig in zip(features, classifierAnalysis):
titleString += "\n" + view + " : " + classifierConfig
diff --git a/Code/Multiview/Results/20160805-051412Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-Methyl-MiRNA-RNASEQ-Clinical-learnRate2.0-nbIter10-Fake.txt b/Code/MonoMutliViewClassifiers/Multiview/Results/20160805-051412Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-Methyl-MiRNA-RNASEQ-Clinical-learnRate2.0-nbIter10-Fake.txt
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rename from Code/Multiview/Results/20160805-051412Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-Methyl-MiRNA-RNASEQ-Clinical-learnRate2.0-nbIter10-Fake.txt
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160805-051412Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-Methyl-MiRNA-RNASEQ-Clinical-learnRate2.0-nbIter10-Fake.txt
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diff --git a/Code/Multiview/Results/20160805-051504Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-Methyl-MiRNA-RNASEQ-Clinical-learnRate2.0-nbIter10-Fake.txt b/Code/MonoMutliViewClassifiers/Multiview/Results/20160805-051504Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-Methyl-MiRNA-RNASEQ-Clinical-learnRate2.0-nbIter10-Fake.txt
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rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160810-151543Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate5.0-nbIter2-MultiOmic-accuracyByIteration.png
diff --git a/Code/Multiview/Results/20160810-151543Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate5.0-nbIter2-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Multiview/Results/20160810-151543Results-Mumbo-DecisionTree:DecisionTree:DecisionTree:DecisionTree-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate5.0-nbIter2-MultiOmic.txt
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diff --git a/Code/Multiview/Results/20160822-125346-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160822-125346-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160822-125346-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160822-125346-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160822-130127-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160822-130127-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160822-130127-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160822-130127-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160822-152017-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160822-152017-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160822-152017-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160822-152017-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160822-152034-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160822-152034-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-MultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160822-152034-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-MultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160822-152034-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-MultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160822-152113-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160822-152113-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-MultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160822-152113-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-MultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160822-152113-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-MultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160823-095233-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160823-095233-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160823-095233-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160823-095233-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160823-095734-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160823-095734-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160823-095734-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160823-095734-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160823-100003-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160823-100003-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160823-100003-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160823-100003-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160823-100209-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160823-100209-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160823-100209-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160823-100209-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160823-100355-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160823-100355-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160823-100355-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160823-100355-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160823-100549-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160823-100549-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160823-100549-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160823-100549-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160823-100728-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160823-100728-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160823-100728-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160823-100728-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160823-101021-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160823-101021-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160823-101021-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160823-101021-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160823-101135-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160823-101135-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160823-101135-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160823-101135-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160823-101459-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160823-101459-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160823-101459-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160823-101459-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
diff --git a/Code/Multiview/Results/20160823-101527-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160823-101527-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
similarity index 100%
rename from Code/Multiview/Results/20160823-101527-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
rename to Code/MonoMutliViewClassifiers/Multiview/Results/20160823-101527-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-083255-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-083255-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..442b8234
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-083255-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,2 @@
+2016-08-24 08:32:55,450 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
+2016-08-24 08:32:55,471 DEBUG: Start: Getting Methylation Data
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-083315-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-083315-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..0db771fc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-083315-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,2 @@
+2016-08-24 08:33:15,026 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 08:33:15,027 INFO: Info: Labels used: No, Yes
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-083427-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-083427-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
new file mode 100644
index 00000000..ade5b1d0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-083427-CMultiV-Mumbo-RGB_HOG_SIFT-MultiOmic-LOG.log
@@ -0,0 +1,10 @@
+2016-08-24 08:34:27,721 INFO: Start: Read CSV Database Files for MultiOmic
+2016-08-24 08:34:27,744 DEBUG: Start: Getting Methylation Data
+2016-08-24 08:34:40,551 DEBUG: Done: Getting Methylation Data
+2016-08-24 08:34:40,551 DEBUG: Start: Getting MiRNA Data
+2016-08-24 08:34:41,071 DEBUG: Done: Getting MiRNA Data
+2016-08-24 08:34:41,071 DEBUG: Start: Getting RNASeq Data
+2016-08-24 08:36:25,941 DEBUG: Done: Getting RNASeq Data
+2016-08-24 08:36:26,035 DEBUG: Start: Getting Clinical Data
+2016-08-24 08:36:26,594 DEBUG: Done: Getting Clinical Data
+2016-08-24 08:36:28,223 INFO: Info: Labels used: No, Yes
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-084100-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-084100-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..a816c4d7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-084100-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,12 @@
+2016-08-24 08:41:00,873 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
+2016-08-24 08:41:00,968 DEBUG: Start: Getting Methylation Data
+2016-08-24 08:41:13,912 DEBUG: Done: Getting Methylation Data
+2016-08-24 08:41:13,912 DEBUG: Start: Getting MiRNA Data
+2016-08-24 08:41:14,437 DEBUG: Done: Getting MiRNA Data
+2016-08-24 08:41:14,438 DEBUG: Start: Getting RNASeq Data
+2016-08-24 08:41:58,294 DEBUG: Done: Getting RNASeq Data
+2016-08-24 08:41:58,382 DEBUG: Start: Getting Clinical Data
+2016-08-24 08:41:58,478 DEBUG: Done: Getting Clinical Data
+2016-08-24 08:41:58,521 DEBUG: Start: Getting Modified RNASeq Data
+2016-08-24 08:42:49,046 DEBUG: Done: Getting Modified RNASeq Data
+2016-08-24 08:42:50,194 INFO: Info: Labels used: No, Yes
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-084717-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-084717-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..02a820c2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-084717-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,21 @@
+2016-08-24 08:47:17,766 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 08:47:17,767 INFO: Info: Labels used: No, Yes
+2016-08-24 08:47:17,767 INFO: Info: Length of dataset:347
+2016-08-24 08:47:17,769 INFO: ### Main Programm for Multiview Classification
+2016-08-24 08:47:17,769 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 08:47:17,769 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 08:47:17,769 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 08:47:17,770 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 08:47:17,770 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 08:47:17,771 INFO: Done: Read Database Files
+2016-08-24 08:47:17,771 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 08:47:17,774 INFO: Done: Determine validation split
+2016-08-24 08:47:17,774 INFO: Start: Determine 2 folds
+2016-08-24 08:47:17,783 INFO: Info: Length of Learning Sets: 122
+2016-08-24 08:47:17,783 INFO: Info: Length of Testing Sets: 122
+2016-08-24 08:47:17,783 INFO: Info: Length of Validation Set: 103
+2016-08-24 08:47:17,783 INFO: Done: Determine folds
+2016-08-24 08:47:17,783 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 08:47:17,784 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 08:48:32,759 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 08:48:32,759 INFO: Start: Fold number 1
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-084943-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-084943-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..a96b7a7e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-084943-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,97 @@
+2016-08-24 08:49:43,519 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 08:49:43,519 INFO: Info: Labels used: No, Yes
+2016-08-24 08:49:43,520 INFO: Info: Length of dataset:347
+2016-08-24 08:49:43,521 INFO: ### Main Programm for Multiview Classification
+2016-08-24 08:49:43,521 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 08:49:43,522 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 08:49:43,522 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 08:49:43,523 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 08:49:43,523 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 08:49:43,523 INFO: Done: Read Database Files
+2016-08-24 08:49:43,523 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 08:49:43,527 INFO: Done: Determine validation split
+2016-08-24 08:49:43,527 INFO: Start: Determine 2 folds
+2016-08-24 08:49:43,537 INFO: Info: Length of Learning Sets: 122
+2016-08-24 08:49:43,537 INFO: Info: Length of Testing Sets: 122
+2016-08-24 08:49:43,537 INFO: Info: Length of Validation Set: 103
+2016-08-24 08:49:43,537 INFO: Done: Determine folds
+2016-08-24 08:49:43,537 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 08:49:43,537 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 08:49:43,537 DEBUG: Start: Gridsearch for DecisionTree
+2016-08-24 08:49:51,018 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:49:51,018 DEBUG: Start: Gridsearch for DecisionTree
+2016-08-24 08:49:52,921 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:49:52,921 DEBUG: Start: Gridsearch for DecisionTree
+2016-08-24 08:50:09,522 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:50:09,522 DEBUG: Start: Gridsearch for DecisionTree
+2016-08-24 08:50:11,247 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:50:11,247 DEBUG: Start: Gridsearch for DecisionTree
+2016-08-24 08:50:51,870 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:50:51,871 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 08:50:51,871 INFO: Start: Fold number 1
+2016-08-24 08:50:53,455 DEBUG: Start: Iteration 1
+2016-08-24 08:50:53,476 DEBUG: View 0 : 0.605263157895
+2016-08-24 08:50:53,484 DEBUG: View 1 : 0.605263157895
+2016-08-24 08:50:53,520 DEBUG: View 2 : 0.611842105263
+2016-08-24 08:50:53,528 DEBUG: View 3 : 0.493421052632
+2016-08-24 08:50:53,568 DEBUG: Best view : RANSeq_
+2016-08-24 08:50:53,652 DEBUG: Start: Iteration 2
+2016-08-24 08:50:53,669 DEBUG: View 0 : 0.605263157895
+2016-08-24 08:50:53,677 DEBUG: View 1 : 0.605263157895
+2016-08-24 08:50:53,713 DEBUG: View 2 : 0.394736842105
+2016-08-24 08:50:53,720 DEBUG: View 3 : 0.532894736842
+2016-08-24 08:50:53,763 DEBUG: Best view : Methyl_
+2016-08-24 08:50:53,908 DEBUG: Start: Iteration 3
+2016-08-24 08:50:53,924 DEBUG: View 0 : 0.565789473684
+2016-08-24 08:50:53,931 DEBUG: View 1 : 0.710526315789
+2016-08-24 08:50:53,966 DEBUG: View 2 : 0.407894736842
+2016-08-24 08:50:53,974 DEBUG: View 3 : 0.460526315789
+2016-08-24 08:50:54,024 DEBUG: Best view : MiRNA__
+2016-08-24 08:50:54,225 DEBUG: Start: Iteration 4
+2016-08-24 08:50:54,241 DEBUG: View 0 : 0.407894736842
+2016-08-24 08:50:54,249 DEBUG: View 1 : 0.427631578947
+2016-08-24 08:50:54,285 DEBUG: View 2 : 0.598684210526
+2016-08-24 08:50:54,292 DEBUG: View 3 : 0.519736842105
+2016-08-24 08:50:54,345 DEBUG: Best view : RANSeq_
+2016-08-24 08:50:54,614 DEBUG: Start: Iteration 5
+2016-08-24 08:50:54,630 DEBUG: View 0 : 0.565789473684
+2016-08-24 08:50:54,638 DEBUG: View 1 : 0.552631578947
+2016-08-24 08:50:54,673 DEBUG: View 2 : 0.480263157895
+2016-08-24 08:50:54,681 DEBUG: View 3 : 0.526315789474
+2016-08-24 08:50:54,736 DEBUG: Best view : MiRNA__
+2016-08-24 08:50:55,067 DEBUG: Start: Iteration 6
+2016-08-24 08:50:55,083 DEBUG: View 0 : 0.585526315789
+2016-08-24 08:50:55,090 DEBUG: View 1 : 0.381578947368
+2016-08-24 08:50:55,127 DEBUG: View 2 : 0.453947368421
+2016-08-24 08:50:55,134 DEBUG: View 3 : 0.467105263158
+2016-08-24 08:50:55,193 DEBUG: Best view : Methyl_
+2016-08-24 08:50:55,584 DEBUG: Start: Iteration 7
+2016-08-24 08:50:55,601 DEBUG: View 0 : 0.822368421053
+2016-08-24 08:50:55,609 DEBUG: View 1 : 0.651315789474
+2016-08-24 08:50:55,645 DEBUG: View 2 : 0.480263157895
+2016-08-24 08:50:55,653 DEBUG: View 3 : 0.375
+2016-08-24 08:50:55,712 DEBUG: Best view : Methyl_
+2016-08-24 08:50:56,161 DEBUG: Start: Iteration 8
+2016-08-24 08:50:56,178 DEBUG: View 0 : 0.638157894737
+2016-08-24 08:50:56,185 DEBUG: View 1 : 0.578947368421
+2016-08-24 08:50:56,221 DEBUG: View 2 : 0.631578947368
+2016-08-24 08:50:56,228 DEBUG: View 3 : 0.407894736842
+2016-08-24 08:50:56,290 DEBUG: Best view : Methyl_
+2016-08-24 08:50:56,793 DEBUG: Start: Iteration 9
+2016-08-24 08:50:56,809 DEBUG: View 0 : 0.532894736842
+2016-08-24 08:50:56,816 DEBUG: View 1 : 0.539473684211
+2016-08-24 08:50:56,852 DEBUG: View 2 : 0.532894736842
+2016-08-24 08:50:56,860 DEBUG: View 3 : 0.585526315789
+2016-08-24 08:50:56,923 DEBUG: Best view : Clinic_
+2016-08-24 08:50:57,481 DEBUG: Start: Iteration 10
+2016-08-24 08:50:57,497 DEBUG: View 0 : 0.618421052632
+2016-08-24 08:50:57,505 DEBUG: View 1 : 0.361842105263
+2016-08-24 08:50:57,541 DEBUG: View 2 : 0.394736842105
+2016-08-24 08:50:57,548 DEBUG: View 3 : 0.664473684211
+2016-08-24 08:50:57,614 DEBUG: Best view : Clinic_
+2016-08-24 08:50:58,229 DEBUG: Start: Iteration 11
+2016-08-24 08:50:58,244 DEBUG: View 0 : 0.565789473684
+2016-08-24 08:50:58,252 DEBUG: View 1 : 0.421052631579
+2016-08-24 08:50:58,287 DEBUG: View 2 : 0.539473684211
+2016-08-24 08:50:58,295 DEBUG: View 3 : 0.493421052632
+2016-08-24 08:50:58,363 DEBUG: Best view : Methyl_
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-085300-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-085300-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..4b32381d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-085300-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,24 @@
+2016-08-24 08:53:00,304 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 08:53:00,305 INFO: Info: Labels used: No, Yes
+2016-08-24 08:53:00,306 INFO: Info: Length of dataset:347
+2016-08-24 08:53:00,309 INFO: ### Main Programm for Multiview Classification
+2016-08-24 08:53:00,309 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 08:53:00,311 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 08:53:00,312 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 08:53:00,313 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 08:53:00,314 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 08:53:00,314 INFO: Done: Read Database Files
+2016-08-24 08:53:00,314 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 08:53:00,320 INFO: Done: Determine validation split
+2016-08-24 08:53:00,320 INFO: Start: Determine 2 folds
+2016-08-24 08:53:00,330 INFO: Info: Length of Learning Sets: 122
+2016-08-24 08:53:00,330 INFO: Info: Length of Testing Sets: 122
+2016-08-24 08:53:00,330 INFO: Info: Length of Validation Set: 103
+2016-08-24 08:53:00,331 INFO: Done: Determine folds
+2016-08-24 08:53:00,331 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 08:53:00,331 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 08:53:00,331 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 08:53:07,603 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:53:07,604 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 08:53:09,500 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:53:09,500 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-085340-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-085340-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..38b211e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-085340-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,97 @@
+2016-08-24 08:53:40,067 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 08:53:40,068 INFO: Info: Labels used: No, Yes
+2016-08-24 08:53:40,068 INFO: Info: Length of dataset:347
+2016-08-24 08:53:40,069 INFO: ### Main Programm for Multiview Classification
+2016-08-24 08:53:40,069 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 08:53:40,070 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 08:53:40,070 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 08:53:40,071 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 08:53:40,071 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 08:53:40,071 INFO: Done: Read Database Files
+2016-08-24 08:53:40,071 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 08:53:40,075 INFO: Done: Determine validation split
+2016-08-24 08:53:40,075 INFO: Start: Determine 2 folds
+2016-08-24 08:53:40,085 INFO: Info: Length of Learning Sets: 122
+2016-08-24 08:53:40,085 INFO: Info: Length of Testing Sets: 122
+2016-08-24 08:53:40,085 INFO: Info: Length of Validation Set: 103
+2016-08-24 08:53:40,085 INFO: Done: Determine folds
+2016-08-24 08:53:40,085 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 08:53:40,085 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 08:53:40,086 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 08:53:47,504 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:53:47,504 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 08:53:49,404 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:53:49,405 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 08:54:06,005 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:54:06,006 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 08:54:07,742 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:54:07,742 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 08:54:44,843 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:54:44,843 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 08:54:44,844 INFO: Start: Fold number 1
+2016-08-24 08:54:46,449 DEBUG: Start: Iteration 1
+2016-08-24 08:54:46,465 DEBUG: View 0 : 0.631901840491
+2016-08-24 08:54:46,473 DEBUG: View 1 : 0.638036809816
+2016-08-24 08:54:46,510 DEBUG: View 2 : 0.441717791411
+2016-08-24 08:54:46,518 DEBUG: View 3 : 0.631901840491
+2016-08-24 08:54:46,561 DEBUG: Best view : Methyl_
+2016-08-24 08:54:46,662 DEBUG: Start: Iteration 2
+2016-08-24 08:54:46,680 DEBUG: View 0 : 0.503067484663
+2016-08-24 08:54:46,688 DEBUG: View 1 : 0.466257668712
+2016-08-24 08:54:46,726 DEBUG: View 2 : 0.478527607362
+2016-08-24 08:54:46,734 DEBUG: View 3 : 0.441717791411
+2016-08-24 08:54:46,781 DEBUG: Best view : Methyl_
+2016-08-24 08:54:46,945 DEBUG: Start: Iteration 3
+2016-08-24 08:54:46,962 DEBUG: View 0 : 0.361963190184
+2016-08-24 08:54:46,970 DEBUG: View 1 : 0.613496932515
+2016-08-24 08:54:47,008 DEBUG: View 2 : 0.411042944785
+2016-08-24 08:54:47,016 DEBUG: View 3 : 0.656441717791
+2016-08-24 08:54:47,071 DEBUG: Best view : Clinic_
+2016-08-24 08:54:47,297 DEBUG: Start: Iteration 4
+2016-08-24 08:54:47,314 DEBUG: View 0 : 0.435582822086
+2016-08-24 08:54:47,322 DEBUG: View 1 : 0.509202453988
+2016-08-24 08:54:47,360 DEBUG: View 2 : 0.38036809816
+2016-08-24 08:54:47,368 DEBUG: View 3 : 0.631901840491
+2016-08-24 08:54:47,426 DEBUG: Best view : Clinic_
+2016-08-24 08:54:47,709 DEBUG: Start: Iteration 5
+2016-08-24 08:54:47,726 DEBUG: View 0 : 0.638036809816
+2016-08-24 08:54:47,734 DEBUG: View 1 : 0.564417177914
+2016-08-24 08:54:47,772 DEBUG: View 2 : 0.39263803681
+2016-08-24 08:54:47,780 DEBUG: View 3 : 0.546012269939
+2016-08-24 08:54:47,841 DEBUG: Best view : Methyl_
+2016-08-24 08:54:48,187 DEBUG: Start: Iteration 6
+2016-08-24 08:54:48,204 DEBUG: View 0 : 0.509202453988
+2016-08-24 08:54:48,211 DEBUG: View 1 : 0.638036809816
+2016-08-24 08:54:48,250 DEBUG: View 2 : 0.564417177914
+2016-08-24 08:54:48,258 DEBUG: View 3 : 0.558282208589
+2016-08-24 08:54:48,320 DEBUG: Best view : MiRNA__
+2016-08-24 08:54:48,727 DEBUG: Start: Iteration 7
+2016-08-24 08:54:48,744 DEBUG: View 0 : 0.564417177914
+2016-08-24 08:54:48,752 DEBUG: View 1 : 0.533742331288
+2016-08-24 08:54:48,791 DEBUG: View 2 : 0.509202453988
+2016-08-24 08:54:48,799 DEBUG: View 3 : 0.466257668712
+2016-08-24 08:54:48,863 DEBUG: Best view : Methyl_
+2016-08-24 08:54:49,333 DEBUG: Start: Iteration 8
+2016-08-24 08:54:49,350 DEBUG: View 0 : 0.423312883436
+2016-08-24 08:54:49,358 DEBUG: View 1 : 0.39263803681
+2016-08-24 08:54:49,396 DEBUG: View 2 : 0.40490797546
+2016-08-24 08:54:49,404 DEBUG: View 3 : 0.546012269939
+2016-08-24 08:54:49,471 DEBUG: Best view : Clinic_
+2016-08-24 08:54:50,001 DEBUG: Start: Iteration 9
+2016-08-24 08:54:50,018 DEBUG: View 0 : 0.478527607362
+2016-08-24 08:54:50,025 DEBUG: View 1 : 0.368098159509
+2016-08-24 08:54:50,063 DEBUG: View 2 : 0.631901840491
+2016-08-24 08:54:50,071 DEBUG: View 3 : 0.539877300613
+2016-08-24 08:54:50,141 DEBUG: Best view : RANSeq_
+2016-08-24 08:54:50,745 DEBUG: Start: Iteration 10
+2016-08-24 08:54:50,762 DEBUG: View 0 : 0.576687116564
+2016-08-24 08:54:50,770 DEBUG: View 1 : 0.496932515337
+2016-08-24 08:54:50,808 DEBUG: View 2 : 0.613496932515
+2016-08-24 08:54:50,816 DEBUG: View 3 : 0.374233128834
+2016-08-24 08:54:50,888 DEBUG: Best view : RANSeq_
+2016-08-24 08:54:51,566 DEBUG: Start: Iteration 11
+2016-08-24 08:54:51,583 DEBUG: View 0 : 0.472392638037
+2016-08-24 08:54:51,591 DEBUG: View 1 : 0.435582822086
+2016-08-24 08:54:51,629 DEBUG: View 2 : 0.478527607362
+2016-08-24 08:54:51,637 DEBUG: View 3 : 0.576687116564
+2016-08-24 08:54:51,711 DEBUG: Best view : Clinic_
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-085528-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-085528-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..85f1830f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-085528-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,98 @@
+2016-08-24 08:55:28,014 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 08:55:28,015 INFO: Info: Labels used: No, Yes
+2016-08-24 08:55:28,015 INFO: Info: Length of dataset:347
+2016-08-24 08:55:28,016 INFO: ### Main Programm for Multiview Classification
+2016-08-24 08:55:28,017 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 08:55:28,017 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 08:55:28,017 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 08:55:28,018 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 08:55:28,019 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 08:55:28,019 INFO: Done: Read Database Files
+2016-08-24 08:55:28,019 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 08:55:28,025 INFO: Done: Determine validation split
+2016-08-24 08:55:28,025 INFO: Start: Determine 2 folds
+2016-08-24 08:55:28,039 INFO: Info: Length of Learning Sets: 122
+2016-08-24 08:55:28,040 INFO: Info: Length of Testing Sets: 122
+2016-08-24 08:55:28,040 INFO: Info: Length of Validation Set: 103
+2016-08-24 08:55:28,040 INFO: Done: Determine folds
+2016-08-24 08:55:28,040 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 08:55:28,040 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 08:55:28,040 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 08:55:35,427 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:55:35,427 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 08:55:37,400 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:55:37,401 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 08:55:55,603 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:55:55,604 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 08:55:57,380 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:55:57,381 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 08:56:35,467 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 08:56:35,467 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 08:56:35,467 INFO: Start: Fold number 1
+2016-08-24 08:56:37,036 DEBUG: Start: Iteration 1
+2016-08-24 08:56:37,052 DEBUG: View 0 : 0.37037037037
+2016-08-24 08:56:37,060 DEBUG: View 1 : 0.62962962963
+2016-08-24 08:56:37,089 DEBUG: View 2 : 0.37037037037
+2016-08-24 08:56:37,096 DEBUG: View 3 : 0.530864197531
+2016-08-24 08:56:37,139 DEBUG: Best view : Methyl_
+2016-08-24 08:56:37,218 DEBUG: Start: Iteration 2
+2016-08-24 08:56:37,235 DEBUG: View 0 : 0.481481481481
+2016-08-24 08:56:37,242 DEBUG: View 1 : 0.41975308642
+2016-08-24 08:56:37,279 DEBUG: View 2 : 0.481481481481
+2016-08-24 08:56:37,287 DEBUG: View 3 : 0.530864197531
+2016-08-24 08:56:37,339 DEBUG: Best view : Clinic_
+2016-08-24 08:56:37,477 DEBUG: Start: Iteration 3
+2016-08-24 08:56:37,494 DEBUG: View 0 : 0.438271604938
+2016-08-24 08:56:37,502 DEBUG: View 1 : 0.407407407407
+2016-08-24 08:56:37,539 DEBUG: View 2 : 0.382716049383
+2016-08-24 08:56:37,547 DEBUG: View 3 : 0.376543209877
+2016-08-24 08:56:37,547 WARNING: All bad for iteration 2
+2016-08-24 08:56:37,602 DEBUG: Best view : Methyl_
+2016-08-24 08:56:37,803 DEBUG: Start: Iteration 4
+2016-08-24 08:56:37,819 DEBUG: View 0 : 0.604938271605
+2016-08-24 08:56:37,827 DEBUG: View 1 : 0.271604938272
+2016-08-24 08:56:37,864 DEBUG: View 2 : 0.469135802469
+2016-08-24 08:56:37,872 DEBUG: View 3 : 0.487654320988
+2016-08-24 08:56:37,929 DEBUG: Best view : Methyl_
+2016-08-24 08:56:38,194 DEBUG: Start: Iteration 5
+2016-08-24 08:56:38,211 DEBUG: View 0 : 0.395061728395
+2016-08-24 08:56:38,219 DEBUG: View 1 : 0.413580246914
+2016-08-24 08:56:38,256 DEBUG: View 2 : 0.481481481481
+2016-08-24 08:56:38,264 DEBUG: View 3 : 0.506172839506
+2016-08-24 08:56:38,323 DEBUG: Best view : Clinic_
+2016-08-24 08:56:38,648 DEBUG: Start: Iteration 6
+2016-08-24 08:56:38,665 DEBUG: View 0 : 0.543209876543
+2016-08-24 08:56:38,672 DEBUG: View 1 : 0.425925925926
+2016-08-24 08:56:38,709 DEBUG: View 2 : 0.376543209877
+2016-08-24 08:56:38,717 DEBUG: View 3 : 0.586419753086
+2016-08-24 08:56:38,780 DEBUG: Best view : Clinic_
+2016-08-24 08:56:39,164 DEBUG: Start: Iteration 7
+2016-08-24 08:56:39,180 DEBUG: View 0 : 0.561728395062
+2016-08-24 08:56:39,188 DEBUG: View 1 : 0.5
+2016-08-24 08:56:39,225 DEBUG: View 2 : 0.537037037037
+2016-08-24 08:56:39,233 DEBUG: View 3 : 0.567901234568
+2016-08-24 08:56:39,298 DEBUG: Best view : Methyl_
+2016-08-24 08:56:39,744 DEBUG: Start: Iteration 8
+2016-08-24 08:56:39,761 DEBUG: View 0 : 0.592592592593
+2016-08-24 08:56:39,769 DEBUG: View 1 : 0.438271604938
+2016-08-24 08:56:39,806 DEBUG: View 2 : 0.456790123457
+2016-08-24 08:56:39,813 DEBUG: View 3 : 0.530864197531
+2016-08-24 08:56:39,880 DEBUG: Best view : Methyl_
+2016-08-24 08:56:40,391 DEBUG: Start: Iteration 9
+2016-08-24 08:56:40,408 DEBUG: View 0 : 0.561728395062
+2016-08-24 08:56:40,416 DEBUG: View 1 : 0.413580246914
+2016-08-24 08:56:40,453 DEBUG: View 2 : 0.617283950617
+2016-08-24 08:56:40,461 DEBUG: View 3 : 0.512345679012
+2016-08-24 08:56:40,530 DEBUG: Best view : RANSeq_
+2016-08-24 08:56:41,117 DEBUG: Start: Iteration 10
+2016-08-24 08:56:41,133 DEBUG: View 0 : 0.648148148148
+2016-08-24 08:56:41,141 DEBUG: View 1 : 0.456790123457
+2016-08-24 08:56:41,178 DEBUG: View 2 : 0.407407407407
+2016-08-24 08:56:41,186 DEBUG: View 3 : 0.66049382716
+2016-08-24 08:56:41,257 DEBUG: Best view : Methyl_
+2016-08-24 08:56:41,906 DEBUG: Start: Iteration 11
+2016-08-24 08:56:41,923 DEBUG: View 0 : 0.401234567901
+2016-08-24 08:56:41,931 DEBUG: View 1 : 0.586419753086
+2016-08-24 08:56:41,968 DEBUG: View 2 : 0.382716049383
+2016-08-24 08:56:41,975 DEBUG: View 3 : 0.493827160494
+2016-08-24 08:56:42,049 DEBUG: Best view : MiRNA__
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-090120-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-090120-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..d966042b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-090120-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,97 @@
+2016-08-24 09:01:20,823 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:01:20,823 INFO: Info: Labels used: No, Yes
+2016-08-24 09:01:20,823 INFO: Info: Length of dataset:347
+2016-08-24 09:01:20,825 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:01:20,825 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:01:20,825 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:01:20,826 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:01:20,826 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:01:20,827 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:01:20,827 INFO: Done: Read Database Files
+2016-08-24 09:01:20,827 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:01:20,830 INFO: Done: Determine validation split
+2016-08-24 09:01:20,830 INFO: Start: Determine 2 folds
+2016-08-24 09:01:20,838 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:01:20,838 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:01:20,838 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:01:20,838 INFO: Done: Determine folds
+2016-08-24 09:01:20,839 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:01:20,839 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:01:20,839 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:01:28,173 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:01:28,173 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:01:30,085 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:01:30,086 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 09:01:46,691 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:01:46,691 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 09:01:48,441 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:01:48,441 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 09:02:44,634 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:02:44,634 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 09:02:44,634 INFO: Start: Fold number 1
+2016-08-24 09:02:46,206 DEBUG: Start: Iteration 1
+2016-08-24 09:02:46,269 DEBUG: View 0 : 0.37037037037
+2016-08-24 09:02:46,303 DEBUG: View 1 : 0.623456790123
+2016-08-24 09:02:46,497 DEBUG: View 2 : 0.648148148148
+2016-08-24 09:02:46,505 DEBUG: View 3 : 0.62962962963
+2016-08-24 09:02:46,552 DEBUG: Best view : Methyl_
+2016-08-24 09:02:46,634 DEBUG: Start: Iteration 2
+2016-08-24 09:02:46,652 DEBUG: View 0 : 0.438271604938
+2016-08-24 09:02:46,660 DEBUG: View 1 : 0.493827160494
+2016-08-24 09:02:46,699 DEBUG: View 2 : 0.549382716049
+2016-08-24 09:02:46,707 DEBUG: View 3 : 0.518518518519
+2016-08-24 09:02:46,760 DEBUG: Best view : RANSeq_
+2016-08-24 09:02:46,915 DEBUG: Start: Iteration 3
+2016-08-24 09:02:46,932 DEBUG: View 0 : 0.543209876543
+2016-08-24 09:02:46,940 DEBUG: View 1 : 0.425925925926
+2016-08-24 09:02:46,978 DEBUG: View 2 : 0.518518518519
+2016-08-24 09:02:46,985 DEBUG: View 3 : 0.648148148148
+2016-08-24 09:02:47,040 DEBUG: Best view : Clinic_
+2016-08-24 09:02:47,253 DEBUG: Start: Iteration 4
+2016-08-24 09:02:47,270 DEBUG: View 0 : 0.432098765432
+2016-08-24 09:02:47,278 DEBUG: View 1 : 0.623456790123
+2016-08-24 09:02:47,316 DEBUG: View 2 : 0.456790123457
+2016-08-24 09:02:47,324 DEBUG: View 3 : 0.537037037037
+2016-08-24 09:02:47,381 DEBUG: Best view : MiRNA__
+2016-08-24 09:02:47,652 DEBUG: Start: Iteration 5
+2016-08-24 09:02:47,669 DEBUG: View 0 : 0.395061728395
+2016-08-24 09:02:47,677 DEBUG: View 1 : 0.561728395062
+2016-08-24 09:02:47,715 DEBUG: View 2 : 0.524691358025
+2016-08-24 09:02:47,723 DEBUG: View 3 : 0.413580246914
+2016-08-24 09:02:47,781 DEBUG: Best view : MiRNA__
+2016-08-24 09:02:48,113 DEBUG: Start: Iteration 6
+2016-08-24 09:02:48,130 DEBUG: View 0 : 0.716049382716
+2016-08-24 09:02:48,138 DEBUG: View 1 : 0.351851851852
+2016-08-24 09:02:48,176 DEBUG: View 2 : 0.549382716049
+2016-08-24 09:02:48,183 DEBUG: View 3 : 0.530864197531
+2016-08-24 09:02:48,245 DEBUG: Best view : Methyl_
+2016-08-24 09:02:48,639 DEBUG: Start: Iteration 7
+2016-08-24 09:02:48,656 DEBUG: View 0 : 0.598765432099
+2016-08-24 09:02:48,664 DEBUG: View 1 : 0.524691358025
+2016-08-24 09:02:48,701 DEBUG: View 2 : 0.524691358025
+2016-08-24 09:02:48,709 DEBUG: View 3 : 0.382716049383
+2016-08-24 09:02:48,773 DEBUG: Best view : Methyl_
+2016-08-24 09:02:49,242 DEBUG: Start: Iteration 8
+2016-08-24 09:02:49,262 DEBUG: View 0 : 0.33950617284
+2016-08-24 09:02:49,272 DEBUG: View 1 : 0.617283950617
+2016-08-24 09:02:49,315 DEBUG: View 2 : 0.5
+2016-08-24 09:02:49,325 DEBUG: View 3 : 0.574074074074
+2016-08-24 09:02:49,401 DEBUG: Best view : MiRNA__
+2016-08-24 09:02:49,959 DEBUG: Start: Iteration 9
+2016-08-24 09:02:49,975 DEBUG: View 0 : 0.512345679012
+2016-08-24 09:02:49,984 DEBUG: View 1 : 0.537037037037
+2016-08-24 09:02:50,021 DEBUG: View 2 : 0.438271604938
+2016-08-24 09:02:50,029 DEBUG: View 3 : 0.604938271605
+2016-08-24 09:02:50,097 DEBUG: Best view : Clinic_
+2016-08-24 09:02:50,670 DEBUG: Start: Iteration 10
+2016-08-24 09:02:50,687 DEBUG: View 0 : 0.401234567901
+2016-08-24 09:02:50,695 DEBUG: View 1 : 0.635802469136
+2016-08-24 09:02:50,733 DEBUG: View 2 : 0.518518518519
+2016-08-24 09:02:50,740 DEBUG: View 3 : 0.543209876543
+2016-08-24 09:02:50,811 DEBUG: Best view : MiRNA__
+2016-08-24 09:02:51,445 DEBUG: Start: Iteration 11
+2016-08-24 09:02:51,462 DEBUG: View 0 : 0.592592592593
+2016-08-24 09:02:51,470 DEBUG: View 1 : 0.537037037037
+2016-08-24 09:02:51,508 DEBUG: View 2 : 0.388888888889
+2016-08-24 09:02:51,516 DEBUG: View 3 : 0.450617283951
+2016-08-24 09:02:51,589 DEBUG: Best view : Methyl_
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-090956-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-090956-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..ca273594
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-090956-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,14246 @@
+2016-08-24 09:09:56,962 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:09:56,963 INFO: Info: Labels used: No, Yes
+2016-08-24 09:09:56,963 INFO: Info: Length of dataset:347
+2016-08-24 09:09:56,973 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:09:56,973 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:09:56,973 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:09:56,974 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:09:56,974 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:09:56,975 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:09:56,975 INFO: Done: Read Database Files
+2016-08-24 09:09:56,975 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:09:56,978 INFO: Done: Determine validation split
+2016-08-24 09:09:56,978 INFO: Start: Determine 2 folds
+2016-08-24 09:09:56,987 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:09:56,987 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:09:56,988 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:09:56,988 INFO: Done: Determine folds
+2016-08-24 09:09:56,988 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:09:56,988 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:09:56,988 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:10:04,379 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:10:04,380 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:10:06,314 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:10:06,315 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 09:10:23,238 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:10:23,238 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 09:10:25,001 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:10:25,002 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 09:11:02,594 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:11:02,594 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 09:11:02,594 INFO: Start: Fold number 1
+2016-08-24 09:11:04,137 DEBUG: Start: Iteration 1
+2016-08-24 09:11:04,153 DEBUG: View 0 : 0.622641509434
+2016-08-24 09:11:04,161 DEBUG: View 1 : 0.339622641509
+2016-08-24 09:11:04,189 DEBUG: View 2 : 0.622641509434
+2016-08-24 09:11:04,197 DEBUG: View 3 : 0.377358490566
+2016-08-24 09:11:04,239 DEBUG: Best view : MiRNA__
+2016-08-24 09:11:04,311 DEBUG: Start: Iteration 2
+2016-08-24 09:11:04,328 DEBUG: View 0 : 0.534591194969
+2016-08-24 09:11:04,336 DEBUG: View 1 : 0.522012578616
+2016-08-24 09:11:04,373 DEBUG: View 2 : 0.540880503145
+2016-08-24 09:11:04,381 DEBUG: View 3 : 0.477987421384
+2016-08-24 09:11:04,426 DEBUG: Best view : RANSeq_
+2016-08-24 09:11:04,573 DEBUG: Start: Iteration 3
+2016-08-24 09:11:04,590 DEBUG: View 0 : 0.603773584906
+2016-08-24 09:11:04,598 DEBUG: View 1 : 0.566037735849
+2016-08-24 09:11:04,636 DEBUG: View 2 : 0.553459119497
+2016-08-24 09:11:04,643 DEBUG: View 3 : 0.62893081761
+2016-08-24 09:11:04,696 DEBUG: Best view : Clinic_
+2016-08-24 09:11:04,902 DEBUG: Start: Iteration 4
+2016-08-24 09:11:04,918 DEBUG: View 0 : 0.496855345912
+2016-08-24 09:11:04,926 DEBUG: View 1 : 0.51572327044
+2016-08-24 09:11:04,963 DEBUG: View 2 : 0.62893081761
+2016-08-24 09:11:04,970 DEBUG: View 3 : 0.421383647799
+2016-08-24 09:11:05,025 DEBUG: Best view : RANSeq_
+2016-08-24 09:11:05,308 DEBUG: Start: Iteration 5
+2016-08-24 09:11:05,324 DEBUG: View 0 : 0.477987421384
+2016-08-24 09:11:05,332 DEBUG: View 1 : 0.685534591195
+2016-08-24 09:11:05,369 DEBUG: View 2 : 0.452830188679
+2016-08-24 09:11:05,377 DEBUG: View 3 : 0.584905660377
+2016-08-24 09:11:05,434 DEBUG: Best view : MiRNA__
+2016-08-24 09:11:05,769 DEBUG: Start: Iteration 6
+2016-08-24 09:11:05,786 DEBUG: View 0 : 0.433962264151
+2016-08-24 09:11:05,793 DEBUG: View 1 : 0.333333333333
+2016-08-24 09:11:05,830 DEBUG: View 2 : 0.396226415094
+2016-08-24 09:11:05,838 DEBUG: View 3 : 0.616352201258
+2016-08-24 09:11:05,899 DEBUG: Best view : Clinic_
+2016-08-24 09:11:06,295 DEBUG: Start: Iteration 7
+2016-08-24 09:11:06,311 DEBUG: View 0 : 0.509433962264
+2016-08-24 09:11:06,319 DEBUG: View 1 : 0.295597484277
+2016-08-24 09:11:06,356 DEBUG: View 2 : 0.471698113208
+2016-08-24 09:11:06,364 DEBUG: View 3 : 0.427672955975
+2016-08-24 09:11:06,426 DEBUG: Best view : Methyl_
+2016-08-24 09:11:06,881 DEBUG: Start: Iteration 8
+2016-08-24 09:11:06,897 DEBUG: View 0 : 0.452830188679
+2016-08-24 09:11:06,905 DEBUG: View 1 : 0.295597484277
+2016-08-24 09:11:06,942 DEBUG: View 2 : 0.440251572327
+2016-08-24 09:11:06,950 DEBUG: View 3 : 0.62893081761
+2016-08-24 09:11:07,016 DEBUG: Best view : Clinic_
+2016-08-24 09:11:07,528 DEBUG: Start: Iteration 9
+2016-08-24 09:11:07,544 DEBUG: View 0 : 0.522012578616
+2016-08-24 09:11:07,552 DEBUG: View 1 : 0.754716981132
+2016-08-24 09:11:07,589 DEBUG: View 2 : 0.534591194969
+2016-08-24 09:11:07,597 DEBUG: View 3 : 0.396226415094
+2016-08-24 09:11:07,664 DEBUG: Best view : MiRNA__
+2016-08-24 09:11:08,239 DEBUG: Start: Iteration 10
+2016-08-24 09:11:08,256 DEBUG: View 0 : 0.48427672956
+2016-08-24 09:11:08,263 DEBUG: View 1 : 0.654088050314
+2016-08-24 09:11:08,301 DEBUG: View 2 : 0.610062893082
+2016-08-24 09:11:08,310 DEBUG: View 3 : 0.572327044025
+2016-08-24 09:11:08,380 DEBUG: Best view : MiRNA__
+2016-08-24 09:11:09,007 DEBUG: Start: Iteration 11
+2016-08-24 09:11:09,024 DEBUG: View 0 : 0.51572327044
+2016-08-24 09:11:09,032 DEBUG: View 1 : 0.559748427673
+2016-08-24 09:11:09,069 DEBUG: View 2 : 0.534591194969
+2016-08-24 09:11:09,077 DEBUG: View 3 : 0.389937106918
+2016-08-24 09:11:09,148 DEBUG: Best view : MiRNA__
+2016-08-24 09:11:09,842 DEBUG: Start: Iteration 12
+2016-08-24 09:11:09,873 DEBUG: View 0 : 0.503144654088
+2016-08-24 09:11:09,881 DEBUG: View 1 : 0.748427672956
+2016-08-24 09:11:09,918 DEBUG: View 2 : 0.540880503145
+2016-08-24 09:11:09,926 DEBUG: View 3 : 0.503144654088
+2016-08-24 09:11:09,999 DEBUG: Best view : MiRNA__
+2016-08-24 09:11:10,742 INFO: Start: Classification
+2016-08-24 09:11:12,499 INFO: Done: Fold number 1
+2016-08-24 09:11:12,499 INFO: Start: Fold number 2
+2016-08-24 09:11:14,013 DEBUG: Start: Iteration 1
+2016-08-24 09:11:14,027 DEBUG: View 0 : 0.617834394904
+2016-08-24 09:11:14,035 DEBUG: View 1 : 0.382165605096
+2016-08-24 09:11:14,062 DEBUG: View 2 : 0.617834394904
+2016-08-24 09:11:14,070 DEBUG: View 3 : 0.528662420382
+2016-08-24 09:11:14,108 DEBUG: Best view : Methyl_
+2016-08-24 09:11:14,183 DEBUG: Start: Iteration 2
+2016-08-24 09:11:14,199 DEBUG: View 0 : 0.496815286624
+2016-08-24 09:11:14,207 DEBUG: View 1 : 0.420382165605
+2016-08-24 09:11:14,243 DEBUG: View 2 : 0.426751592357
+2016-08-24 09:11:14,250 DEBUG: View 3 : 0.426751592357
+2016-08-24 09:11:14,250 WARNING: All bad for iteration 1
+2016-08-24 09:11:14,299 DEBUG: Best view : Methyl_
+2016-08-24 09:11:14,436 DEBUG: Start: Iteration 3
+2016-08-24 09:11:14,452 DEBUG: View 0 : 0.662420382166
+2016-08-24 09:11:14,460 DEBUG: View 1 : 0.464968152866
+2016-08-24 09:11:14,495 DEBUG: View 2 : 0.503184713376
+2016-08-24 09:11:14,503 DEBUG: View 3 : 0.528662420382
+2016-08-24 09:11:14,554 DEBUG: Best view : Methyl_
+2016-08-24 09:11:14,751 DEBUG: Start: Iteration 4
+2016-08-24 09:11:14,767 DEBUG: View 0 : 0.433121019108
+2016-08-24 09:11:14,775 DEBUG: View 1 : 0.496815286624
+2016-08-24 09:11:14,811 DEBUG: View 2 : 0.43949044586
+2016-08-24 09:11:14,818 DEBUG: View 3 : 0.547770700637
+2016-08-24 09:11:14,872 DEBUG: Best view : Clinic_
+2016-08-24 09:11:15,126 DEBUG: Start: Iteration 5
+2016-08-24 09:11:15,143 DEBUG: View 0 : 0.503184713376
+2016-08-24 09:11:15,150 DEBUG: View 1 : 0.687898089172
+2016-08-24 09:11:15,187 DEBUG: View 2 : 0.420382165605
+2016-08-24 09:11:15,194 DEBUG: View 3 : 0.592356687898
+2016-08-24 09:11:15,250 DEBUG: Best view : MiRNA__
+2016-08-24 09:11:15,562 DEBUG: Start: Iteration 6
+2016-08-24 09:11:15,578 DEBUG: View 0 : 0.554140127389
+2016-08-24 09:11:15,585 DEBUG: View 1 : 0.515923566879
+2016-08-24 09:11:15,621 DEBUG: View 2 : 0.452229299363
+2016-08-24 09:11:15,629 DEBUG: View 3 : 0.624203821656
+2016-08-24 09:11:15,687 DEBUG: Best view : Clinic_
+2016-08-24 09:11:16,055 DEBUG: Start: Iteration 7
+2016-08-24 09:11:16,071 DEBUG: View 0 : 0.471337579618
+2016-08-24 09:11:16,079 DEBUG: View 1 : 0.388535031847
+2016-08-24 09:11:16,114 DEBUG: View 2 : 0.420382165605
+2016-08-24 09:11:16,122 DEBUG: View 3 : 0.535031847134
+2016-08-24 09:11:16,182 DEBUG: Best view : Clinic_
+2016-08-24 09:11:16,608 DEBUG: Start: Iteration 8
+2016-08-24 09:11:16,624 DEBUG: View 0 : 0.43949044586
+2016-08-24 09:11:16,632 DEBUG: View 1 : 0.726114649682
+2016-08-24 09:11:16,668 DEBUG: View 2 : 0.605095541401
+2016-08-24 09:11:16,676 DEBUG: View 3 : 0.605095541401
+2016-08-24 09:11:16,739 DEBUG: Best view : MiRNA__
+2016-08-24 09:11:17,220 DEBUG: Start: Iteration 9
+2016-08-24 09:11:17,236 DEBUG: View 0 : 0.579617834395
+2016-08-24 09:11:17,244 DEBUG: View 1 : 0.547770700637
+2016-08-24 09:11:17,280 DEBUG: View 2 : 0.388535031847
+2016-08-24 09:11:17,288 DEBUG: View 3 : 0.458598726115
+2016-08-24 09:11:17,352 DEBUG: Best view : Methyl_
+2016-08-24 09:11:17,896 DEBUG: Start: Iteration 10
+2016-08-24 09:11:17,913 DEBUG: View 0 : 0.464968152866
+2016-08-24 09:11:17,921 DEBUG: View 1 : 0.605095541401
+2016-08-24 09:11:17,958 DEBUG: View 2 : 0.528662420382
+2016-08-24 09:11:17,965 DEBUG: View 3 : 0.40127388535
+2016-08-24 09:11:18,034 DEBUG: Best view : MiRNA__
+2016-08-24 09:11:18,635 DEBUG: Start: Iteration 11
+2016-08-24 09:11:18,651 DEBUG: View 0 : 0.363057324841
+2016-08-24 09:11:18,659 DEBUG: View 1 : 0.573248407643
+2016-08-24 09:11:18,695 DEBUG: View 2 : 0.363057324841
+2016-08-24 09:11:18,703 DEBUG: View 3 : 0.445859872611
+2016-08-24 09:11:18,773 DEBUG: Best view : MiRNA__
+2016-08-24 09:11:19,431 DEBUG: Start: Iteration 12
+2016-08-24 09:11:19,447 DEBUG: View 0 : 0.43949044586
+2016-08-24 09:11:19,455 DEBUG: View 1 : 0.375796178344
+2016-08-24 09:11:19,491 DEBUG: View 2 : 0.515923566879
+2016-08-24 09:11:19,499 DEBUG: View 3 : 0.592356687898
+2016-08-24 09:11:19,571 DEBUG: Best view : Clinic_
+2016-08-24 09:11:20,286 INFO: Start: Classification
+2016-08-24 09:11:21,987 INFO: Done: Fold number 2
+2016-08-24 09:11:21,987 INFO: Done: Classification
+2016-08-24 09:11:21,988 INFO: Info: Time for Classification: 85[s]
+2016-08-24 09:11:21,988 INFO: Start: Result Analysis for Mumbo
+2016-08-24 09:11:28,399 INFO: Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 70.8648800224
+ -On Test : 77.0491803279
+ -On Validation : 81.067961165
+
+Dataset info :
+ -Database name : ModifiedMultiOmic
+ -Labels : No, Yes
+ -Views : Methyl, MiRNA, RNASEQ, Clinical
+ -2 folds
+ - Validation set length : 103 for learning rate : 0.7
+
+Classification configuration :
+ -Algorithm used : Mumbo
+ -Iterations : 1000
+ -Weak Classifiers : DecisionTree with depth 1.0, sub-sampled at 0.006 on Methyl
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on MiRNA
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on RNASEQ
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on Clinical
+
+ Mean average accuracies and stats for each fold :
+ - Fold 0
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0061572327044
+ - Percentage of time chosen : 0.989
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.00627044025157
+ - Percentage of time chosen : 0.006
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00632704402516
+ - Percentage of time chosen : 0.002
+ - On Clinic_ :
+ - Mean average Accuracy : 0.0060251572327
+ - Percentage of time chosen : 0.003
+ - Fold 1
+ - On Methyl_ :
+ - Mean average Accuracy : 0.00602547770701
+ - Percentage of time chosen : 0.992
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0061847133758
+ - Percentage of time chosen : 0.004
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00568152866242
+ - Percentage of time chosen : 0.0
+ - On Clinic_ :
+ - Mean average Accuracy : 0.00628662420382
+ - Percentage of time chosen : 0.004
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.0237952169
+ Accuracy on test : 72.9508196721
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 54.0880503145
+ Accuracy on test : 65.5737704918
+ Accuracy on validation : 65.0485436893
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 57.9357449025
+ Accuracy on test : 69.262295082
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 62.893081761
+ Accuracy on test : 59.8360655738
+ Accuracy on validation : 68.932038835
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 66.2420382166
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 78.640776699
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 64.5675599888
+ Accuracy on test : 67.6229508197
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 59.1194968553
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 69.9029126214
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 66.2420382166
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 62.680767536
+ Accuracy on test : 74.5901639344
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 66.6666666667
+ Accuracy on test : 68.8524590164
+ Accuracy on validation : 76.6990291262
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 68.152866242
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 67.4097664544
+ Accuracy on test : 74.1803278689
+ - Iteration 6
+ Fold 1
+ Accuracy on train : 63.5220125786
+ Accuracy on test : 72.131147541
+ Accuracy on validation : 72.8155339806
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 66.8789808917
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 65.2004967352
+ Accuracy on test : 75.8196721311
+ - Iteration 7
+ Fold 1
+ Accuracy on train : 67.9245283019
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 77.6699029126
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 68.152866242
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 68.038697272
+ Accuracy on test : 75.4098360656
+ - Iteration 8
+ Fold 1
+ Accuracy on train : 64.1509433962
+ Accuracy on test : 66.393442623
+ Accuracy on validation : 71.8446601942
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 71.974522293
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 82.5242718447
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 68.0627328446
+ Accuracy on test : 73.7704918033
+ - Iteration 9
+ Fold 1
+ Accuracy on train : 71.6981132075
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 70.0636942675
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 81.5533980583
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 70.8809037375
+ Accuracy on test : 76.2295081967
+ - Iteration 10
+ Fold 1
+ Accuracy on train : 69.1823899371
+ Accuracy on test : 71.3114754098
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
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+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.0237952169
+ Accuracy on test : 72.9508196721
+ - Iteration 997
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.0237952169
+ Accuracy on test : 72.9508196721
+ - Iteration 998
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.0237952169
+ Accuracy on test : 72.9508196721
+ - Iteration 999
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.0237952169
+ Accuracy on test : 72.9508196721
+ - Iteration 1000
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.0237952169
+ Accuracy on test : 72.9508196721
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:01:13 0:00:01
+ Fold 2 0:01:23 0:00:01
+ Total 0:02:37 0:00:03
+ So a total classification time of 0:01:25.
+
+
+2016-08-24 09:11:29,450 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091129Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091129Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png
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diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091129Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091129Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt
new file mode 100644
index 00000000..6cb12d88
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091129Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt
@@ -0,0 +1,14060 @@
+ Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 70.8648800224
+ -On Test : 77.0491803279
+ -On Validation : 81.067961165
+
+Dataset info :
+ -Database name : ModifiedMultiOmic
+ -Labels : No, Yes
+ -Views : Methyl, MiRNA, RNASEQ, Clinical
+ -2 folds
+ - Validation set length : 103 for learning rate : 0.7
+
+Classification configuration :
+ -Algorithm used : Mumbo
+ -Iterations : 1000
+ -Weak Classifiers : DecisionTree with depth 1.0, sub-sampled at 0.006 on Methyl
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on MiRNA
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on RNASEQ
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on Clinical
+
+ Mean average accuracies and stats for each fold :
+ - Fold 0
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0061572327044
+ - Percentage of time chosen : 0.989
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.00627044025157
+ - Percentage of time chosen : 0.006
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00632704402516
+ - Percentage of time chosen : 0.002
+ - On Clinic_ :
+ - Mean average Accuracy : 0.0060251572327
+ - Percentage of time chosen : 0.003
+ - Fold 1
+ - On Methyl_ :
+ - Mean average Accuracy : 0.00602547770701
+ - Percentage of time chosen : 0.992
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0061847133758
+ - Percentage of time chosen : 0.004
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00568152866242
+ - Percentage of time chosen : 0.0
+ - On Clinic_ :
+ - Mean average Accuracy : 0.00628662420382
+ - Percentage of time chosen : 0.004
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.0237952169
+ Accuracy on test : 72.9508196721
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 54.0880503145
+ Accuracy on test : 65.5737704918
+ Accuracy on validation : 65.0485436893
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 57.9357449025
+ Accuracy on test : 69.262295082
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 62.893081761
+ Accuracy on test : 59.8360655738
+ Accuracy on validation : 68.932038835
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 66.2420382166
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 78.640776699
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 64.5675599888
+ Accuracy on test : 67.6229508197
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 59.1194968553
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 69.9029126214
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 66.2420382166
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 62.680767536
+ Accuracy on test : 74.5901639344
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 66.6666666667
+ Accuracy on test : 68.8524590164
+ Accuracy on validation : 76.6990291262
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 68.152866242
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 67.4097664544
+ Accuracy on test : 74.1803278689
+ - Iteration 6
+ Fold 1
+ Accuracy on train : 63.5220125786
+ Accuracy on test : 72.131147541
+ Accuracy on validation : 72.8155339806
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 66.8789808917
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 65.2004967352
+ Accuracy on test : 75.8196721311
+ - Iteration 7
+ Fold 1
+ Accuracy on train : 67.9245283019
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 77.6699029126
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 68.152866242
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 68.038697272
+ Accuracy on test : 75.4098360656
+ - Iteration 8
+ Fold 1
+ Accuracy on train : 64.1509433962
+ Accuracy on test : 66.393442623
+ Accuracy on validation : 71.8446601942
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 71.974522293
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 82.5242718447
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 68.0627328446
+ Accuracy on test : 73.7704918033
+ - Iteration 9
+ Fold 1
+ Accuracy on train : 71.6981132075
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 70.0636942675
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 81.5533980583
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 70.8809037375
+ Accuracy on test : 76.2295081967
+ - Iteration 10
+ Fold 1
+ Accuracy on train : 69.1823899371
+ Accuracy on test : 71.3114754098
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 69.4267515924
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 69.3045707647
+ Accuracy on test : 75.0
+ - Iteration 11
+ Fold 1
+ Accuracy on train : 71.0691823899
+ Accuracy on test : 72.131147541
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 69.4267515924
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 70.2479669911
+ Accuracy on test : 75.4098360656
+ - Iteration 12
+ Fold 1
+ Accuracy on train : 74.213836478
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 82.5242718447
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 67.5159235669
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
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+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.0237952169
+ Accuracy on test : 72.9508196721
+ - Iteration 999
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.0237952169
+ Accuracy on test : 72.9508196721
+ - Iteration 1000
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.0237952169
+ Accuracy on test : 72.9508196721
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:01:13 0:00:01
+ Fold 2 0:01:23 0:00:01
+ Total 0:02:37 0:00:03
+ So a total classification time of 0:01:25.
+
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091625-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091625-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..d062fb38
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091625-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,14246 @@
+2016-08-24 09:16:25,238 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:16:25,238 INFO: Info: Labels used: No, Yes
+2016-08-24 09:16:25,239 INFO: Info: Length of dataset:347
+2016-08-24 09:16:25,240 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:16:25,240 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:16:25,241 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:16:25,241 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:16:25,241 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:16:25,242 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:16:25,242 INFO: Done: Read Database Files
+2016-08-24 09:16:25,242 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:16:25,245 INFO: Done: Determine validation split
+2016-08-24 09:16:25,245 INFO: Start: Determine 2 folds
+2016-08-24 09:16:25,255 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:16:25,255 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:16:25,255 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:16:25,255 INFO: Done: Determine folds
+2016-08-24 09:16:25,255 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:16:25,256 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:16:25,256 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:16:32,562 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:16:32,563 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:16:34,472 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:16:34,473 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 09:16:51,458 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:16:51,458 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 09:16:53,197 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:16:53,197 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 09:17:30,357 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:17:30,357 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 09:17:30,357 INFO: Start: Fold number 1
+2016-08-24 09:17:32,053 DEBUG: Start: Iteration 1
+2016-08-24 09:17:32,069 DEBUG: View 0 : 0.377358490566
+2016-08-24 09:17:32,077 DEBUG: View 1 : 0.377358490566
+2016-08-24 09:17:32,106 DEBUG: View 2 : 0.622641509434
+2016-08-24 09:17:32,113 DEBUG: View 3 : 0.622641509434
+2016-08-24 09:17:32,155 DEBUG: Best view : Methyl_
+2016-08-24 09:17:32,230 DEBUG: Start: Iteration 2
+2016-08-24 09:17:32,247 DEBUG: View 0 : 0.547169811321
+2016-08-24 09:17:32,255 DEBUG: View 1 : 0.59748427673
+2016-08-24 09:17:32,292 DEBUG: View 2 : 0.547169811321
+2016-08-24 09:17:32,299 DEBUG: View 3 : 0.559748427673
+2016-08-24 09:17:32,344 DEBUG: Best view : MiRNA__
+2016-08-24 09:17:32,475 DEBUG: Start: Iteration 3
+2016-08-24 09:17:32,492 DEBUG: View 0 : 0.641509433962
+2016-08-24 09:17:32,500 DEBUG: View 1 : 0.534591194969
+2016-08-24 09:17:32,536 DEBUG: View 2 : 0.389937106918
+2016-08-24 09:17:32,544 DEBUG: View 3 : 0.59748427673
+2016-08-24 09:17:32,597 DEBUG: Best view : Methyl_
+2016-08-24 09:17:32,792 DEBUG: Start: Iteration 4
+2016-08-24 09:17:32,808 DEBUG: View 0 : 0.389937106918
+2016-08-24 09:17:32,816 DEBUG: View 1 : 0.62893081761
+2016-08-24 09:17:32,852 DEBUG: View 2 : 0.389937106918
+2016-08-24 09:17:32,860 DEBUG: View 3 : 0.446540880503
+2016-08-24 09:17:32,915 DEBUG: Best view : MiRNA__
+2016-08-24 09:17:33,165 DEBUG: Start: Iteration 5
+2016-08-24 09:17:33,182 DEBUG: View 0 : 0.509433962264
+2016-08-24 09:17:33,189 DEBUG: View 1 : 0.603773584906
+2016-08-24 09:17:33,226 DEBUG: View 2 : 0.62893081761
+2016-08-24 09:17:33,233 DEBUG: View 3 : 0.503144654088
+2016-08-24 09:17:33,291 DEBUG: Best view : MiRNA__
+2016-08-24 09:17:33,599 DEBUG: Start: Iteration 6
+2016-08-24 09:17:33,615 DEBUG: View 0 : 0.421383647799
+2016-08-24 09:17:33,623 DEBUG: View 1 : 0.572327044025
+2016-08-24 09:17:33,660 DEBUG: View 2 : 0.553459119497
+2016-08-24 09:17:33,668 DEBUG: View 3 : 0.490566037736
+2016-08-24 09:17:33,727 DEBUG: Best view : MiRNA__
+2016-08-24 09:17:34,092 DEBUG: Start: Iteration 7
+2016-08-24 09:17:34,109 DEBUG: View 0 : 0.553459119497
+2016-08-24 09:17:34,116 DEBUG: View 1 : 0.603773584906
+2016-08-24 09:17:34,153 DEBUG: View 2 : 0.610062893082
+2016-08-24 09:17:34,161 DEBUG: View 3 : 0.433962264151
+2016-08-24 09:17:34,223 DEBUG: Best view : RANSeq_
+2016-08-24 09:17:34,661 DEBUG: Start: Iteration 8
+2016-08-24 09:17:34,677 DEBUG: View 0 : 0.522012578616
+2016-08-24 09:17:34,685 DEBUG: View 1 : 0.654088050314
+2016-08-24 09:17:34,721 DEBUG: View 2 : 0.48427672956
+2016-08-24 09:17:34,729 DEBUG: View 3 : 0.477987421384
+2016-08-24 09:17:34,793 DEBUG: Best view : MiRNA__
+2016-08-24 09:17:35,289 DEBUG: Start: Iteration 9
+2016-08-24 09:17:35,305 DEBUG: View 0 : 0.358490566038
+2016-08-24 09:17:35,313 DEBUG: View 1 : 0.534591194969
+2016-08-24 09:17:35,350 DEBUG: View 2 : 0.522012578616
+2016-08-24 09:17:35,357 DEBUG: View 3 : 0.704402515723
+2016-08-24 09:17:35,424 DEBUG: Best view : Clinic_
+2016-08-24 09:17:35,976 DEBUG: Start: Iteration 10
+2016-08-24 09:17:35,992 DEBUG: View 0 : 0.666666666667
+2016-08-24 09:17:36,000 DEBUG: View 1 : 0.616352201258
+2016-08-24 09:17:36,037 DEBUG: View 2 : 0.415094339623
+2016-08-24 09:17:36,044 DEBUG: View 3 : 0.465408805031
+2016-08-24 09:17:36,112 DEBUG: Best view : Methyl_
+2016-08-24 09:17:36,725 DEBUG: Start: Iteration 11
+2016-08-24 09:17:36,742 DEBUG: View 0 : 0.427672955975
+2016-08-24 09:17:36,749 DEBUG: View 1 : 0.534591194969
+2016-08-24 09:17:36,786 DEBUG: View 2 : 0.446540880503
+2016-08-24 09:17:36,793 DEBUG: View 3 : 0.522012578616
+2016-08-24 09:17:36,864 DEBUG: Best view : Clinic_
+2016-08-24 09:17:37,731 DEBUG: Start: Iteration 12
+2016-08-24 09:17:37,748 DEBUG: View 0 : 0.440251572327
+2016-08-24 09:17:37,756 DEBUG: View 1 : 0.37106918239
+2016-08-24 09:17:37,793 DEBUG: View 2 : 0.553459119497
+2016-08-24 09:17:37,800 DEBUG: View 3 : 0.509433962264
+2016-08-24 09:17:37,874 DEBUG: Best view : RANSeq_
+2016-08-24 09:17:38,629 INFO: Start: Classification
+2016-08-24 09:17:40,368 INFO: Done: Fold number 1
+2016-08-24 09:17:40,368 INFO: Start: Fold number 2
+2016-08-24 09:17:41,934 DEBUG: Start: Iteration 1
+2016-08-24 09:17:41,952 DEBUG: View 0 : 0.5
+2016-08-24 09:17:41,960 DEBUG: View 1 : 0.379746835443
+2016-08-24 09:17:41,988 DEBUG: View 2 : 0.620253164557
+2016-08-24 09:17:41,996 DEBUG: View 3 : 0.620253164557
+2016-08-24 09:17:42,041 DEBUG: Best view : Methyl_
+2016-08-24 09:17:42,117 DEBUG: Start: Iteration 2
+2016-08-24 09:17:42,133 DEBUG: View 0 : 0.436708860759
+2016-08-24 09:17:42,141 DEBUG: View 1 : 0.354430379747
+2016-08-24 09:17:42,178 DEBUG: View 2 : 0.430379746835
+2016-08-24 09:17:42,185 DEBUG: View 3 : 0.556962025316
+2016-08-24 09:17:42,230 DEBUG: Best view : Clinic_
+2016-08-24 09:17:42,361 DEBUG: Start: Iteration 3
+2016-08-24 09:17:42,377 DEBUG: View 0 : 0.481012658228
+2016-08-24 09:17:42,385 DEBUG: View 1 : 0.373417721519
+2016-08-24 09:17:42,421 DEBUG: View 2 : 0.386075949367
+2016-08-24 09:17:42,429 DEBUG: View 3 : 0.386075949367
+2016-08-24 09:17:42,429 WARNING: All bad for iteration 2
+2016-08-24 09:17:42,482 DEBUG: Best view : Methyl_
+2016-08-24 09:17:42,675 DEBUG: Start: Iteration 4
+2016-08-24 09:17:42,692 DEBUG: View 0 : 0.506329113924
+2016-08-24 09:17:42,699 DEBUG: View 1 : 0.544303797468
+2016-08-24 09:17:42,735 DEBUG: View 2 : 0.550632911392
+2016-08-24 09:17:42,743 DEBUG: View 3 : 0.626582278481
+2016-08-24 09:17:42,798 DEBUG: Best view : Clinic_
+2016-08-24 09:17:43,047 DEBUG: Start: Iteration 5
+2016-08-24 09:17:43,063 DEBUG: View 0 : 0.588607594937
+2016-08-24 09:17:43,071 DEBUG: View 1 : 0.601265822785
+2016-08-24 09:17:43,107 DEBUG: View 2 : 0.443037974684
+2016-08-24 09:17:43,114 DEBUG: View 3 : 0.544303797468
+2016-08-24 09:17:43,171 DEBUG: Best view : MiRNA__
+2016-08-24 09:17:43,477 DEBUG: Start: Iteration 6
+2016-08-24 09:17:43,494 DEBUG: View 0 : 0.481012658228
+2016-08-24 09:17:43,501 DEBUG: View 1 : 0.601265822785
+2016-08-24 09:17:43,538 DEBUG: View 2 : 0.493670886076
+2016-08-24 09:17:43,545 DEBUG: View 3 : 0.493670886076
+2016-08-24 09:17:43,605 DEBUG: Best view : MiRNA__
+2016-08-24 09:17:43,969 DEBUG: Start: Iteration 7
+2016-08-24 09:17:43,985 DEBUG: View 0 : 0.322784810127
+2016-08-24 09:17:43,993 DEBUG: View 1 : 0.474683544304
+2016-08-24 09:17:44,029 DEBUG: View 2 : 0.417721518987
+2016-08-24 09:17:44,036 DEBUG: View 3 : 0.601265822785
+2016-08-24 09:17:44,097 DEBUG: Best view : Clinic_
+2016-08-24 09:17:44,516 DEBUG: Start: Iteration 8
+2016-08-24 09:17:44,533 DEBUG: View 0 : 0.525316455696
+2016-08-24 09:17:44,540 DEBUG: View 1 : 0.632911392405
+2016-08-24 09:17:44,577 DEBUG: View 2 : 0.607594936709
+2016-08-24 09:17:44,584 DEBUG: View 3 : 0.411392405063
+2016-08-24 09:17:44,648 DEBUG: Best view : MiRNA__
+2016-08-24 09:17:45,123 DEBUG: Start: Iteration 9
+2016-08-24 09:17:45,139 DEBUG: View 0 : 0.677215189873
+2016-08-24 09:17:45,147 DEBUG: View 1 : 0.398734177215
+2016-08-24 09:17:45,183 DEBUG: View 2 : 0.575949367089
+2016-08-24 09:17:45,191 DEBUG: View 3 : 0.462025316456
+2016-08-24 09:17:45,257 DEBUG: Best view : Methyl_
+2016-08-24 09:17:45,800 DEBUG: Start: Iteration 10
+2016-08-24 09:17:45,817 DEBUG: View 0 : 0.664556962025
+2016-08-24 09:17:45,825 DEBUG: View 1 : 0.632911392405
+2016-08-24 09:17:45,862 DEBUG: View 2 : 0.613924050633
+2016-08-24 09:17:45,869 DEBUG: View 3 : 0.601265822785
+2016-08-24 09:17:45,941 DEBUG: Best view : Methyl_
+2016-08-24 09:17:46,549 DEBUG: Start: Iteration 11
+2016-08-24 09:17:46,566 DEBUG: View 0 : 0.506329113924
+2016-08-24 09:17:46,573 DEBUG: View 1 : 0.373417721519
+2016-08-24 09:17:46,610 DEBUG: View 2 : 0.620253164557
+2016-08-24 09:17:46,617 DEBUG: View 3 : 0.563291139241
+2016-08-24 09:17:46,688 DEBUG: Best view : RANSeq_
+2016-08-24 09:17:47,371 DEBUG: Start: Iteration 12
+2016-08-24 09:17:47,390 DEBUG: View 0 : 0.405063291139
+2016-08-24 09:17:47,399 DEBUG: View 1 : 0.658227848101
+2016-08-24 09:17:47,435 DEBUG: View 2 : 0.620253164557
+2016-08-24 09:17:47,443 DEBUG: View 3 : 0.626582278481
+2016-08-24 09:17:47,517 DEBUG: Best view : MiRNA__
+2016-08-24 09:17:48,252 INFO: Start: Classification
+2016-08-24 09:17:49,990 INFO: Done: Fold number 2
+2016-08-24 09:17:49,990 INFO: Done: Classification
+2016-08-24 09:17:49,990 INFO: Info: Time for Classification: 84[s]
+2016-08-24 09:17:49,990 INFO: Start: Result Analysis for Mumbo
+2016-08-24 09:17:55,375 INFO: Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 68.4519544622
+ -On Test : 76.6393442623
+ -On Validation : 76.213592233
+
+Dataset info :
+ -Database name : ModifiedMultiOmic
+ -Labels : No, Yes
+ -Views : Methyl, MiRNA, RNASEQ, Clinical
+ -2 folds
+ - Validation set length : 103 for learning rate : 0.7
+
+Classification configuration :
+ -Algorithm used : Mumbo
+ -Iterations : 1000
+ -Weak Classifiers : DecisionTree with depth 1.0, sub-sampled at 0.0075 on Methyl
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on MiRNA
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on RNASEQ
+ -DecisionTree with depth 1.0, sub-sampled at 0.0065 on Clinical
+
+ Mean average accuracies and stats for each fold :
+ - Fold 0
+ - On Methyl_ :
+ - Mean average Accuracy : 0.00585534591195
+ - Percentage of time chosen : 0.991
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.00662893081761
+ - Percentage of time chosen : 0.005
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00616352201258
+ - Percentage of time chosen : 0.002
+ - On Clinic_ :
+ - Mean average Accuracy : 0.00633333333333
+ - Percentage of time chosen : 0.002
+ - Fold 1
+ - On Methyl_ :
+ - Mean average Accuracy : 0.00609493670886
+ - Percentage of time chosen : 0.992
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0060253164557
+ - Percentage of time chosen : 0.004
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00637974683544
+ - Percentage of time chosen : 0.001
+ - On Clinic_ :
+ - Mean average Accuracy : 0.00649367088608
+ - Percentage of time chosen : 0.003
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 59.748427673
+ Accuracy on test : 53.2786885246
+ Accuracy on validation : 67.9611650485
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 55.6962025316
+ Accuracy on test : 64.7540983607
+ Accuracy on validation : 63.1067961165
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 57.7223151023
+ Accuracy on test : 59.0163934426
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 64.1509433962
+ Accuracy on test : 69.6721311475
+ Accuracy on validation : 75.7281553398
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 55.6962025316
+ Accuracy on test : 64.7540983607
+ Accuracy on validation : 63.1067961165
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 59.9235729639
+ Accuracy on test : 67.2131147541
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 62.893081761
+ Accuracy on test : 71.3114754098
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 62.6582278481
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 69.9029126214
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 62.7756548046
+ Accuracy on test : 72.5409836066
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 63.5220125786
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 63.2911392405
+ Accuracy on test : 72.131147541
+ Accuracy on validation : 70.8737864078
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 63.4065759096
+ Accuracy on test : 72.5409836066
+ - Iteration 6
+ Fold 1
+ Accuracy on train : 62.893081761
+ Accuracy on test : 71.3114754098
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 69.9029126214
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 62.4591991084
+ Accuracy on test : 72.5409836066
+ - Iteration 7
+ Fold 1
+ Accuracy on train : 64.1509433962
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 74.7572815534
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 69.6721311475
+ Accuracy on validation : 70.8737864078
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 63.088129926
+ Accuracy on test : 71.7213114754
+ - Iteration 8
+ Fold 1
+ Accuracy on train : 62.893081761
+ Accuracy on test : 74.5901639344
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 65.1898734177
+ Accuracy on test : 72.131147541
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 64.0414775894
+ Accuracy on test : 73.3606557377
+ - Iteration 9
+ Fold 1
+ Accuracy on train : 64.7798742138
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 67.7215189873
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 75.7281553398
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 66.2506966006
+ Accuracy on test : 72.9508196721
+ - Iteration 10
+ Fold 1
+ Accuracy on train : 66.0377358491
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 76.6990291262
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 70.8860759494
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 74.7572815534
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 68.4619058992
+ Accuracy on test : 77.868852459
+ - Iteration 11
+ Fold 1
+ Accuracy on train : 70.4402515723
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 72.7848101266
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 71.6125308495
+ Accuracy on test : 75.8196721311
+ - Iteration 12
+ Fold 1
+ Accuracy on train : 69.1823899371
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 67.7215189873
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 68.4519544622
+ Accuracy on test : 76.6393442623
+ - Iteration 13
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 14
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
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+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:01:13 0:00:01
+ Fold 2 0:01:22 0:00:01
+ Total 0:02:36 0:00:03
+ So a total classification time of 0:01:24.
+
+
+2016-08-24 09:17:56,318 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091756Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091756Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png
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diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091756Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091756Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt
new file mode 100644
index 00000000..616b19ab
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-091756Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt
@@ -0,0 +1,14060 @@
+ Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 68.4519544622
+ -On Test : 76.6393442623
+ -On Validation : 76.213592233
+
+Dataset info :
+ -Database name : ModifiedMultiOmic
+ -Labels : No, Yes
+ -Views : Methyl, MiRNA, RNASEQ, Clinical
+ -2 folds
+ - Validation set length : 103 for learning rate : 0.7
+
+Classification configuration :
+ -Algorithm used : Mumbo
+ -Iterations : 1000
+ -Weak Classifiers : DecisionTree with depth 1.0, sub-sampled at 0.0075 on Methyl
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on MiRNA
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on RNASEQ
+ -DecisionTree with depth 1.0, sub-sampled at 0.0065 on Clinical
+
+ Mean average accuracies and stats for each fold :
+ - Fold 0
+ - On Methyl_ :
+ - Mean average Accuracy : 0.00585534591195
+ - Percentage of time chosen : 0.991
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.00662893081761
+ - Percentage of time chosen : 0.005
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00616352201258
+ - Percentage of time chosen : 0.002
+ - On Clinic_ :
+ - Mean average Accuracy : 0.00633333333333
+ - Percentage of time chosen : 0.002
+ - Fold 1
+ - On Methyl_ :
+ - Mean average Accuracy : 0.00609493670886
+ - Percentage of time chosen : 0.992
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0060253164557
+ - Percentage of time chosen : 0.004
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00637974683544
+ - Percentage of time chosen : 0.001
+ - On Clinic_ :
+ - Mean average Accuracy : 0.00649367088608
+ - Percentage of time chosen : 0.003
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 59.748427673
+ Accuracy on test : 53.2786885246
+ Accuracy on validation : 67.9611650485
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 55.6962025316
+ Accuracy on test : 64.7540983607
+ Accuracy on validation : 63.1067961165
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 57.7223151023
+ Accuracy on test : 59.0163934426
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 64.1509433962
+ Accuracy on test : 69.6721311475
+ Accuracy on validation : 75.7281553398
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 55.6962025316
+ Accuracy on test : 64.7540983607
+ Accuracy on validation : 63.1067961165
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 59.9235729639
+ Accuracy on test : 67.2131147541
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 62.893081761
+ Accuracy on test : 71.3114754098
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 62.6582278481
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 69.9029126214
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 62.7756548046
+ Accuracy on test : 72.5409836066
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 63.5220125786
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 63.2911392405
+ Accuracy on test : 72.131147541
+ Accuracy on validation : 70.8737864078
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 63.4065759096
+ Accuracy on test : 72.5409836066
+ - Iteration 6
+ Fold 1
+ Accuracy on train : 62.893081761
+ Accuracy on test : 71.3114754098
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 69.9029126214
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 62.4591991084
+ Accuracy on test : 72.5409836066
+ - Iteration 7
+ Fold 1
+ Accuracy on train : 64.1509433962
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 74.7572815534
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 69.6721311475
+ Accuracy on validation : 70.8737864078
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 63.088129926
+ Accuracy on test : 71.7213114754
+ - Iteration 8
+ Fold 1
+ Accuracy on train : 62.893081761
+ Accuracy on test : 74.5901639344
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 65.1898734177
+ Accuracy on test : 72.131147541
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 64.0414775894
+ Accuracy on test : 73.3606557377
+ - Iteration 9
+ Fold 1
+ Accuracy on train : 64.7798742138
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 67.7215189873
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 75.7281553398
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 66.2506966006
+ Accuracy on test : 72.9508196721
+ - Iteration 10
+ Fold 1
+ Accuracy on train : 66.0377358491
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 76.6990291262
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 70.8860759494
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 74.7572815534
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 68.4619058992
+ Accuracy on test : 77.868852459
+ - Iteration 11
+ Fold 1
+ Accuracy on train : 70.4402515723
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 72.7848101266
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 71.6125308495
+ Accuracy on test : 75.8196721311
+ - Iteration 12
+ Fold 1
+ Accuracy on train : 69.1823899371
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 67.7215189873
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 68.4519544622
+ Accuracy on test : 76.6393442623
+ - Iteration 13
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 14
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 15
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 16
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 17
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 18
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 19
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 20
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 21
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 22
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 23
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 24
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 25
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 26
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 27
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
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+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 974
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 975
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 976
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 977
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 978
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 979
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 980
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 981
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 982
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 983
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 984
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 985
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 986
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 987
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 988
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 989
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 990
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 991
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 992
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 993
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 994
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 995
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 996
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 997
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 998
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 999
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+ - Iteration 1000
+ Fold 1
+ Accuracy on train : 62.2641509434
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.0253164557
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1447336995
+ Accuracy on test : 72.9508196721
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:01:13 0:00:01
+ Fold 2 0:01:22 0:00:01
+ Total 0:02:36 0:00:03
+ So a total classification time of 0:01:24.
+
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092030-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092030-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..3426f1a2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092030-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,14248 @@
+2016-08-24 09:20:30,659 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:20:30,660 INFO: Info: Labels used: No, Yes
+2016-08-24 09:20:30,660 INFO: Info: Length of dataset:347
+2016-08-24 09:20:30,661 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:20:30,661 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:20:30,662 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:20:30,662 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:20:30,663 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:20:30,663 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:20:30,663 INFO: Done: Read Database Files
+2016-08-24 09:20:30,663 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:20:30,667 INFO: Done: Determine validation split
+2016-08-24 09:20:30,667 INFO: Start: Determine 2 folds
+2016-08-24 09:20:30,678 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:20:30,678 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:20:30,678 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:20:30,678 INFO: Done: Determine folds
+2016-08-24 09:20:30,678 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:20:30,678 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:20:30,679 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:20:37,995 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:20:37,995 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:20:39,915 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:20:39,916 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 09:20:56,530 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:20:56,530 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 09:20:58,283 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:20:58,284 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 09:21:35,801 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:21:35,802 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 09:21:35,802 INFO: Start: Fold number 1
+2016-08-24 09:21:37,555 DEBUG: Start: Iteration 1
+2016-08-24 09:21:37,575 DEBUG: View 0 : 0.375
+2016-08-24 09:21:37,583 DEBUG: View 1 : 0.625
+2016-08-24 09:21:37,620 DEBUG: View 2 : 0.375
+2016-08-24 09:21:37,628 DEBUG: View 3 : 0.625
+2016-08-24 09:21:37,670 DEBUG: Best view : MiRNA__
+2016-08-24 09:21:37,743 DEBUG: Start: Iteration 2
+2016-08-24 09:21:37,760 DEBUG: View 0 : 0.4625
+2016-08-24 09:21:37,768 DEBUG: View 1 : 0.6875
+2016-08-24 09:21:37,805 DEBUG: View 2 : 0.5375
+2016-08-24 09:21:37,812 DEBUG: View 3 : 0.39375
+2016-08-24 09:21:37,858 DEBUG: Best view : MiRNA__
+2016-08-24 09:21:37,989 DEBUG: Start: Iteration 3
+2016-08-24 09:21:38,006 DEBUG: View 0 : 0.43125
+2016-08-24 09:21:38,014 DEBUG: View 1 : 0.7
+2016-08-24 09:21:38,050 DEBUG: View 2 : 0.41875
+2016-08-24 09:21:38,058 DEBUG: View 3 : 0.38125
+2016-08-24 09:21:38,112 DEBUG: Best view : MiRNA__
+2016-08-24 09:21:38,303 DEBUG: Start: Iteration 4
+2016-08-24 09:21:38,320 DEBUG: View 0 : 0.425
+2016-08-24 09:21:38,328 DEBUG: View 1 : 0.63125
+2016-08-24 09:21:38,365 DEBUG: View 2 : 0.525
+2016-08-24 09:21:38,372 DEBUG: View 3 : 0.44375
+2016-08-24 09:21:38,429 DEBUG: Best view : MiRNA__
+2016-08-24 09:21:38,678 DEBUG: Start: Iteration 5
+2016-08-24 09:21:38,695 DEBUG: View 0 : 0.56875
+2016-08-24 09:21:38,703 DEBUG: View 1 : 0.45625
+2016-08-24 09:21:38,739 DEBUG: View 2 : 0.575
+2016-08-24 09:21:38,747 DEBUG: View 3 : 0.5125
+2016-08-24 09:21:38,806 DEBUG: Best view : RANSeq_
+2016-08-24 09:21:39,130 DEBUG: Start: Iteration 6
+2016-08-24 09:21:39,147 DEBUG: View 0 : 0.4625
+2016-08-24 09:21:39,155 DEBUG: View 1 : 0.4375
+2016-08-24 09:21:39,192 DEBUG: View 2 : 0.54375
+2016-08-24 09:21:39,199 DEBUG: View 3 : 0.3875
+2016-08-24 09:21:39,260 DEBUG: Best view : RANSeq_
+2016-08-24 09:21:39,657 DEBUG: Start: Iteration 7
+2016-08-24 09:21:39,673 DEBUG: View 0 : 0.60625
+2016-08-24 09:21:39,681 DEBUG: View 1 : 0.4
+2016-08-24 09:21:39,717 DEBUG: View 2 : 0.54375
+2016-08-24 09:21:39,725 DEBUG: View 3 : 0.6125
+2016-08-24 09:21:39,788 DEBUG: Best view : Methyl_
+2016-08-24 09:21:40,246 DEBUG: Start: Iteration 8
+2016-08-24 09:21:40,263 DEBUG: View 0 : 0.43125
+2016-08-24 09:21:40,271 DEBUG: View 1 : 0.7
+2016-08-24 09:21:40,307 DEBUG: View 2 : 0.44375
+2016-08-24 09:21:40,315 DEBUG: View 3 : 0.50625
+2016-08-24 09:21:40,381 DEBUG: Best view : MiRNA__
+2016-08-24 09:21:40,897 DEBUG: Start: Iteration 9
+2016-08-24 09:21:40,914 DEBUG: View 0 : 0.41875
+2016-08-24 09:21:40,922 DEBUG: View 1 : 0.30625
+2016-08-24 09:21:40,961 DEBUG: View 2 : 0.35625
+2016-08-24 09:21:40,968 DEBUG: View 3 : 0.425
+2016-08-24 09:21:40,968 WARNING: All bad for iteration 8
+2016-08-24 09:21:41,036 DEBUG: Best view : Clinic_
+2016-08-24 09:21:41,611 DEBUG: Start: Iteration 10
+2016-08-24 09:21:41,628 DEBUG: View 0 : 0.70625
+2016-08-24 09:21:41,635 DEBUG: View 1 : 0.6375
+2016-08-24 09:21:41,672 DEBUG: View 2 : 0.55625
+2016-08-24 09:21:41,679 DEBUG: View 3 : 0.39375
+2016-08-24 09:21:41,750 DEBUG: Best view : Methyl_
+2016-08-24 09:21:42,388 DEBUG: Start: Iteration 11
+2016-08-24 09:21:42,404 DEBUG: View 0 : 0.40625
+2016-08-24 09:21:42,412 DEBUG: View 1 : 0.79375
+2016-08-24 09:21:42,449 DEBUG: View 2 : 0.425
+2016-08-24 09:21:42,456 DEBUG: View 3 : 0.525
+2016-08-24 09:21:42,530 DEBUG: Best view : MiRNA__
+2016-08-24 09:21:43,407 DEBUG: Start: Iteration 12
+2016-08-24 09:21:43,424 DEBUG: View 0 : 0.4875
+2016-08-24 09:21:43,432 DEBUG: View 1 : 0.4625
+2016-08-24 09:21:43,468 DEBUG: View 2 : 0.43125
+2016-08-24 09:21:43,475 DEBUG: View 3 : 0.44375
+2016-08-24 09:21:43,476 WARNING: All bad for iteration 11
+2016-08-24 09:21:43,550 DEBUG: Best view : MiRNA__
+2016-08-24 09:21:44,302 INFO: Start: Classification
+2016-08-24 09:21:46,079 INFO: Done: Fold number 1
+2016-08-24 09:21:46,079 INFO: Start: Fold number 2
+2016-08-24 09:21:47,689 DEBUG: Start: Iteration 1
+2016-08-24 09:21:47,704 DEBUG: View 0 : 0.37037037037
+2016-08-24 09:21:47,712 DEBUG: View 1 : 0.364197530864
+2016-08-24 09:21:47,741 DEBUG: View 2 : 0.62962962963
+2016-08-24 09:21:47,748 DEBUG: View 3 : 0.37037037037
+2016-08-24 09:21:47,790 DEBUG: Best view : Methyl_
+2016-08-24 09:21:47,866 DEBUG: Start: Iteration 2
+2016-08-24 09:21:47,883 DEBUG: View 0 : 0.475308641975
+2016-08-24 09:21:47,891 DEBUG: View 1 : 0.345679012346
+2016-08-24 09:21:47,928 DEBUG: View 2 : 0.518518518519
+2016-08-24 09:21:47,935 DEBUG: View 3 : 0.574074074074
+2016-08-24 09:21:47,981 DEBUG: Best view : Clinic_
+2016-08-24 09:21:48,116 DEBUG: Start: Iteration 3
+2016-08-24 09:21:48,133 DEBUG: View 0 : 0.530864197531
+2016-08-24 09:21:48,141 DEBUG: View 1 : 0.561728395062
+2016-08-24 09:21:48,177 DEBUG: View 2 : 0.506172839506
+2016-08-24 09:21:48,185 DEBUG: View 3 : 0.518518518519
+2016-08-24 09:21:48,239 DEBUG: Best view : MiRNA__
+2016-08-24 09:21:48,435 DEBUG: Start: Iteration 4
+2016-08-24 09:21:48,452 DEBUG: View 0 : 0.450617283951
+2016-08-24 09:21:48,460 DEBUG: View 1 : 0.432098765432
+2016-08-24 09:21:48,497 DEBUG: View 2 : 0.481481481481
+2016-08-24 09:21:48,504 DEBUG: View 3 : 0.41975308642
+2016-08-24 09:21:48,504 WARNING: All bad for iteration 3
+2016-08-24 09:21:48,560 DEBUG: Best view : Clinic_
+2016-08-24 09:21:48,815 DEBUG: Start: Iteration 5
+2016-08-24 09:21:48,831 DEBUG: View 0 : 0.666666666667
+2016-08-24 09:21:48,839 DEBUG: View 1 : 0.487654320988
+2016-08-24 09:21:48,876 DEBUG: View 2 : 0.512345679012
+2016-08-24 09:21:48,884 DEBUG: View 3 : 0.41975308642
+2016-08-24 09:21:48,942 DEBUG: Best view : Methyl_
+2016-08-24 09:21:49,259 DEBUG: Start: Iteration 6
+2016-08-24 09:21:49,275 DEBUG: View 0 : 0.567901234568
+2016-08-24 09:21:49,283 DEBUG: View 1 : 0.703703703704
+2016-08-24 09:21:49,320 DEBUG: View 2 : 0.432098765432
+2016-08-24 09:21:49,328 DEBUG: View 3 : 0.604938271605
+2016-08-24 09:21:49,389 DEBUG: Best view : MiRNA__
+2016-08-24 09:21:49,764 DEBUG: Start: Iteration 7
+2016-08-24 09:21:49,781 DEBUG: View 0 : 0.41975308642
+2016-08-24 09:21:49,789 DEBUG: View 1 : 0.617283950617
+2016-08-24 09:21:49,825 DEBUG: View 2 : 0.438271604938
+2016-08-24 09:21:49,833 DEBUG: View 3 : 0.382716049383
+2016-08-24 09:21:49,896 DEBUG: Best view : MiRNA__
+2016-08-24 09:21:50,330 DEBUG: Start: Iteration 8
+2016-08-24 09:21:50,347 DEBUG: View 0 : 0.524691358025
+2016-08-24 09:21:50,355 DEBUG: View 1 : 0.574074074074
+2016-08-24 09:21:50,391 DEBUG: View 2 : 0.469135802469
+2016-08-24 09:21:50,398 DEBUG: View 3 : 0.432098765432
+2016-08-24 09:21:50,464 DEBUG: Best view : MiRNA__
+2016-08-24 09:21:50,959 DEBUG: Start: Iteration 9
+2016-08-24 09:21:50,975 DEBUG: View 0 : 0.475308641975
+2016-08-24 09:21:50,983 DEBUG: View 1 : 0.432098765432
+2016-08-24 09:21:51,020 DEBUG: View 2 : 0.456790123457
+2016-08-24 09:21:51,028 DEBUG: View 3 : 0.524691358025
+2016-08-24 09:21:51,095 DEBUG: Best view : Clinic_
+2016-08-24 09:21:51,646 DEBUG: Start: Iteration 10
+2016-08-24 09:21:51,663 DEBUG: View 0 : 0.524691358025
+2016-08-24 09:21:51,670 DEBUG: View 1 : 0.395061728395
+2016-08-24 09:21:51,707 DEBUG: View 2 : 0.475308641975
+2016-08-24 09:21:51,714 DEBUG: View 3 : 0.537037037037
+2016-08-24 09:21:51,784 DEBUG: Best view : Methyl_
+2016-08-24 09:21:52,399 DEBUG: Start: Iteration 11
+2016-08-24 09:21:52,416 DEBUG: View 0 : 0.666666666667
+2016-08-24 09:21:52,424 DEBUG: View 1 : 0.574074074074
+2016-08-24 09:21:52,460 DEBUG: View 2 : 0.555555555556
+2016-08-24 09:21:52,467 DEBUG: View 3 : 0.5
+2016-08-24 09:21:52,539 DEBUG: Best view : Methyl_
+2016-08-24 09:21:53,381 DEBUG: Start: Iteration 12
+2016-08-24 09:21:53,398 DEBUG: View 0 : 0.512345679012
+2016-08-24 09:21:53,406 DEBUG: View 1 : 0.475308641975
+2016-08-24 09:21:53,442 DEBUG: View 2 : 0.586419753086
+2016-08-24 09:21:53,450 DEBUG: View 3 : 0.456790123457
+2016-08-24 09:21:53,524 DEBUG: Best view : RANSeq_
+2016-08-24 09:21:54,276 INFO: Start: Classification
+2016-08-24 09:21:56,037 INFO: Done: Fold number 2
+2016-08-24 09:21:56,037 INFO: Done: Classification
+2016-08-24 09:21:56,037 INFO: Info: Time for Classification: 85[s]
+2016-08-24 09:21:56,037 INFO: Start: Result Analysis for Mumbo
+2016-08-24 09:22:01,520 INFO: Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 76.1072530864
+ -On Test : 79.0983606557
+ -On Validation : 83.0097087379
+
+Dataset info :
+ -Database name : ModifiedMultiOmic
+ -Labels : No, Yes
+ -Views : Methyl, MiRNA, RNASEQ, Clinical
+ -2 folds
+ - Validation set length : 103 for learning rate : 0.7
+
+Classification configuration :
+ -Algorithm used : Mumbo
+ -Iterations : 1000
+ -Weak Classifiers : DecisionTree with depth 1.0, sub-sampled at 0.006 on Methyl
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on MiRNA
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on RNASEQ
+ -DecisionTree with depth 1.0, sub-sampled at 0.008 on Clinical
+
+ Mean average accuracies and stats for each fold :
+ - Fold 0
+ - On Methyl_ :
+ - Mean average Accuracy : 0.00578125
+ - Percentage of time chosen : 0.99
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0068375
+ - Percentage of time chosen : 0.007
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00573125
+ - Percentage of time chosen : 0.002
+ - On Clinic_ :
+ - Mean average Accuracy : 0.00565
+ - Percentage of time chosen : 0.001
+ - Fold 1
+ - On Methyl_ :
+ - Mean average Accuracy : 0.00618518518519
+ - Percentage of time chosen : 0.992
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.00596296296296
+ - Percentage of time chosen : 0.004
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00606172839506
+ - Percentage of time chosen : 0.001
+ - On Clinic_ :
+ - Mean average Accuracy : 0.00574074074074
+ - Percentage of time chosen : 0.003
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 68.75
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 57.4074074074
+ Accuracy on test : 68.0327868852
+ Accuracy on validation : 70.8737864078
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 63.0787037037
+ Accuracy on test : 72.5409836066
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 70.0
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 57.4074074074
+ Accuracy on test : 68.0327868852
+ Accuracy on validation : 70.8737864078
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 63.7037037037
+ Accuracy on test : 72.9508196721
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 69.375
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 57.4074074074
+ Accuracy on test : 68.0327868852
+ Accuracy on validation : 70.8737864078
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 63.3912037037
+ Accuracy on test : 74.5901639344
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 69.375
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 77.6699029126
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 60.4938271605
+ Accuracy on test : 70.4918032787
+ Accuracy on validation : 71.8446601942
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 64.9344135802
+ Accuracy on test : 75.8196721311
+ - Iteration 6
+ Fold 1
+ Accuracy on train : 68.75
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 76.6990291262
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 67.2839506173
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 68.0169753086
+ Accuracy on test : 75.4098360656
+ - Iteration 7
+ Fold 1
+ Accuracy on train : 70.0
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 78.640776699
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 66.049382716
+ Accuracy on test : 72.131147541
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 68.024691358
+ Accuracy on test : 74.5901639344
+ - Iteration 8
+ Fold 1
+ Accuracy on train : 75.625
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 67.2839506173
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 71.8446601942
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 71.4544753086
+ Accuracy on test : 77.4590163934
+ - Iteration 9
+ Fold 1
+ Accuracy on train : 75.625
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 84.4660194175
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 66.049382716
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 73.786407767
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 70.837191358
+ Accuracy on test : 77.4590163934
+ - Iteration 10
+ Fold 1
+ Accuracy on train : 73.75
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 81.5533980583
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 70.987654321
+ Accuracy on test : 71.3114754098
+ Accuracy on validation : 74.7572815534
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 72.3688271605
+ Accuracy on test : 76.6393442623
+ - Iteration 11
+ Fold 1
+ Accuracy on train : 79.375
+ Accuracy on test : 85.2459016393
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 74.0740740741
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 77.6699029126
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 76.724537037
+ Accuracy on test : 80.3278688525
+ - Iteration 12
+ Fold 1
+ Accuracy on train : 79.375
+ Accuracy on test : 85.2459016393
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 72.8395061728
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 76.1072530864
+ Accuracy on test : 79.0983606557
+ - Iteration 13
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 14
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 15
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 16
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 17
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 18
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 19
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 20
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 21
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 22
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 23
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 24
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 25
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 26
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 27
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 28
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 29
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 30
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 31
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 32
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 33
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 34
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 35
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 36
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 37
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 38
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 39
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 40
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 41
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 42
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 43
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
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+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:01:13 0:00:01
+ Fold 2 0:01:23 0:00:01
+ Total 0:02:37 0:00:03
+ So a total classification time of 0:01:25.
+
+
+2016-08-24 09:22:02,444 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092202Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092202Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png
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diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092202Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092202Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt
new file mode 100644
index 00000000..42ada44b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092202Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt
@@ -0,0 +1,14060 @@
+ Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 76.1072530864
+ -On Test : 79.0983606557
+ -On Validation : 83.0097087379
+
+Dataset info :
+ -Database name : ModifiedMultiOmic
+ -Labels : No, Yes
+ -Views : Methyl, MiRNA, RNASEQ, Clinical
+ -2 folds
+ - Validation set length : 103 for learning rate : 0.7
+
+Classification configuration :
+ -Algorithm used : Mumbo
+ -Iterations : 1000
+ -Weak Classifiers : DecisionTree with depth 1.0, sub-sampled at 0.006 on Methyl
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on MiRNA
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on RNASEQ
+ -DecisionTree with depth 1.0, sub-sampled at 0.008 on Clinical
+
+ Mean average accuracies and stats for each fold :
+ - Fold 0
+ - On Methyl_ :
+ - Mean average Accuracy : 0.00578125
+ - Percentage of time chosen : 0.99
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0068375
+ - Percentage of time chosen : 0.007
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00573125
+ - Percentage of time chosen : 0.002
+ - On Clinic_ :
+ - Mean average Accuracy : 0.00565
+ - Percentage of time chosen : 0.001
+ - Fold 1
+ - On Methyl_ :
+ - Mean average Accuracy : 0.00618518518519
+ - Percentage of time chosen : 0.992
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.00596296296296
+ - Percentage of time chosen : 0.004
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.00606172839506
+ - Percentage of time chosen : 0.001
+ - On Clinic_ :
+ - Mean average Accuracy : 0.00574074074074
+ - Percentage of time chosen : 0.003
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 68.75
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 57.4074074074
+ Accuracy on test : 68.0327868852
+ Accuracy on validation : 70.8737864078
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 63.0787037037
+ Accuracy on test : 72.5409836066
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 70.0
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 57.4074074074
+ Accuracy on test : 68.0327868852
+ Accuracy on validation : 70.8737864078
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 63.7037037037
+ Accuracy on test : 72.9508196721
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 69.375
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 57.4074074074
+ Accuracy on test : 68.0327868852
+ Accuracy on validation : 70.8737864078
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 63.3912037037
+ Accuracy on test : 74.5901639344
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 69.375
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 77.6699029126
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 60.4938271605
+ Accuracy on test : 70.4918032787
+ Accuracy on validation : 71.8446601942
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 64.9344135802
+ Accuracy on test : 75.8196721311
+ - Iteration 6
+ Fold 1
+ Accuracy on train : 68.75
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 76.6990291262
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 67.2839506173
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 68.0169753086
+ Accuracy on test : 75.4098360656
+ - Iteration 7
+ Fold 1
+ Accuracy on train : 70.0
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 78.640776699
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 66.049382716
+ Accuracy on test : 72.131147541
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 68.024691358
+ Accuracy on test : 74.5901639344
+ - Iteration 8
+ Fold 1
+ Accuracy on train : 75.625
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 67.2839506173
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 71.8446601942
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 71.4544753086
+ Accuracy on test : 77.4590163934
+ - Iteration 9
+ Fold 1
+ Accuracy on train : 75.625
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 84.4660194175
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 66.049382716
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 73.786407767
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 70.837191358
+ Accuracy on test : 77.4590163934
+ - Iteration 10
+ Fold 1
+ Accuracy on train : 73.75
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 81.5533980583
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 70.987654321
+ Accuracy on test : 71.3114754098
+ Accuracy on validation : 74.7572815534
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 72.3688271605
+ Accuracy on test : 76.6393442623
+ - Iteration 11
+ Fold 1
+ Accuracy on train : 79.375
+ Accuracy on test : 85.2459016393
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 74.0740740741
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 77.6699029126
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 76.724537037
+ Accuracy on test : 80.3278688525
+ - Iteration 12
+ Fold 1
+ Accuracy on train : 79.375
+ Accuracy on test : 85.2459016393
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 72.8395061728
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 76.1072530864
+ Accuracy on test : 79.0983606557
+ - Iteration 13
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 14
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 15
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 16
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 17
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 18
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 19
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 20
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 21
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 22
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 23
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 24
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
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+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 989
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 990
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 991
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 992
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 993
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 994
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 995
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 996
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 997
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 998
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 999
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+ - Iteration 1000
+ Fold 1
+ Accuracy on train : 62.5
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 62.962962963
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.7314814815
+ Accuracy on test : 72.9508196721
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:01:13 0:00:01
+ Fold 2 0:01:23 0:00:01
+ Total 0:02:37 0:00:03
+ So a total classification time of 0:01:25.
+
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092301-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092301-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..8ef0a665
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092301-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,557 @@
+2016-08-24 09:23:01,452 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:23:01,453 INFO: Info: Labels used: No, Yes
+2016-08-24 09:23:01,453 INFO: Info: Length of dataset:347
+2016-08-24 09:23:01,454 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:23:01,455 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:23:01,455 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:23:01,455 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:23:01,456 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:23:01,456 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:23:01,456 INFO: Done: Read Database Files
+2016-08-24 09:23:01,456 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:23:01,460 INFO: Done: Determine validation split
+2016-08-24 09:23:01,460 INFO: Start: Determine 2 folds
+2016-08-24 09:23:01,469 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:23:01,469 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:23:01,469 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:23:01,469 INFO: Done: Determine folds
+2016-08-24 09:23:01,469 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:23:01,469 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:23:01,469 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:23:08,825 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:23:08,826 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:23:10,814 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:23:10,814 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 09:23:27,482 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:23:27,482 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 09:23:29,240 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:23:29,241 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 09:24:07,505 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:24:07,505 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 09:24:07,505 INFO: Start: Fold number 1
+2016-08-24 09:24:09,143 DEBUG: Start: Iteration 1
+2016-08-24 09:24:09,163 DEBUG: View 0 : 0.596153846154
+2016-08-24 09:24:09,171 DEBUG: View 1 : 0.384615384615
+2016-08-24 09:24:09,198 DEBUG: View 2 : 0.615384615385
+2016-08-24 09:24:09,206 DEBUG: View 3 : 0.615384615385
+2016-08-24 09:24:09,247 DEBUG: Best view : MiRNA__
+2016-08-24 09:24:09,317 DEBUG: Start: Iteration 2
+2016-08-24 09:24:09,335 DEBUG: View 0 : 0.5
+2016-08-24 09:24:09,342 DEBUG: View 1 : 0.288461538462
+2016-08-24 09:24:09,379 DEBUG: View 2 : 0.5
+2016-08-24 09:24:09,386 DEBUG: View 3 : 0.410256410256
+2016-08-24 09:24:09,432 DEBUG: Best view : Methyl_
+2016-08-24 09:24:09,567 DEBUG: Start: Iteration 3
+2016-08-24 09:24:09,584 DEBUG: View 0 : 0.461538461538
+2016-08-24 09:24:09,592 DEBUG: View 1 : 0.621794871795
+2016-08-24 09:24:09,628 DEBUG: View 2 : 0.532051282051
+2016-08-24 09:24:09,636 DEBUG: View 3 : 0.621794871795
+2016-08-24 09:24:09,689 DEBUG: Best view : MiRNA__
+2016-08-24 09:24:09,884 DEBUG: Start: Iteration 4
+2016-08-24 09:24:09,900 DEBUG: View 0 : 0.544871794872
+2016-08-24 09:24:09,908 DEBUG: View 1 : 0.474358974359
+2016-08-24 09:24:09,944 DEBUG: View 2 : 0.442307692308
+2016-08-24 09:24:09,952 DEBUG: View 3 : 0.423076923077
+2016-08-24 09:24:10,007 DEBUG: Best view : Methyl_
+2016-08-24 09:24:10,258 DEBUG: Start: Iteration 5
+2016-08-24 09:24:10,275 DEBUG: View 0 : 0.49358974359
+2016-08-24 09:24:10,282 DEBUG: View 1 : 0.557692307692
+2016-08-24 09:24:10,319 DEBUG: View 2 : 0.596153846154
+2016-08-24 09:24:10,327 DEBUG: View 3 : 0.429487179487
+2016-08-24 09:24:10,384 DEBUG: Best view : RANSeq_
+2016-08-24 09:24:10,714 DEBUG: Start: Iteration 6
+2016-08-24 09:24:10,731 DEBUG: View 0 : 0.544871794872
+2016-08-24 09:24:10,739 DEBUG: View 1 : 0.429487179487
+2016-08-24 09:24:10,775 DEBUG: View 2 : 0.608974358974
+2016-08-24 09:24:10,783 DEBUG: View 3 : 0.608974358974
+2016-08-24 09:24:10,843 DEBUG: Best view : RANSeq_
+2016-08-24 09:24:11,237 DEBUG: Start: Iteration 7
+2016-08-24 09:24:11,253 DEBUG: View 0 : 0.467948717949
+2016-08-24 09:24:11,261 DEBUG: View 1 : 0.570512820513
+2016-08-24 09:24:11,297 DEBUG: View 2 : 0.403846153846
+2016-08-24 09:24:11,305 DEBUG: View 3 : 0.487179487179
+2016-08-24 09:24:11,367 DEBUG: Best view : MiRNA__
+2016-08-24 09:24:11,817 DEBUG: Start: Iteration 8
+2016-08-24 09:24:11,834 DEBUG: View 0 : 0.410256410256
+2016-08-24 09:24:11,841 DEBUG: View 1 : 0.435897435897
+2016-08-24 09:24:11,877 DEBUG: View 2 : 0.615384615385
+2016-08-24 09:24:11,885 DEBUG: View 3 : 0.570512820513
+2016-08-24 09:24:11,949 DEBUG: Best view : Clinic_
+2016-08-24 09:24:12,494 DEBUG: Start: Iteration 9
+2016-08-24 09:24:12,510 DEBUG: View 0 : 0.583333333333
+2016-08-24 09:24:12,518 DEBUG: View 1 : 0.602564102564
+2016-08-24 09:24:12,556 DEBUG: View 2 : 0.391025641026
+2016-08-24 09:24:12,563 DEBUG: View 3 : 0.461538461538
+2016-08-24 09:24:12,633 DEBUG: Best view : MiRNA__
+2016-08-24 09:24:13,242 DEBUG: Start: Iteration 10
+2016-08-24 09:24:13,269 DEBUG: View 0 : 0.551282051282
+2016-08-24 09:24:13,278 DEBUG: View 1 : 0.647435897436
+2016-08-24 09:24:13,322 DEBUG: View 2 : 0.551282051282
+2016-08-24 09:24:13,331 DEBUG: View 3 : 0.602564102564
+2016-08-24 09:24:13,410 DEBUG: Best view : MiRNA__
+2016-08-24 09:24:14,046 DEBUG: Start: Iteration 11
+2016-08-24 09:24:14,063 DEBUG: View 0 : 0.435897435897
+2016-08-24 09:24:14,070 DEBUG: View 1 : 0.455128205128
+2016-08-24 09:24:14,106 DEBUG: View 2 : 0.416666666667
+2016-08-24 09:24:14,114 DEBUG: View 3 : 0.532051282051
+2016-08-24 09:24:14,187 DEBUG: Best view : Clinic_
+2016-08-24 09:24:14,942 DEBUG: Start: Iteration 12
+2016-08-24 09:24:14,963 DEBUG: View 0 : 0.724358974359
+2016-08-24 09:24:14,972 DEBUG: View 1 : 0.775641025641
+2016-08-24 09:24:15,010 DEBUG: View 2 : 0.467948717949
+2016-08-24 09:24:15,019 DEBUG: View 3 : 0.487179487179
+2016-08-24 09:24:15,094 DEBUG: Best view : MiRNA__
+2016-08-24 09:24:15,834 DEBUG: Start: Iteration 13
+2016-08-24 09:24:15,853 DEBUG: View 0 : 0.782051282051
+2016-08-24 09:24:15,861 DEBUG: View 1 : 0.480769230769
+2016-08-24 09:24:15,899 DEBUG: View 2 : 0.538461538462
+2016-08-24 09:24:15,907 DEBUG: View 3 : 0.423076923077
+2016-08-24 09:24:15,982 DEBUG: Best view : Methyl_
+2016-08-24 09:24:16,787 DEBUG: Start: Iteration 14
+2016-08-24 09:24:16,804 DEBUG: View 0 : 0.788461538462
+2016-08-24 09:24:16,812 DEBUG: View 1 : 0.641025641026
+2016-08-24 09:24:16,848 DEBUG: View 2 : 0.564102564103
+2016-08-24 09:24:16,856 DEBUG: View 3 : 0.615384615385
+2016-08-24 09:24:16,934 DEBUG: Best view : Methyl_
+2016-08-24 09:24:17,794 DEBUG: Start: Iteration 15
+2016-08-24 09:24:17,810 DEBUG: View 0 : 0.589743589744
+2016-08-24 09:24:17,818 DEBUG: View 1 : 0.519230769231
+2016-08-24 09:24:17,854 DEBUG: View 2 : 0.403846153846
+2016-08-24 09:24:17,861 DEBUG: View 3 : 0.570512820513
+2016-08-24 09:24:17,941 DEBUG: Best view : Methyl_
+2016-08-24 09:24:18,865 DEBUG: Start: Iteration 16
+2016-08-24 09:24:18,882 DEBUG: View 0 : 0.673076923077
+2016-08-24 09:24:18,890 DEBUG: View 1 : 0.634615384615
+2016-08-24 09:24:18,930 DEBUG: View 2 : 0.461538461538
+2016-08-24 09:24:18,938 DEBUG: View 3 : 0.416666666667
+2016-08-24 09:24:19,022 DEBUG: Best view : Methyl_
+2016-08-24 09:24:20,318 DEBUG: Start: Iteration 17
+2016-08-24 09:24:20,347 DEBUG: View 0 : 0.685897435897
+2016-08-24 09:24:20,361 DEBUG: View 1 : 0.403846153846
+2016-08-24 09:24:20,409 DEBUG: View 2 : 0.50641025641
+2016-08-24 09:24:20,418 DEBUG: View 3 : 0.666666666667
+2016-08-24 09:24:20,513 DEBUG: Best view : Clinic_
+2016-08-24 09:24:21,727 DEBUG: Start: Iteration 18
+2016-08-24 09:24:21,746 DEBUG: View 0 : 0.544871794872
+2016-08-24 09:24:21,755 DEBUG: View 1 : 0.467948717949
+2016-08-24 09:24:21,799 DEBUG: View 2 : 0.576923076923
+2016-08-24 09:24:21,809 DEBUG: View 3 : 0.455128205128
+2016-08-24 09:24:21,907 DEBUG: Best view : Methyl_
+2016-08-24 09:24:23,082 DEBUG: Start: Iteration 19
+2016-08-24 09:24:23,100 DEBUG: View 0 : 0.519230769231
+2016-08-24 09:24:23,109 DEBUG: View 1 : 0.339743589744
+2016-08-24 09:24:23,158 DEBUG: View 2 : 0.525641025641
+2016-08-24 09:24:23,167 DEBUG: View 3 : 0.525641025641
+2016-08-24 09:24:23,274 DEBUG: Best view : Methyl_
+2016-08-24 09:24:24,497 DEBUG: Start: Iteration 20
+2016-08-24 09:24:24,515 DEBUG: View 0 : 0.615384615385
+2016-08-24 09:24:24,523 DEBUG: View 1 : 0.50641025641
+2016-08-24 09:24:24,571 DEBUG: View 2 : 0.384615384615
+2016-08-24 09:24:24,579 DEBUG: View 3 : 0.512820512821
+2016-08-24 09:24:24,674 DEBUG: Best view : Methyl_
+2016-08-24 09:24:25,966 DEBUG: Start: Iteration 21
+2016-08-24 09:24:25,984 DEBUG: View 0 : 0.391025641026
+2016-08-24 09:24:25,992 DEBUG: View 1 : 0.615384615385
+2016-08-24 09:24:26,030 DEBUG: View 2 : 0.480769230769
+2016-08-24 09:24:26,038 DEBUG: View 3 : 0.538461538462
+2016-08-24 09:24:26,133 DEBUG: Best view : MiRNA__
+2016-08-24 09:24:27,438 DEBUG: Start: Iteration 22
+2016-08-24 09:24:27,455 DEBUG: View 0 : 0.358974358974
+2016-08-24 09:24:27,463 DEBUG: View 1 : 0.730769230769
+2016-08-24 09:24:27,499 DEBUG: View 2 : 0.557692307692
+2016-08-24 09:24:27,507 DEBUG: View 3 : 0.378205128205
+2016-08-24 09:24:27,603 DEBUG: Best view : MiRNA__
+2016-08-24 09:24:29,071 DEBUG: Start: Iteration 23
+2016-08-24 09:24:29,087 DEBUG: View 0 : 0.512820512821
+2016-08-24 09:24:29,095 DEBUG: View 1 : 0.371794871795
+2016-08-24 09:24:29,135 DEBUG: View 2 : 0.487179487179
+2016-08-24 09:24:29,143 DEBUG: View 3 : 0.455128205128
+2016-08-24 09:24:29,243 DEBUG: Best view : Methyl_
+2016-08-24 09:24:30,754 DEBUG: Start: Iteration 24
+2016-08-24 09:24:30,771 DEBUG: View 0 : 0.615384615385
+2016-08-24 09:24:30,779 DEBUG: View 1 : 0.698717948718
+2016-08-24 09:24:30,832 DEBUG: View 2 : 0.49358974359
+2016-08-24 09:24:30,848 DEBUG: View 3 : 0.583333333333
+2016-08-24 09:24:30,972 DEBUG: Best view : MiRNA__
+2016-08-24 09:24:32,536 DEBUG: Start: Iteration 25
+2016-08-24 09:24:32,553 DEBUG: View 0 : 0.512820512821
+2016-08-24 09:24:32,561 DEBUG: View 1 : 0.461538461538
+2016-08-24 09:24:32,598 DEBUG: View 2 : 0.532051282051
+2016-08-24 09:24:32,609 DEBUG: View 3 : 0.512820512821
+2016-08-24 09:24:32,735 DEBUG: Best view : Methyl_
+2016-08-24 09:24:34,372 DEBUG: Start: Iteration 26
+2016-08-24 09:24:34,395 DEBUG: View 0 : 0.705128205128
+2016-08-24 09:24:34,404 DEBUG: View 1 : 0.442307692308
+2016-08-24 09:24:34,443 DEBUG: View 2 : 0.5
+2016-08-24 09:24:34,451 DEBUG: View 3 : 0.666666666667
+2016-08-24 09:24:34,558 DEBUG: Best view : Methyl_
+2016-08-24 09:24:36,187 DEBUG: Start: Iteration 27
+2016-08-24 09:24:36,206 DEBUG: View 0 : 0.525641025641
+2016-08-24 09:24:36,214 DEBUG: View 1 : 0.429487179487
+2016-08-24 09:24:36,252 DEBUG: View 2 : 0.467948717949
+2016-08-24 09:24:36,260 DEBUG: View 3 : 0.467948717949
+2016-08-24 09:24:36,370 DEBUG: Best view : Methyl_
+2016-08-24 09:24:38,001 DEBUG: Start: Iteration 28
+2016-08-24 09:24:38,017 DEBUG: View 0 : 0.608974358974
+2016-08-24 09:24:38,025 DEBUG: View 1 : 0.448717948718
+2016-08-24 09:24:38,061 DEBUG: View 2 : 0.589743589744
+2016-08-24 09:24:38,069 DEBUG: View 3 : 0.416666666667
+2016-08-24 09:24:38,177 DEBUG: Best view : Methyl_
+2016-08-24 09:24:39,928 DEBUG: Start: Iteration 29
+2016-08-24 09:24:39,944 DEBUG: View 0 : 0.403846153846
+2016-08-24 09:24:39,952 DEBUG: View 1 : 0.410256410256
+2016-08-24 09:24:39,989 DEBUG: View 2 : 0.538461538462
+2016-08-24 09:24:39,997 DEBUG: View 3 : 0.544871794872
+2016-08-24 09:24:40,108 DEBUG: Best view : Clinic_
+2016-08-24 09:24:41,868 DEBUG: Start: Iteration 30
+2016-08-24 09:24:41,885 DEBUG: View 0 : 0.692307692308
+2016-08-24 09:24:41,893 DEBUG: View 1 : 0.49358974359
+2016-08-24 09:24:41,929 DEBUG: View 2 : 0.423076923077
+2016-08-24 09:24:41,937 DEBUG: View 3 : 0.487179487179
+2016-08-24 09:24:42,050 DEBUG: Best view : Methyl_
+2016-08-24 09:24:44,013 DEBUG: Start: Iteration 31
+2016-08-24 09:24:44,030 DEBUG: View 0 : 0.442307692308
+2016-08-24 09:24:44,038 DEBUG: View 1 : 0.576923076923
+2016-08-24 09:24:44,074 DEBUG: View 2 : 0.442307692308
+2016-08-24 09:24:44,082 DEBUG: View 3 : 0.538461538462
+2016-08-24 09:24:44,197 DEBUG: Best view : MiRNA__
+2016-08-24 09:24:46,115 DEBUG: Start: Iteration 32
+2016-08-24 09:24:46,140 DEBUG: View 0 : 0.448717948718
+2016-08-24 09:24:46,155 DEBUG: View 1 : 0.326923076923
+2016-08-24 09:24:46,202 DEBUG: View 2 : 0.512820512821
+2016-08-24 09:24:46,212 DEBUG: View 3 : 0.442307692308
+2016-08-24 09:24:46,350 DEBUG: Best view : RANSeq_
+2016-08-24 09:24:48,336 DEBUG: Start: Iteration 33
+2016-08-24 09:24:48,352 DEBUG: View 0 : 0.621794871795
+2016-08-24 09:24:48,360 DEBUG: View 1 : 0.50641025641
+2016-08-24 09:24:48,397 DEBUG: View 2 : 0.589743589744
+2016-08-24 09:24:48,405 DEBUG: View 3 : 0.429487179487
+2016-08-24 09:24:48,525 DEBUG: Best view : Methyl_
+2016-08-24 09:24:50,614 DEBUG: Start: Iteration 34
+2016-08-24 09:24:50,633 DEBUG: View 0 : 0.416666666667
+2016-08-24 09:24:50,642 DEBUG: View 1 : 0.391025641026
+2016-08-24 09:24:50,684 DEBUG: View 2 : 0.5
+2016-08-24 09:24:50,693 DEBUG: View 3 : 0.423076923077
+2016-08-24 09:24:50,835 DEBUG: Best view : RANSeq_
+2016-08-24 09:24:53,102 DEBUG: Start: Iteration 35
+2016-08-24 09:24:53,119 DEBUG: View 0 : 0.564102564103
+2016-08-24 09:24:53,126 DEBUG: View 1 : 0.589743589744
+2016-08-24 09:24:53,163 DEBUG: View 2 : 0.391025641026
+2016-08-24 09:24:53,171 DEBUG: View 3 : 0.519230769231
+2016-08-24 09:24:53,313 DEBUG: Best view : Methyl_
+2016-08-24 09:24:55,605 DEBUG: Start: Iteration 36
+2016-08-24 09:24:55,622 DEBUG: View 0 : 0.448717948718
+2016-08-24 09:24:55,630 DEBUG: View 1 : 0.653846153846
+2016-08-24 09:24:55,667 DEBUG: View 2 : 0.589743589744
+2016-08-24 09:24:55,675 DEBUG: View 3 : 0.397435897436
+2016-08-24 09:24:55,810 DEBUG: Best view : MiRNA__
+2016-08-24 09:24:58,037 DEBUG: Start: Iteration 37
+2016-08-24 09:24:58,053 DEBUG: View 0 : 0.461538461538
+2016-08-24 09:24:58,061 DEBUG: View 1 : 0.660256410256
+2016-08-24 09:24:58,098 DEBUG: View 2 : 0.5
+2016-08-24 09:24:58,105 DEBUG: View 3 : 0.525641025641
+2016-08-24 09:24:58,235 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:00,477 DEBUG: Start: Iteration 38
+2016-08-24 09:25:00,494 DEBUG: View 0 : 0.525641025641
+2016-08-24 09:25:00,502 DEBUG: View 1 : 0.49358974359
+2016-08-24 09:25:00,540 DEBUG: View 2 : 0.551282051282
+2016-08-24 09:25:00,548 DEBUG: View 3 : 0.519230769231
+2016-08-24 09:25:00,684 DEBUG: Best view : Methyl_
+2016-08-24 09:25:02,984 DEBUG: Start: Iteration 39
+2016-08-24 09:25:03,001 DEBUG: View 0 : 0.532051282051
+2016-08-24 09:25:03,008 DEBUG: View 1 : 0.641025641026
+2016-08-24 09:25:03,045 DEBUG: View 2 : 0.576923076923
+2016-08-24 09:25:03,053 DEBUG: View 3 : 0.647435897436
+2016-08-24 09:25:03,187 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:05,555 DEBUG: Start: Iteration 40
+2016-08-24 09:25:05,571 DEBUG: View 0 : 0.442307692308
+2016-08-24 09:25:05,579 DEBUG: View 1 : 0.634615384615
+2016-08-24 09:25:05,616 DEBUG: View 2 : 0.512820512821
+2016-08-24 09:25:05,624 DEBUG: View 3 : 0.532051282051
+2016-08-24 09:25:05,761 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:08,173 DEBUG: Start: Iteration 41
+2016-08-24 09:25:08,189 DEBUG: View 0 : 0.576923076923
+2016-08-24 09:25:08,197 DEBUG: View 1 : 0.692307692308
+2016-08-24 09:25:08,233 DEBUG: View 2 : 0.615384615385
+2016-08-24 09:25:08,241 DEBUG: View 3 : 0.589743589744
+2016-08-24 09:25:08,380 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:10,856 DEBUG: Start: Iteration 42
+2016-08-24 09:25:10,873 DEBUG: View 0 : 0.410256410256
+2016-08-24 09:25:10,881 DEBUG: View 1 : 0.384615384615
+2016-08-24 09:25:10,918 DEBUG: View 2 : 0.621794871795
+2016-08-24 09:25:10,926 DEBUG: View 3 : 0.564102564103
+2016-08-24 09:25:11,067 DEBUG: Best view : RANSeq_
+2016-08-24 09:25:13,607 DEBUG: Start: Iteration 43
+2016-08-24 09:25:13,623 DEBUG: View 0 : 0.608974358974
+2016-08-24 09:25:13,631 DEBUG: View 1 : 0.711538461538
+2016-08-24 09:25:13,668 DEBUG: View 2 : 0.557692307692
+2016-08-24 09:25:13,675 DEBUG: View 3 : 0.589743589744
+2016-08-24 09:25:13,817 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:16,417 DEBUG: Start: Iteration 44
+2016-08-24 09:25:16,434 DEBUG: View 0 : 0.423076923077
+2016-08-24 09:25:16,442 DEBUG: View 1 : 0.641025641026
+2016-08-24 09:25:16,478 DEBUG: View 2 : 0.538461538462
+2016-08-24 09:25:16,486 DEBUG: View 3 : 0.608974358974
+2016-08-24 09:25:16,632 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:19,287 DEBUG: Start: Iteration 45
+2016-08-24 09:25:19,303 DEBUG: View 0 : 0.49358974359
+2016-08-24 09:25:19,311 DEBUG: View 1 : 0.628205128205
+2016-08-24 09:25:19,348 DEBUG: View 2 : 0.519230769231
+2016-08-24 09:25:19,356 DEBUG: View 3 : 0.544871794872
+2016-08-24 09:25:19,504 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:22,218 DEBUG: Start: Iteration 46
+2016-08-24 09:25:22,235 DEBUG: View 0 : 0.525641025641
+2016-08-24 09:25:22,243 DEBUG: View 1 : 0.423076923077
+2016-08-24 09:25:22,280 DEBUG: View 2 : 0.544871794872
+2016-08-24 09:25:22,287 DEBUG: View 3 : 0.416666666667
+2016-08-24 09:25:22,437 DEBUG: Best view : Methyl_
+2016-08-24 09:25:25,212 DEBUG: Start: Iteration 47
+2016-08-24 09:25:25,228 DEBUG: View 0 : 0.378205128205
+2016-08-24 09:25:25,236 DEBUG: View 1 : 0.480769230769
+2016-08-24 09:25:25,273 DEBUG: View 2 : 0.583333333333
+2016-08-24 09:25:25,281 DEBUG: View 3 : 0.570512820513
+2016-08-24 09:25:25,434 DEBUG: Best view : Clinic_
+2016-08-24 09:25:28,262 DEBUG: Start: Iteration 48
+2016-08-24 09:25:28,279 DEBUG: View 0 : 0.525641025641
+2016-08-24 09:25:28,287 DEBUG: View 1 : 0.333333333333
+2016-08-24 09:25:28,323 DEBUG: View 2 : 0.544871794872
+2016-08-24 09:25:28,331 DEBUG: View 3 : 0.435897435897
+2016-08-24 09:25:28,487 DEBUG: Best view : RANSeq_
+2016-08-24 09:25:31,392 DEBUG: Start: Iteration 49
+2016-08-24 09:25:31,409 DEBUG: View 0 : 0.570512820513
+2016-08-24 09:25:31,416 DEBUG: View 1 : 0.50641025641
+2016-08-24 09:25:31,453 DEBUG: View 2 : 0.474358974359
+2016-08-24 09:25:31,461 DEBUG: View 3 : 0.461538461538
+2016-08-24 09:25:31,616 DEBUG: Best view : Methyl_
+2016-08-24 09:25:34,579 DEBUG: Start: Iteration 50
+2016-08-24 09:25:34,596 DEBUG: View 0 : 0.628205128205
+2016-08-24 09:25:34,604 DEBUG: View 1 : 0.474358974359
+2016-08-24 09:25:34,640 DEBUG: View 2 : 0.576923076923
+2016-08-24 09:25:34,648 DEBUG: View 3 : 0.519230769231
+2016-08-24 09:25:34,807 DEBUG: Best view : Methyl_
+2016-08-24 09:25:37,851 DEBUG: Start: Iteration 51
+2016-08-24 09:25:37,868 DEBUG: View 0 : 0.525641025641
+2016-08-24 09:25:37,876 DEBUG: View 1 : 0.49358974359
+2016-08-24 09:25:37,912 DEBUG: View 2 : 0.50641025641
+2016-08-24 09:25:37,920 DEBUG: View 3 : 0.397435897436
+2016-08-24 09:25:38,082 DEBUG: Best view : Methyl_
+2016-08-24 09:25:41,390 DEBUG: Start: Iteration 52
+2016-08-24 09:25:41,409 DEBUG: View 0 : 0.397435897436
+2016-08-24 09:25:41,417 DEBUG: View 1 : 0.634615384615
+2016-08-24 09:25:41,454 DEBUG: View 2 : 0.532051282051
+2016-08-24 09:25:41,462 DEBUG: View 3 : 0.416666666667
+2016-08-24 09:25:41,625 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:44,792 INFO: Start: Classification
+2016-08-24 09:25:52,398 INFO: Done: Fold number 1
+2016-08-24 09:25:52,398 INFO: Start: Fold number 2
+2016-08-24 09:25:54,031 DEBUG: Start: Iteration 1
+2016-08-24 09:25:54,047 DEBUG: View 0 : 0.62962962963
+2016-08-24 09:25:54,055 DEBUG: View 1 : 0.666666666667
+2016-08-24 09:25:54,093 DEBUG: View 2 : 0.623456790123
+2016-08-24 09:25:54,101 DEBUG: View 3 : 0.37037037037
+2016-08-24 09:25:54,143 DEBUG: Best view : Clinic_
+2016-08-24 09:25:54,215 DEBUG: Start: Iteration 2
+2016-08-24 09:25:54,233 DEBUG: View 0 : 0.530864197531
+2016-08-24 09:25:54,241 DEBUG: View 1 : 0.277777777778
+2016-08-24 09:25:54,279 DEBUG: View 2 : 0.41975308642
+2016-08-24 09:25:54,287 DEBUG: View 3 : 0.561728395062
+2016-08-24 09:25:54,340 DEBUG: Best view : Clinic_
+2016-08-24 09:25:54,473 DEBUG: Start: Iteration 3
+2016-08-24 09:25:54,491 DEBUG: View 0 : 0.506172839506
+2016-08-24 09:25:54,502 DEBUG: View 1 : 0.592592592593
+2016-08-24 09:25:54,543 DEBUG: View 2 : 0.549382716049
+2016-08-24 09:25:54,551 DEBUG: View 3 : 0.450617283951
+2016-08-24 09:25:54,607 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:54,799 DEBUG: Start: Iteration 4
+2016-08-24 09:25:54,817 DEBUG: View 0 : 0.469135802469
+2016-08-24 09:25:54,825 DEBUG: View 1 : 0.549382716049
+2016-08-24 09:25:54,865 DEBUG: View 2 : 0.487654320988
+2016-08-24 09:25:54,873 DEBUG: View 3 : 0.382716049383
+2016-08-24 09:25:54,931 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:55,184 DEBUG: Start: Iteration 5
+2016-08-24 09:25:55,202 DEBUG: View 0 : 0.506172839506
+2016-08-24 09:25:55,211 DEBUG: View 1 : 0.716049382716
+2016-08-24 09:25:55,249 DEBUG: View 2 : 0.469135802469
+2016-08-24 09:25:55,257 DEBUG: View 3 : 0.623456790123
+2016-08-24 09:25:55,325 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:55,645 DEBUG: Start: Iteration 6
+2016-08-24 09:25:55,663 DEBUG: View 0 : 0.388888888889
+2016-08-24 09:25:55,671 DEBUG: View 1 : 0.771604938272
+2016-08-24 09:25:55,709 DEBUG: View 2 : 0.598765432099
+2016-08-24 09:25:55,717 DEBUG: View 3 : 0.524691358025
+2016-08-24 09:25:55,779 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:56,151 DEBUG: Start: Iteration 7
+2016-08-24 09:25:56,168 DEBUG: View 0 : 0.524691358025
+2016-08-24 09:25:56,176 DEBUG: View 1 : 0.641975308642
+2016-08-24 09:25:56,214 DEBUG: View 2 : 0.506172839506
+2016-08-24 09:25:56,222 DEBUG: View 3 : 0.537037037037
+2016-08-24 09:25:56,288 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:56,721 DEBUG: Start: Iteration 8
+2016-08-24 09:25:56,738 DEBUG: View 0 : 0.413580246914
+2016-08-24 09:25:56,747 DEBUG: View 1 : 0.524691358025
+2016-08-24 09:25:56,785 DEBUG: View 2 : 0.586419753086
+2016-08-24 09:25:56,793 DEBUG: View 3 : 0.407407407407
+2016-08-24 09:25:56,862 DEBUG: Best view : RANSeq_
+2016-08-24 09:25:57,369 DEBUG: Start: Iteration 9
+2016-08-24 09:25:57,386 DEBUG: View 0 : 0.444444444444
+2016-08-24 09:25:57,394 DEBUG: View 1 : 0.604938271605
+2016-08-24 09:25:57,431 DEBUG: View 2 : 0.444444444444
+2016-08-24 09:25:57,439 DEBUG: View 3 : 0.481481481481
+2016-08-24 09:25:57,507 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:58,072 DEBUG: Start: Iteration 10
+2016-08-24 09:25:58,089 DEBUG: View 0 : 0.382716049383
+2016-08-24 09:25:58,097 DEBUG: View 1 : 0.623456790123
+2016-08-24 09:25:58,134 DEBUG: View 2 : 0.617283950617
+2016-08-24 09:25:58,142 DEBUG: View 3 : 0.41975308642
+2016-08-24 09:25:58,213 DEBUG: Best view : MiRNA__
+2016-08-24 09:25:58,839 DEBUG: Start: Iteration 11
+2016-08-24 09:25:58,856 DEBUG: View 0 : 0.506172839506
+2016-08-24 09:25:58,864 DEBUG: View 1 : 0.487654320988
+2016-08-24 09:25:58,901 DEBUG: View 2 : 0.530864197531
+2016-08-24 09:25:58,909 DEBUG: View 3 : 0.407407407407
+2016-08-24 09:25:58,983 DEBUG: Best view : RANSeq_
+2016-08-24 09:25:59,688 DEBUG: Start: Iteration 12
+2016-08-24 09:25:59,705 DEBUG: View 0 : 0.518518518519
+2016-08-24 09:25:59,714 DEBUG: View 1 : 0.543209876543
+2016-08-24 09:25:59,751 DEBUG: View 2 : 0.530864197531
+2016-08-24 09:25:59,759 DEBUG: View 3 : 0.469135802469
+2016-08-24 09:25:59,835 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:00,606 DEBUG: Start: Iteration 13
+2016-08-24 09:26:00,623 DEBUG: View 0 : 0.487654320988
+2016-08-24 09:26:00,631 DEBUG: View 1 : 0.549382716049
+2016-08-24 09:26:00,669 DEBUG: View 2 : 0.413580246914
+2016-08-24 09:26:00,676 DEBUG: View 3 : 0.530864197531
+2016-08-24 09:26:00,756 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:01,578 DEBUG: Start: Iteration 14
+2016-08-24 09:26:01,595 DEBUG: View 0 : 0.561728395062
+2016-08-24 09:26:01,603 DEBUG: View 1 : 0.555555555556
+2016-08-24 09:26:01,640 DEBUG: View 2 : 0.537037037037
+2016-08-24 09:26:01,648 DEBUG: View 3 : 0.376543209877
+2016-08-24 09:26:01,729 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:02,647 DEBUG: Start: Iteration 15
+2016-08-24 09:26:02,664 DEBUG: View 0 : 0.444444444444
+2016-08-24 09:26:02,672 DEBUG: View 1 : 0.364197530864
+2016-08-24 09:26:02,709 DEBUG: View 2 : 0.450617283951
+2016-08-24 09:26:02,717 DEBUG: View 3 : 0.561728395062
+2016-08-24 09:26:02,800 DEBUG: Best view : Clinic_
+2016-08-24 09:26:03,734 DEBUG: Start: Iteration 16
+2016-08-24 09:26:03,751 DEBUG: View 0 : 0.604938271605
+2016-08-24 09:26:03,759 DEBUG: View 1 : 0.666666666667
+2016-08-24 09:26:03,796 DEBUG: View 2 : 0.530864197531
+2016-08-24 09:26:03,804 DEBUG: View 3 : 0.611111111111
+2016-08-24 09:26:03,888 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:04,879 DEBUG: Start: Iteration 17
+2016-08-24 09:26:04,896 DEBUG: View 0 : 0.561728395062
+2016-08-24 09:26:04,904 DEBUG: View 1 : 0.641975308642
+2016-08-24 09:26:04,941 DEBUG: View 2 : 0.450617283951
+2016-08-24 09:26:04,948 DEBUG: View 3 : 0.62962962963
+2016-08-24 09:26:05,035 DEBUG: Best view : Clinic_
+2016-08-24 09:26:06,079 DEBUG: Start: Iteration 18
+2016-08-24 09:26:06,096 DEBUG: View 0 : 0.438271604938
+2016-08-24 09:26:06,104 DEBUG: View 1 : 0.549382716049
+2016-08-24 09:26:06,141 DEBUG: View 2 : 0.493827160494
+2016-08-24 09:26:06,149 DEBUG: View 3 : 0.524691358025
+2016-08-24 09:26:06,238 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:07,352 DEBUG: Start: Iteration 19
+2016-08-24 09:26:07,369 DEBUG: View 0 : 0.512345679012
+2016-08-24 09:26:07,377 DEBUG: View 1 : 0.611111111111
+2016-08-24 09:26:07,415 DEBUG: View 2 : 0.617283950617
+2016-08-24 09:26:07,423 DEBUG: View 3 : 0.574074074074
+2016-08-24 09:26:07,517 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:08,688 DEBUG: Start: Iteration 20
+2016-08-24 09:26:08,705 DEBUG: View 0 : 0.407407407407
+2016-08-24 09:26:08,713 DEBUG: View 1 : 0.62962962963
+2016-08-24 09:26:08,751 DEBUG: View 2 : 0.549382716049
+2016-08-24 09:26:08,759 DEBUG: View 3 : 0.493827160494
+2016-08-24 09:26:08,855 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:10,080 DEBUG: Start: Iteration 21
+2016-08-24 09:26:10,097 DEBUG: View 0 : 0.561728395062
+2016-08-24 09:26:10,105 DEBUG: View 1 : 0.672839506173
+2016-08-24 09:26:10,142 DEBUG: View 2 : 0.648148148148
+2016-08-24 09:26:10,150 DEBUG: View 3 : 0.438271604938
+2016-08-24 09:26:10,246 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:11,549 DEBUG: Start: Iteration 22
+2016-08-24 09:26:11,566 DEBUG: View 0 : 0.549382716049
+2016-08-24 09:26:11,574 DEBUG: View 1 : 0.586419753086
+2016-08-24 09:26:11,612 DEBUG: View 2 : 0.450617283951
+2016-08-24 09:26:11,620 DEBUG: View 3 : 0.561728395062
+2016-08-24 09:26:11,718 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:13,055 DEBUG: Start: Iteration 23
+2016-08-24 09:26:13,072 DEBUG: View 0 : 0.592592592593
+2016-08-24 09:26:13,080 DEBUG: View 1 : 0.666666666667
+2016-08-24 09:26:13,117 DEBUG: View 2 : 0.62962962963
+2016-08-24 09:26:13,125 DEBUG: View 3 : 0.586419753086
+2016-08-24 09:26:13,226 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:14,621 DEBUG: Start: Iteration 24
+2016-08-24 09:26:14,638 DEBUG: View 0 : 0.425925925926
+2016-08-24 09:26:14,646 DEBUG: View 1 : 0.376543209877
+2016-08-24 09:26:14,684 DEBUG: View 2 : 0.475308641975
+2016-08-24 09:26:14,691 DEBUG: View 3 : 0.413580246914
+2016-08-24 09:26:14,692 WARNING: All bad for iteration 23
+2016-08-24 09:26:14,795 DEBUG: Best view : RANSeq_
+2016-08-24 09:26:16,277 DEBUG: Start: Iteration 25
+2016-08-24 09:26:16,294 DEBUG: View 0 : 0.462962962963
+2016-08-24 09:26:16,302 DEBUG: View 1 : 0.716049382716
+2016-08-24 09:26:16,339 DEBUG: View 2 : 0.530864197531
+2016-08-24 09:26:16,347 DEBUG: View 3 : 0.401234567901
+2016-08-24 09:26:16,452 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:18,023 DEBUG: Start: Iteration 26
+2016-08-24 09:26:18,040 DEBUG: View 0 : 0.62962962963
+2016-08-24 09:26:18,048 DEBUG: View 1 : 0.567901234568
+2016-08-24 09:26:18,089 DEBUG: View 2 : 0.506172839506
+2016-08-24 09:26:18,098 DEBUG: View 3 : 0.567901234568
+2016-08-24 09:26:18,209 DEBUG: Best view : Methyl_
+2016-08-24 09:26:19,837 DEBUG: Start: Iteration 27
+2016-08-24 09:26:19,854 DEBUG: View 0 : 0.413580246914
+2016-08-24 09:26:19,862 DEBUG: View 1 : 0.672839506173
+2016-08-24 09:26:19,900 DEBUG: View 2 : 0.364197530864
+2016-08-24 09:26:19,908 DEBUG: View 3 : 0.469135802469
+2016-08-24 09:26:20,019 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:21,774 DEBUG: Start: Iteration 28
+2016-08-24 09:26:21,791 DEBUG: View 0 : 0.555555555556
+2016-08-24 09:26:21,799 DEBUG: View 1 : 0.388888888889
+2016-08-24 09:26:21,837 DEBUG: View 2 : 0.530864197531
+2016-08-24 09:26:21,845 DEBUG: View 3 : 0.561728395062
+2016-08-24 09:26:21,962 DEBUG: Best view : Methyl_
+2016-08-24 09:26:23,703 DEBUG: Start: Iteration 29
+2016-08-24 09:26:23,720 DEBUG: View 0 : 0.493827160494
+2016-08-24 09:26:23,728 DEBUG: View 1 : 0.611111111111
+2016-08-24 09:26:23,765 DEBUG: View 2 : 0.617283950617
+2016-08-24 09:26:23,773 DEBUG: View 3 : 0.438271604938
+2016-08-24 09:26:23,890 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:25,713 DEBUG: Start: Iteration 30
+2016-08-24 09:26:25,730 DEBUG: View 0 : 0.432098765432
+2016-08-24 09:26:25,739 DEBUG: View 1 : 0.586419753086
+2016-08-24 09:26:25,776 DEBUG: View 2 : 0.604938271605
+2016-08-24 09:26:25,784 DEBUG: View 3 : 0.617283950617
+2016-08-24 09:26:25,904 DEBUG: Best view : Clinic_
+2016-08-24 09:26:27,986 DEBUG: Start: Iteration 31
+2016-08-24 09:26:28,004 DEBUG: View 0 : 0.623456790123
+2016-08-24 09:26:28,013 DEBUG: View 1 : 0.456790123457
+2016-08-24 09:26:28,051 DEBUG: View 2 : 0.506172839506
+2016-08-24 09:26:28,059 DEBUG: View 3 : 0.376543209877
+2016-08-24 09:26:28,180 DEBUG: Best view : Methyl_
+2016-08-24 09:26:30,112 DEBUG: Start: Iteration 32
+2016-08-24 09:26:30,129 DEBUG: View 0 : 0.456790123457
+2016-08-24 09:26:30,137 DEBUG: View 1 : 0.567901234568
+2016-08-24 09:26:30,175 DEBUG: View 2 : 0.382716049383
+2016-08-24 09:26:30,182 DEBUG: View 3 : 0.567901234568
+2016-08-24 09:26:30,305 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:32,285 DEBUG: Start: Iteration 33
+2016-08-24 09:26:32,302 DEBUG: View 0 : 0.512345679012
+2016-08-24 09:26:32,310 DEBUG: View 1 : 0.327160493827
+2016-08-24 09:26:32,347 DEBUG: View 2 : 0.592592592593
+2016-08-24 09:26:32,355 DEBUG: View 3 : 0.487654320988
+2016-08-24 09:26:32,483 DEBUG: Best view : RANSeq_
+2016-08-24 09:26:34,814 DEBUG: Start: Iteration 34
+2016-08-24 09:26:34,837 DEBUG: View 0 : 0.5
+2016-08-24 09:26:34,853 DEBUG: View 1 : 0.537037037037
+2016-08-24 09:26:34,913 DEBUG: View 2 : 0.487654320988
+2016-08-24 09:26:34,927 DEBUG: View 3 : 0.395061728395
+2016-08-24 09:26:35,074 DEBUG: Best view : MiRNA__
+2016-08-24 09:26:37,180 DEBUG: Start: Iteration 35
+2016-08-24 09:26:37,197 DEBUG: View 0 : 0.493827160494
+2016-08-24 09:26:37,205 DEBUG: View 1 : 0.493827160494
+2016-08-24 09:26:37,242 DEBUG: View 2 : 0.512345679012
+2016-08-24 09:26:37,250 DEBUG: View 3 : 0.641975308642
+2016-08-24 09:26:37,379 DEBUG: Best view : Clinic_
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092640-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092640-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..1c9b53f0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092640-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,50 @@
+2016-08-24 09:26:40,875 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:26:40,875 INFO: Info: Labels used: No, Yes
+2016-08-24 09:26:40,875 INFO: Info: Length of dataset:347
+2016-08-24 09:26:40,877 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:26:40,877 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:26:40,877 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:26:40,878 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:26:40,878 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:26:40,878 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:26:40,879 INFO: Done: Read Database Files
+2016-08-24 09:26:40,879 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:26:40,882 INFO: Done: Determine validation split
+2016-08-24 09:26:40,882 INFO: Start: Determine 2 folds
+2016-08-24 09:26:40,891 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:26:40,892 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:26:40,892 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:26:40,892 INFO: Done: Determine folds
+2016-08-24 09:26:40,892 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:26:40,892 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:26:40,892 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:26:48,203 DEBUG: 0.593198847262Poulet
+2016-08-24 09:26:48,203 DEBUG: 0.572910662824Poulet
+2016-08-24 09:26:48,203 DEBUG: 0.586167146974Poulet
+2016-08-24 09:26:48,203 DEBUG: 0.5134870317Poulet
+2016-08-24 09:26:48,203 DEBUG: 0.508933717579Poulet
+2016-08-24 09:26:48,203 DEBUG: 0.549682997118Poulet
+2016-08-24 09:26:48,204 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:26:48,204 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:26:50,116 DEBUG: 0.584495677233Poulet
+2016-08-24 09:26:50,117 DEBUG: 0.583342939481Poulet
+2016-08-24 09:26:50,117 DEBUG: 0.52288184438Poulet
+2016-08-24 09:26:50,117 DEBUG: 0.560691642651Poulet
+2016-08-24 09:26:50,117 DEBUG: 0.536657060519Poulet
+2016-08-24 09:26:50,117 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:26:50,117 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 09:27:07,105 DEBUG: 0.571469740634Poulet
+2016-08-24 09:27:07,105 DEBUG: 0.587665706052Poulet
+2016-08-24 09:27:07,105 DEBUG: 0.551527377522Poulet
+2016-08-24 09:27:07,105 DEBUG: 0.55469740634Poulet
+2016-08-24 09:27:07,105 DEBUG: 0.508760806916Poulet
+2016-08-24 09:27:07,105 DEBUG: 0.507262247839Poulet
+2016-08-24 09:27:07,106 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:27:07,107 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 09:27:08,919 DEBUG: 0.583227665706Poulet
+2016-08-24 09:27:08,919 DEBUG: 0.570489913545Poulet
+2016-08-24 09:27:08,919 DEBUG: 0.55976945245Poulet
+2016-08-24 09:27:08,919 DEBUG: 0.586570605187Poulet
+2016-08-24 09:27:08,919 DEBUG: 0.516195965418Poulet
+2016-08-24 09:27:08,919 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:27:08,920 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092735-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092735-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..a19abd19
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-092735-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,650 @@
+2016-08-24 09:27:35,426 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:27:35,427 INFO: Info: Labels used: No, Yes
+2016-08-24 09:27:35,427 INFO: Info: Length of dataset:347
+2016-08-24 09:27:35,428 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:27:35,428 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:27:35,429 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:27:35,429 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:27:35,430 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:27:35,430 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:27:35,430 INFO: Done: Read Database Files
+2016-08-24 09:27:35,430 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:27:35,433 INFO: Done: Determine validation split
+2016-08-24 09:27:35,434 INFO: Start: Determine 2 folds
+2016-08-24 09:27:35,442 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:27:35,442 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:27:35,442 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:27:35,442 INFO: Done: Determine folds
+2016-08-24 09:27:35,442 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:27:35,442 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:27:35,442 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:27:42,729 DEBUG: 0.591873198847Poulet
+2016-08-24 09:27:42,729 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:27:42,730 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:27:44,640 DEBUG: 0.569740634006Poulet
+2016-08-24 09:27:44,640 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:27:44,641 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 09:28:01,306 DEBUG: 0.576945244957Poulet
+2016-08-24 09:28:01,306 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:28:01,307 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 09:28:03,058 DEBUG: 0.582305475504Poulet
+2016-08-24 09:28:03,058 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:28:03,058 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 09:28:40,826 DEBUG: 0.558962536023Poulet
+2016-08-24 09:28:40,827 DEBUG: 0.560518731988Poulet
+2016-08-24 09:28:40,827 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:28:40,827 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 09:28:40,827 INFO: Start: Fold number 1
+2016-08-24 09:28:42,410 DEBUG: Start: Iteration 1
+2016-08-24 09:28:42,426 DEBUG: View 0 : 0.602649006623
+2016-08-24 09:28:42,433 DEBUG: View 1 : 0.649006622517
+2016-08-24 09:28:42,460 DEBUG: View 2 : 0.602649006623
+2016-08-24 09:28:42,467 DEBUG: View 3 : 0.602649006623
+2016-08-24 09:28:42,507 DEBUG: Best view : Methyl_
+2016-08-24 09:28:42,579 DEBUG: Start: Iteration 2
+2016-08-24 09:28:42,596 DEBUG: View 0 : 0.549668874172
+2016-08-24 09:28:42,603 DEBUG: View 1 : 0.576158940397
+2016-08-24 09:28:42,639 DEBUG: View 2 : 0.529801324503
+2016-08-24 09:28:42,646 DEBUG: View 3 : 0.576158940397
+2016-08-24 09:28:42,689 DEBUG: Best view : MiRNA__
+2016-08-24 09:28:42,816 DEBUG: Start: Iteration 3
+2016-08-24 09:28:42,832 DEBUG: View 0 : 0.470198675497
+2016-08-24 09:28:42,840 DEBUG: View 1 : 0.443708609272
+2016-08-24 09:28:42,875 DEBUG: View 2 : 0.543046357616
+2016-08-24 09:28:42,882 DEBUG: View 3 : 0.443708609272
+2016-08-24 09:28:42,933 DEBUG: Best view : RANSeq_
+2016-08-24 09:28:43,132 DEBUG: Start: Iteration 4
+2016-08-24 09:28:43,148 DEBUG: View 0 : 0.390728476821
+2016-08-24 09:28:43,155 DEBUG: View 1 : 0.715231788079
+2016-08-24 09:28:43,190 DEBUG: View 2 : 0.503311258278
+2016-08-24 09:28:43,198 DEBUG: View 3 : 0.483443708609
+2016-08-24 09:28:43,250 DEBUG: Best view : MiRNA__
+2016-08-24 09:28:43,503 DEBUG: Start: Iteration 5
+2016-08-24 09:28:43,520 DEBUG: View 0 : 0.483443708609
+2016-08-24 09:28:43,527 DEBUG: View 1 : 0.397350993377
+2016-08-24 09:28:43,563 DEBUG: View 2 : 0.602649006623
+2016-08-24 09:28:43,570 DEBUG: View 3 : 0.629139072848
+2016-08-24 09:28:43,624 DEBUG: Best view : Clinic_
+2016-08-24 09:28:43,933 DEBUG: Start: Iteration 6
+2016-08-24 09:28:43,949 DEBUG: View 0 : 0.675496688742
+2016-08-24 09:28:43,956 DEBUG: View 1 : 0.456953642384
+2016-08-24 09:28:43,992 DEBUG: View 2 : 0.476821192053
+2016-08-24 09:28:43,999 DEBUG: View 3 : 0.503311258278
+2016-08-24 09:28:44,056 DEBUG: Best view : Methyl_
+2016-08-24 09:28:44,423 DEBUG: Start: Iteration 7
+2016-08-24 09:28:44,439 DEBUG: View 0 : 0.602649006623
+2016-08-24 09:28:44,447 DEBUG: View 1 : 0.298013245033
+2016-08-24 09:28:44,482 DEBUG: View 2 : 0.602649006623
+2016-08-24 09:28:44,489 DEBUG: View 3 : 0.569536423841
+2016-08-24 09:28:44,547 DEBUG: Best view : RANSeq_
+2016-08-24 09:28:44,982 DEBUG: Start: Iteration 8
+2016-08-24 09:28:44,998 DEBUG: View 0 : 0.529801324503
+2016-08-24 09:28:45,005 DEBUG: View 1 : 0.390728476821
+2016-08-24 09:28:45,041 DEBUG: View 2 : 0.509933774834
+2016-08-24 09:28:45,048 DEBUG: View 3 : 0.509933774834
+2016-08-24 09:28:45,108 DEBUG: Best view : Methyl_
+2016-08-24 09:28:45,601 DEBUG: Start: Iteration 9
+2016-08-24 09:28:45,618 DEBUG: View 0 : 0.417218543046
+2016-08-24 09:28:45,625 DEBUG: View 1 : 0.609271523179
+2016-08-24 09:28:45,660 DEBUG: View 2 : 0.582781456954
+2016-08-24 09:28:45,668 DEBUG: View 3 : 0.476821192053
+2016-08-24 09:28:45,730 DEBUG: Best view : MiRNA__
+2016-08-24 09:28:46,279 DEBUG: Start: Iteration 10
+2016-08-24 09:28:46,295 DEBUG: View 0 : 0.503311258278
+2016-08-24 09:28:46,303 DEBUG: View 1 : 0.635761589404
+2016-08-24 09:28:46,338 DEBUG: View 2 : 0.470198675497
+2016-08-24 09:28:46,345 DEBUG: View 3 : 0.58940397351
+2016-08-24 09:28:46,410 DEBUG: Best view : MiRNA__
+2016-08-24 09:28:47,014 DEBUG: Start: Iteration 11
+2016-08-24 09:28:47,030 DEBUG: View 0 : 0.562913907285
+2016-08-24 09:28:47,038 DEBUG: View 1 : 0.582781456954
+2016-08-24 09:28:47,073 DEBUG: View 2 : 0.423841059603
+2016-08-24 09:28:47,080 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:28:47,147 DEBUG: Best view : MiRNA__
+2016-08-24 09:28:47,805 DEBUG: Start: Iteration 12
+2016-08-24 09:28:47,821 DEBUG: View 0 : 0.529801324503
+2016-08-24 09:28:47,829 DEBUG: View 1 : 0.635761589404
+2016-08-24 09:28:47,864 DEBUG: View 2 : 0.437086092715
+2016-08-24 09:28:47,871 DEBUG: View 3 : 0.576158940397
+2016-08-24 09:28:47,940 DEBUG: Best view : MiRNA__
+2016-08-24 09:28:48,655 DEBUG: Start: Iteration 13
+2016-08-24 09:28:48,671 DEBUG: View 0 : 0.483443708609
+2016-08-24 09:28:48,679 DEBUG: View 1 : 0.668874172185
+2016-08-24 09:28:48,714 DEBUG: View 2 : 0.53642384106
+2016-08-24 09:28:48,721 DEBUG: View 3 : 0.430463576159
+2016-08-24 09:28:48,792 DEBUG: Best view : MiRNA__
+2016-08-24 09:28:49,562 DEBUG: Start: Iteration 14
+2016-08-24 09:28:49,579 DEBUG: View 0 : 0.450331125828
+2016-08-24 09:28:49,586 DEBUG: View 1 : 0.443708609272
+2016-08-24 09:28:49,621 DEBUG: View 2 : 0.483443708609
+2016-08-24 09:28:49,628 DEBUG: View 3 : 0.423841059603
+2016-08-24 09:28:49,628 WARNING: All bad for iteration 13
+2016-08-24 09:28:49,703 DEBUG: Best view : RANSeq_
+2016-08-24 09:28:50,539 DEBUG: Start: Iteration 15
+2016-08-24 09:28:50,555 DEBUG: View 0 : 0.470198675497
+2016-08-24 09:28:50,563 DEBUG: View 1 : 0.615894039735
+2016-08-24 09:28:50,598 DEBUG: View 2 : 0.490066225166
+2016-08-24 09:28:50,605 DEBUG: View 3 : 0.569536423841
+2016-08-24 09:28:50,682 DEBUG: Best view : MiRNA__
+2016-08-24 09:28:51,575 DEBUG: Start: Iteration 16
+2016-08-24 09:28:51,591 DEBUG: View 0 : 0.456953642384
+2016-08-24 09:28:51,598 DEBUG: View 1 : 0.430463576159
+2016-08-24 09:28:51,633 DEBUG: View 2 : 0.562913907285
+2016-08-24 09:28:51,640 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:28:51,718 DEBUG: Best view : Clinic_
+2016-08-24 09:28:52,694 DEBUG: Start: Iteration 17
+2016-08-24 09:28:52,710 DEBUG: View 0 : 0.403973509934
+2016-08-24 09:28:52,717 DEBUG: View 1 : 0.370860927152
+2016-08-24 09:28:52,752 DEBUG: View 2 : 0.476821192053
+2016-08-24 09:28:52,760 DEBUG: View 3 : 0.417218543046
+2016-08-24 09:28:52,760 WARNING: All bad for iteration 16
+2016-08-24 09:28:52,839 DEBUG: Best view : RANSeq_
+2016-08-24 09:28:53,853 DEBUG: Start: Iteration 18
+2016-08-24 09:28:53,869 DEBUG: View 0 : 0.437086092715
+2016-08-24 09:28:53,876 DEBUG: View 1 : 0.483443708609
+2016-08-24 09:28:53,911 DEBUG: View 2 : 0.622516556291
+2016-08-24 09:28:53,918 DEBUG: View 3 : 0.629139072848
+2016-08-24 09:28:54,002 DEBUG: Best view : Clinic_
+2016-08-24 09:28:55,105 DEBUG: Start: Iteration 19
+2016-08-24 09:28:55,121 DEBUG: View 0 : 0.615894039735
+2016-08-24 09:28:55,129 DEBUG: View 1 : 0.430463576159
+2016-08-24 09:28:55,164 DEBUG: View 2 : 0.46357615894
+2016-08-24 09:28:55,171 DEBUG: View 3 : 0.496688741722
+2016-08-24 09:28:55,255 DEBUG: Best view : Methyl_
+2016-08-24 09:28:56,383 DEBUG: Start: Iteration 20
+2016-08-24 09:28:56,399 DEBUG: View 0 : 0.503311258278
+2016-08-24 09:28:56,407 DEBUG: View 1 : 0.602649006623
+2016-08-24 09:28:56,442 DEBUG: View 2 : 0.576158940397
+2016-08-24 09:28:56,449 DEBUG: View 3 : 0.576158940397
+2016-08-24 09:28:56,535 DEBUG: Best view : MiRNA__
+2016-08-24 09:28:57,716 DEBUG: Start: Iteration 21
+2016-08-24 09:28:57,732 DEBUG: View 0 : 0.496688741722
+2016-08-24 09:28:57,739 DEBUG: View 1 : 0.46357615894
+2016-08-24 09:28:57,774 DEBUG: View 2 : 0.417218543046
+2016-08-24 09:28:57,781 DEBUG: View 3 : 0.569536423841
+2016-08-24 09:28:57,870 DEBUG: Best view : Clinic_
+2016-08-24 09:28:59,107 DEBUG: Start: Iteration 22
+2016-08-24 09:28:59,123 DEBUG: View 0 : 0.728476821192
+2016-08-24 09:28:59,130 DEBUG: View 1 : 0.350993377483
+2016-08-24 09:28:59,165 DEBUG: View 2 : 0.529801324503
+2016-08-24 09:28:59,172 DEBUG: View 3 : 0.437086092715
+2016-08-24 09:28:59,262 DEBUG: Best view : Methyl_
+2016-08-24 09:29:00,559 DEBUG: Start: Iteration 23
+2016-08-24 09:29:00,575 DEBUG: View 0 : 0.476821192053
+2016-08-24 09:29:00,583 DEBUG: View 1 : 0.609271523179
+2016-08-24 09:29:00,618 DEBUG: View 2 : 0.503311258278
+2016-08-24 09:29:00,625 DEBUG: View 3 : 0.496688741722
+2016-08-24 09:29:00,718 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:02,130 DEBUG: Start: Iteration 24
+2016-08-24 09:29:02,147 DEBUG: View 0 : 0.615894039735
+2016-08-24 09:29:02,155 DEBUG: View 1 : 0.397350993377
+2016-08-24 09:29:02,193 DEBUG: View 2 : 0.476821192053
+2016-08-24 09:29:02,201 DEBUG: View 3 : 0.576158940397
+2016-08-24 09:29:02,307 DEBUG: Best view : Methyl_
+2016-08-24 09:29:03,725 DEBUG: Start: Iteration 25
+2016-08-24 09:29:03,741 DEBUG: View 0 : 0.443708609272
+2016-08-24 09:29:03,748 DEBUG: View 1 : 0.615894039735
+2016-08-24 09:29:03,783 DEBUG: View 2 : 0.443708609272
+2016-08-24 09:29:03,790 DEBUG: View 3 : 0.370860927152
+2016-08-24 09:29:03,888 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:05,358 DEBUG: Start: Iteration 26
+2016-08-24 09:29:05,374 DEBUG: View 0 : 0.615894039735
+2016-08-24 09:29:05,381 DEBUG: View 1 : 0.556291390728
+2016-08-24 09:29:05,417 DEBUG: View 2 : 0.556291390728
+2016-08-24 09:29:05,425 DEBUG: View 3 : 0.41059602649
+2016-08-24 09:29:05,526 DEBUG: Best view : Methyl_
+2016-08-24 09:29:07,057 DEBUG: Start: Iteration 27
+2016-08-24 09:29:07,073 DEBUG: View 0 : 0.423841059603
+2016-08-24 09:29:07,081 DEBUG: View 1 : 0.688741721854
+2016-08-24 09:29:07,116 DEBUG: View 2 : 0.509933774834
+2016-08-24 09:29:07,123 DEBUG: View 3 : 0.503311258278
+2016-08-24 09:29:07,225 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:08,808 DEBUG: Start: Iteration 28
+2016-08-24 09:29:08,824 DEBUG: View 0 : 0.543046357616
+2016-08-24 09:29:08,832 DEBUG: View 1 : 0.675496688742
+2016-08-24 09:29:08,867 DEBUG: View 2 : 0.549668874172
+2016-08-24 09:29:08,874 DEBUG: View 3 : 0.483443708609
+2016-08-24 09:29:08,979 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:10,621 DEBUG: Start: Iteration 29
+2016-08-24 09:29:10,637 DEBUG: View 0 : 0.53642384106
+2016-08-24 09:29:10,645 DEBUG: View 1 : 0.701986754967
+2016-08-24 09:29:10,680 DEBUG: View 2 : 0.496688741722
+2016-08-24 09:29:10,687 DEBUG: View 3 : 0.516556291391
+2016-08-24 09:29:10,794 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:12,491 DEBUG: Start: Iteration 30
+2016-08-24 09:29:12,507 DEBUG: View 0 : 0.483443708609
+2016-08-24 09:29:12,514 DEBUG: View 1 : 0.615894039735
+2016-08-24 09:29:12,550 DEBUG: View 2 : 0.390728476821
+2016-08-24 09:29:12,557 DEBUG: View 3 : 0.509933774834
+2016-08-24 09:29:12,666 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:14,431 DEBUG: Start: Iteration 31
+2016-08-24 09:29:14,447 DEBUG: View 0 : 0.53642384106
+2016-08-24 09:29:14,454 DEBUG: View 1 : 0.58940397351
+2016-08-24 09:29:14,490 DEBUG: View 2 : 0.529801324503
+2016-08-24 09:29:14,497 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:29:14,607 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:16,428 DEBUG: Start: Iteration 32
+2016-08-24 09:29:16,445 DEBUG: View 0 : 0.523178807947
+2016-08-24 09:29:16,452 DEBUG: View 1 : 0.483443708609
+2016-08-24 09:29:16,488 DEBUG: View 2 : 0.470198675497
+2016-08-24 09:29:16,495 DEBUG: View 3 : 0.456953642384
+2016-08-24 09:29:16,608 DEBUG: Best view : Methyl_
+2016-08-24 09:29:18,508 DEBUG: Start: Iteration 33
+2016-08-24 09:29:18,524 DEBUG: View 0 : 0.582781456954
+2016-08-24 09:29:18,531 DEBUG: View 1 : 0.662251655629
+2016-08-24 09:29:18,568 DEBUG: View 2 : 0.476821192053
+2016-08-24 09:29:18,575 DEBUG: View 3 : 0.523178807947
+2016-08-24 09:29:18,688 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:20,620 DEBUG: Start: Iteration 34
+2016-08-24 09:29:20,637 DEBUG: View 0 : 0.529801324503
+2016-08-24 09:29:20,644 DEBUG: View 1 : 0.450331125828
+2016-08-24 09:29:20,679 DEBUG: View 2 : 0.476821192053
+2016-08-24 09:29:20,686 DEBUG: View 3 : 0.596026490066
+2016-08-24 09:29:20,802 DEBUG: Best view : Clinic_
+2016-08-24 09:29:22,810 DEBUG: Start: Iteration 35
+2016-08-24 09:29:22,827 DEBUG: View 0 : 0.523178807947
+2016-08-24 09:29:22,834 DEBUG: View 1 : 0.582781456954
+2016-08-24 09:29:22,872 DEBUG: View 2 : 0.668874172185
+2016-08-24 09:29:22,880 DEBUG: View 3 : 0.58940397351
+2016-08-24 09:29:23,000 DEBUG: Best view : RANSeq_
+2016-08-24 09:29:25,109 DEBUG: Start: Iteration 36
+2016-08-24 09:29:25,125 DEBUG: View 0 : 0.46357615894
+2016-08-24 09:29:25,133 DEBUG: View 1 : 0.390728476821
+2016-08-24 09:29:25,170 DEBUG: View 2 : 0.384105960265
+2016-08-24 09:29:25,178 DEBUG: View 3 : 0.53642384106
+2016-08-24 09:29:25,307 DEBUG: Best view : Clinic_
+2016-08-24 09:29:27,522 DEBUG: Start: Iteration 37
+2016-08-24 09:29:27,538 DEBUG: View 0 : 0.635761589404
+2016-08-24 09:29:27,545 DEBUG: View 1 : 0.476821192053
+2016-08-24 09:29:27,580 DEBUG: View 2 : 0.509933774834
+2016-08-24 09:29:27,588 DEBUG: View 3 : 0.523178807947
+2016-08-24 09:29:27,714 DEBUG: Best view : Methyl_
+2016-08-24 09:29:29,889 DEBUG: Start: Iteration 38
+2016-08-24 09:29:29,905 DEBUG: View 0 : 0.569536423841
+2016-08-24 09:29:29,912 DEBUG: View 1 : 0.64238410596
+2016-08-24 09:29:29,948 DEBUG: View 2 : 0.556291390728
+2016-08-24 09:29:29,955 DEBUG: View 3 : 0.423841059603
+2016-08-24 09:29:30,079 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:32,282 DEBUG: Start: Iteration 39
+2016-08-24 09:29:32,298 DEBUG: View 0 : 0.549668874172
+2016-08-24 09:29:32,305 DEBUG: View 1 : 0.562913907285
+2016-08-24 09:29:32,341 DEBUG: View 2 : 0.403973509934
+2016-08-24 09:29:32,348 DEBUG: View 3 : 0.503311258278
+2016-08-24 09:29:32,473 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:34,735 DEBUG: Start: Iteration 40
+2016-08-24 09:29:34,751 DEBUG: View 0 : 0.569536423841
+2016-08-24 09:29:34,760 DEBUG: View 1 : 0.437086092715
+2016-08-24 09:29:34,797 DEBUG: View 2 : 0.46357615894
+2016-08-24 09:29:34,804 DEBUG: View 3 : 0.503311258278
+2016-08-24 09:29:34,933 DEBUG: Best view : Methyl_
+2016-08-24 09:29:37,258 DEBUG: Start: Iteration 41
+2016-08-24 09:29:37,274 DEBUG: View 0 : 0.549668874172
+2016-08-24 09:29:37,281 DEBUG: View 1 : 0.708609271523
+2016-08-24 09:29:37,316 DEBUG: View 2 : 0.41059602649
+2016-08-24 09:29:37,323 DEBUG: View 3 : 0.437086092715
+2016-08-24 09:29:37,453 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:39,827 DEBUG: Start: Iteration 42
+2016-08-24 09:29:39,843 DEBUG: View 0 : 0.615894039735
+2016-08-24 09:29:39,850 DEBUG: View 1 : 0.549668874172
+2016-08-24 09:29:39,886 DEBUG: View 2 : 0.562913907285
+2016-08-24 09:29:39,893 DEBUG: View 3 : 0.543046357616
+2016-08-24 09:29:40,025 DEBUG: Best view : Methyl_
+2016-08-24 09:29:42,592 DEBUG: Start: Iteration 43
+2016-08-24 09:29:42,611 DEBUG: View 0 : 0.602649006623
+2016-08-24 09:29:42,620 DEBUG: View 1 : 0.549668874172
+2016-08-24 09:29:42,664 DEBUG: View 2 : 0.450331125828
+2016-08-24 09:29:42,673 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:29:42,907 DEBUG: Best view : Methyl_
+2016-08-24 09:29:45,611 DEBUG: Start: Iteration 44
+2016-08-24 09:29:45,627 DEBUG: View 0 : 0.344370860927
+2016-08-24 09:29:45,635 DEBUG: View 1 : 0.582781456954
+2016-08-24 09:29:45,672 DEBUG: View 2 : 0.556291390728
+2016-08-24 09:29:45,679 DEBUG: View 3 : 0.450331125828
+2016-08-24 09:29:45,821 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:48,520 DEBUG: Start: Iteration 45
+2016-08-24 09:29:48,539 DEBUG: View 0 : 0.662251655629
+2016-08-24 09:29:48,548 DEBUG: View 1 : 0.596026490066
+2016-08-24 09:29:48,589 DEBUG: View 2 : 0.549668874172
+2016-08-24 09:29:48,597 DEBUG: View 3 : 0.483443708609
+2016-08-24 09:29:48,760 DEBUG: Best view : Methyl_
+2016-08-24 09:29:51,441 DEBUG: Start: Iteration 46
+2016-08-24 09:29:51,457 DEBUG: View 0 : 0.523178807947
+2016-08-24 09:29:51,466 DEBUG: View 1 : 0.64238410596
+2016-08-24 09:29:51,502 DEBUG: View 2 : 0.562913907285
+2016-08-24 09:29:51,510 DEBUG: View 3 : 0.430463576159
+2016-08-24 09:29:51,667 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:54,334 DEBUG: Start: Iteration 47
+2016-08-24 09:29:54,350 DEBUG: View 0 : 0.437086092715
+2016-08-24 09:29:54,358 DEBUG: View 1 : 0.53642384106
+2016-08-24 09:29:54,393 DEBUG: View 2 : 0.437086092715
+2016-08-24 09:29:54,401 DEBUG: View 3 : 0.483443708609
+2016-08-24 09:29:54,544 DEBUG: Best view : MiRNA__
+2016-08-24 09:29:57,261 DEBUG: Start: Iteration 48
+2016-08-24 09:29:57,277 DEBUG: View 0 : 0.53642384106
+2016-08-24 09:29:57,285 DEBUG: View 1 : 0.569536423841
+2016-08-24 09:29:57,320 DEBUG: View 2 : 0.562913907285
+2016-08-24 09:29:57,327 DEBUG: View 3 : 0.456953642384
+2016-08-24 09:29:57,473 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:00,260 DEBUG: Start: Iteration 49
+2016-08-24 09:30:00,276 DEBUG: View 0 : 0.509933774834
+2016-08-24 09:30:00,283 DEBUG: View 1 : 0.41059602649
+2016-08-24 09:30:00,319 DEBUG: View 2 : 0.562913907285
+2016-08-24 09:30:00,326 DEBUG: View 3 : 0.496688741722
+2016-08-24 09:30:00,474 DEBUG: Best view : RANSeq_
+2016-08-24 09:30:03,315 DEBUG: Start: Iteration 50
+2016-08-24 09:30:03,331 DEBUG: View 0 : 0.569536423841
+2016-08-24 09:30:03,338 DEBUG: View 1 : 0.437086092715
+2016-08-24 09:30:03,373 DEBUG: View 2 : 0.430463576159
+2016-08-24 09:30:03,381 DEBUG: View 3 : 0.503311258278
+2016-08-24 09:30:03,531 DEBUG: Best view : Methyl_
+2016-08-24 09:30:06,430 DEBUG: Start: Iteration 51
+2016-08-24 09:30:06,446 DEBUG: View 0 : 0.476821192053
+2016-08-24 09:30:06,453 DEBUG: View 1 : 0.602649006623
+2016-08-24 09:30:06,489 DEBUG: View 2 : 0.556291390728
+2016-08-24 09:30:06,496 DEBUG: View 3 : 0.609271523179
+2016-08-24 09:30:06,648 DEBUG: Best view : Clinic_
+2016-08-24 09:30:09,800 DEBUG: Start: Iteration 52
+2016-08-24 09:30:09,818 DEBUG: View 0 : 0.64238410596
+2016-08-24 09:30:09,826 DEBUG: View 1 : 0.490066225166
+2016-08-24 09:30:09,861 DEBUG: View 2 : 0.423841059603
+2016-08-24 09:30:09,868 DEBUG: View 3 : 0.470198675497
+2016-08-24 09:30:10,026 DEBUG: Best view : Methyl_
+2016-08-24 09:30:13,052 INFO: Start: Classification
+2016-08-24 09:30:20,660 INFO: Done: Fold number 1
+2016-08-24 09:30:20,660 INFO: Start: Fold number 2
+2016-08-24 09:30:22,264 DEBUG: Start: Iteration 1
+2016-08-24 09:30:22,279 DEBUG: View 0 : 0.62962962963
+2016-08-24 09:30:22,287 DEBUG: View 1 : 0.62962962963
+2016-08-24 09:30:22,315 DEBUG: View 2 : 0.62962962963
+2016-08-24 09:30:22,323 DEBUG: View 3 : 0.62962962963
+2016-08-24 09:30:22,364 DEBUG: Best view : Methyl_
+2016-08-24 09:30:22,443 DEBUG: Start: Iteration 2
+2016-08-24 09:30:22,461 DEBUG: View 0 : 0.432098765432
+2016-08-24 09:30:22,468 DEBUG: View 1 : 0.345679012346
+2016-08-24 09:30:22,506 DEBUG: View 2 : 0.37037037037
+2016-08-24 09:30:22,514 DEBUG: View 3 : 0.598765432099
+2016-08-24 09:30:22,560 DEBUG: Best view : Clinic_
+2016-08-24 09:30:22,701 DEBUG: Start: Iteration 3
+2016-08-24 09:30:22,718 DEBUG: View 0 : 0.481481481481
+2016-08-24 09:30:22,726 DEBUG: View 1 : 0.425925925926
+2016-08-24 09:30:22,764 DEBUG: View 2 : 0.555555555556
+2016-08-24 09:30:22,772 DEBUG: View 3 : 0.444444444444
+2016-08-24 09:30:22,827 DEBUG: Best view : RANSeq_
+2016-08-24 09:30:23,047 DEBUG: Start: Iteration 4
+2016-08-24 09:30:23,064 DEBUG: View 0 : 0.635802469136
+2016-08-24 09:30:23,072 DEBUG: View 1 : 0.549382716049
+2016-08-24 09:30:23,109 DEBUG: View 2 : 0.450617283951
+2016-08-24 09:30:23,117 DEBUG: View 3 : 0.598765432099
+2016-08-24 09:30:23,174 DEBUG: Best view : Methyl_
+2016-08-24 09:30:23,451 DEBUG: Start: Iteration 5
+2016-08-24 09:30:23,468 DEBUG: View 0 : 0.462962962963
+2016-08-24 09:30:23,476 DEBUG: View 1 : 0.364197530864
+2016-08-24 09:30:23,513 DEBUG: View 2 : 0.469135802469
+2016-08-24 09:30:23,521 DEBUG: View 3 : 0.524691358025
+2016-08-24 09:30:23,579 DEBUG: Best view : Clinic_
+2016-08-24 09:30:23,913 DEBUG: Start: Iteration 6
+2016-08-24 09:30:23,929 DEBUG: View 0 : 0.592592592593
+2016-08-24 09:30:23,937 DEBUG: View 1 : 0.666666666667
+2016-08-24 09:30:23,974 DEBUG: View 2 : 0.493827160494
+2016-08-24 09:30:23,981 DEBUG: View 3 : 0.549382716049
+2016-08-24 09:30:24,042 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:24,446 DEBUG: Start: Iteration 7
+2016-08-24 09:30:24,463 DEBUG: View 0 : 0.413580246914
+2016-08-24 09:30:24,471 DEBUG: View 1 : 0.450617283951
+2016-08-24 09:30:24,509 DEBUG: View 2 : 0.432098765432
+2016-08-24 09:30:24,517 DEBUG: View 3 : 0.401234567901
+2016-08-24 09:30:24,517 WARNING: All bad for iteration 6
+2016-08-24 09:30:24,581 DEBUG: Best view : Clinic_
+2016-08-24 09:30:25,033 DEBUG: Start: Iteration 8
+2016-08-24 09:30:25,050 DEBUG: View 0 : 0.413580246914
+2016-08-24 09:30:25,058 DEBUG: View 1 : 0.648148148148
+2016-08-24 09:30:25,095 DEBUG: View 2 : 0.598765432099
+2016-08-24 09:30:25,102 DEBUG: View 3 : 0.549382716049
+2016-08-24 09:30:25,168 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:25,678 DEBUG: Start: Iteration 9
+2016-08-24 09:30:25,695 DEBUG: View 0 : 0.549382716049
+2016-08-24 09:30:25,703 DEBUG: View 1 : 0.549382716049
+2016-08-24 09:30:25,740 DEBUG: View 2 : 0.567901234568
+2016-08-24 09:30:25,748 DEBUG: View 3 : 0.530864197531
+2016-08-24 09:30:25,815 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:26,384 DEBUG: Start: Iteration 10
+2016-08-24 09:30:26,401 DEBUG: View 0 : 0.716049382716
+2016-08-24 09:30:26,409 DEBUG: View 1 : 0.524691358025
+2016-08-24 09:30:26,445 DEBUG: View 2 : 0.444444444444
+2016-08-24 09:30:26,453 DEBUG: View 3 : 0.395061728395
+2016-08-24 09:30:26,522 DEBUG: Best view : Methyl_
+2016-08-24 09:30:27,154 DEBUG: Start: Iteration 11
+2016-08-24 09:30:27,171 DEBUG: View 0 : 0.407407407407
+2016-08-24 09:30:27,179 DEBUG: View 1 : 0.506172839506
+2016-08-24 09:30:27,216 DEBUG: View 2 : 0.469135802469
+2016-08-24 09:30:27,223 DEBUG: View 3 : 0.506172839506
+2016-08-24 09:30:27,295 DEBUG: Best view : Clinic_
+2016-08-24 09:30:27,985 DEBUG: Start: Iteration 12
+2016-08-24 09:30:28,001 DEBUG: View 0 : 0.524691358025
+2016-08-24 09:30:28,009 DEBUG: View 1 : 0.586419753086
+2016-08-24 09:30:28,046 DEBUG: View 2 : 0.543209876543
+2016-08-24 09:30:28,054 DEBUG: View 3 : 0.617283950617
+2016-08-24 09:30:28,128 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:28,887 DEBUG: Start: Iteration 13
+2016-08-24 09:30:28,904 DEBUG: View 0 : 0.567901234568
+2016-08-24 09:30:28,911 DEBUG: View 1 : 0.648148148148
+2016-08-24 09:30:28,948 DEBUG: View 2 : 0.432098765432
+2016-08-24 09:30:28,956 DEBUG: View 3 : 0.395061728395
+2016-08-24 09:30:29,034 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:29,843 DEBUG: Start: Iteration 14
+2016-08-24 09:30:29,860 DEBUG: View 0 : 0.5
+2016-08-24 09:30:29,868 DEBUG: View 1 : 0.716049382716
+2016-08-24 09:30:29,905 DEBUG: View 2 : 0.487654320988
+2016-08-24 09:30:29,912 DEBUG: View 3 : 0.537037037037
+2016-08-24 09:30:29,991 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:30,859 DEBUG: Start: Iteration 15
+2016-08-24 09:30:30,876 DEBUG: View 0 : 0.487654320988
+2016-08-24 09:30:30,884 DEBUG: View 1 : 0.70987654321
+2016-08-24 09:30:30,921 DEBUG: View 2 : 0.425925925926
+2016-08-24 09:30:30,929 DEBUG: View 3 : 0.462962962963
+2016-08-24 09:30:31,012 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:31,939 DEBUG: Start: Iteration 16
+2016-08-24 09:30:31,956 DEBUG: View 0 : 0.574074074074
+2016-08-24 09:30:31,963 DEBUG: View 1 : 0.364197530864
+2016-08-24 09:30:32,000 DEBUG: View 2 : 0.592592592593
+2016-08-24 09:30:32,008 DEBUG: View 3 : 0.444444444444
+2016-08-24 09:30:32,090 DEBUG: Best view : Methyl_
+2016-08-24 09:30:33,081 DEBUG: Start: Iteration 17
+2016-08-24 09:30:33,098 DEBUG: View 0 : 0.58024691358
+2016-08-24 09:30:33,106 DEBUG: View 1 : 0.413580246914
+2016-08-24 09:30:33,143 DEBUG: View 2 : 0.5
+2016-08-24 09:30:33,150 DEBUG: View 3 : 0.543209876543
+2016-08-24 09:30:33,236 DEBUG: Best view : Methyl_
+2016-08-24 09:30:34,291 DEBUG: Start: Iteration 18
+2016-08-24 09:30:34,307 DEBUG: View 0 : 0.530864197531
+2016-08-24 09:30:34,315 DEBUG: View 1 : 0.623456790123
+2016-08-24 09:30:34,352 DEBUG: View 2 : 0.444444444444
+2016-08-24 09:30:34,360 DEBUG: View 3 : 0.493827160494
+2016-08-24 09:30:34,448 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:35,560 DEBUG: Start: Iteration 19
+2016-08-24 09:30:35,576 DEBUG: View 0 : 0.41975308642
+2016-08-24 09:30:35,584 DEBUG: View 1 : 0.481481481481
+2016-08-24 09:30:35,621 DEBUG: View 2 : 0.432098765432
+2016-08-24 09:30:35,628 DEBUG: View 3 : 0.518518518519
+2016-08-24 09:30:35,719 DEBUG: Best view : Clinic_
+2016-08-24 09:30:36,890 DEBUG: Start: Iteration 20
+2016-08-24 09:30:36,907 DEBUG: View 0 : 0.518518518519
+2016-08-24 09:30:36,915 DEBUG: View 1 : 0.283950617284
+2016-08-24 09:30:36,952 DEBUG: View 2 : 0.58024691358
+2016-08-24 09:30:36,960 DEBUG: View 3 : 0.487654320988
+2016-08-24 09:30:37,053 DEBUG: Best view : RANSeq_
+2016-08-24 09:30:38,326 DEBUG: Start: Iteration 21
+2016-08-24 09:30:38,343 DEBUG: View 0 : 0.450617283951
+2016-08-24 09:30:38,351 DEBUG: View 1 : 0.623456790123
+2016-08-24 09:30:38,389 DEBUG: View 2 : 0.456790123457
+2016-08-24 09:30:38,397 DEBUG: View 3 : 0.462962962963
+2016-08-24 09:30:38,493 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:39,813 DEBUG: Start: Iteration 22
+2016-08-24 09:30:39,831 DEBUG: View 0 : 0.598765432099
+2016-08-24 09:30:39,839 DEBUG: View 1 : 0.345679012346
+2016-08-24 09:30:39,876 DEBUG: View 2 : 0.462962962963
+2016-08-24 09:30:39,884 DEBUG: View 3 : 0.604938271605
+2016-08-24 09:30:39,984 DEBUG: Best view : Methyl_
+2016-08-24 09:30:41,351 DEBUG: Start: Iteration 23
+2016-08-24 09:30:41,368 DEBUG: View 0 : 0.524691358025
+2016-08-24 09:30:41,375 DEBUG: View 1 : 0.549382716049
+2016-08-24 09:30:41,413 DEBUG: View 2 : 0.635802469136
+2016-08-24 09:30:41,420 DEBUG: View 3 : 0.487654320988
+2016-08-24 09:30:41,519 DEBUG: Best view : RANSeq_
+2016-08-24 09:30:42,958 DEBUG: Start: Iteration 24
+2016-08-24 09:30:42,975 DEBUG: View 0 : 0.679012345679
+2016-08-24 09:30:42,983 DEBUG: View 1 : 0.401234567901
+2016-08-24 09:30:43,020 DEBUG: View 2 : 0.592592592593
+2016-08-24 09:30:43,027 DEBUG: View 3 : 0.456790123457
+2016-08-24 09:30:43,129 DEBUG: Best view : Methyl_
+2016-08-24 09:30:44,627 DEBUG: Start: Iteration 25
+2016-08-24 09:30:44,644 DEBUG: View 0 : 0.487654320988
+2016-08-24 09:30:44,652 DEBUG: View 1 : 0.654320987654
+2016-08-24 09:30:44,689 DEBUG: View 2 : 0.450617283951
+2016-08-24 09:30:44,696 DEBUG: View 3 : 0.58024691358
+2016-08-24 09:30:44,800 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:46,374 DEBUG: Start: Iteration 26
+2016-08-24 09:30:46,392 DEBUG: View 0 : 0.425925925926
+2016-08-24 09:30:46,400 DEBUG: View 1 : 0.376543209877
+2016-08-24 09:30:46,439 DEBUG: View 2 : 0.512345679012
+2016-08-24 09:30:46,446 DEBUG: View 3 : 0.475308641975
+2016-08-24 09:30:46,558 DEBUG: Best view : RANSeq_
+2016-08-24 09:30:48,255 DEBUG: Start: Iteration 27
+2016-08-24 09:30:48,272 DEBUG: View 0 : 0.586419753086
+2016-08-24 09:30:48,280 DEBUG: View 1 : 0.549382716049
+2016-08-24 09:30:48,318 DEBUG: View 2 : 0.5
+2016-08-24 09:30:48,326 DEBUG: View 3 : 0.432098765432
+2016-08-24 09:30:48,434 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:50,179 DEBUG: Start: Iteration 28
+2016-08-24 09:30:50,196 DEBUG: View 0 : 0.524691358025
+2016-08-24 09:30:50,204 DEBUG: View 1 : 0.41975308642
+2016-08-24 09:30:50,242 DEBUG: View 2 : 0.481481481481
+2016-08-24 09:30:50,250 DEBUG: View 3 : 0.549382716049
+2016-08-24 09:30:50,361 DEBUG: Best view : Methyl_
+2016-08-24 09:30:52,134 DEBUG: Start: Iteration 29
+2016-08-24 09:30:52,151 DEBUG: View 0 : 0.438271604938
+2016-08-24 09:30:52,159 DEBUG: View 1 : 0.672839506173
+2016-08-24 09:30:52,197 DEBUG: View 2 : 0.617283950617
+2016-08-24 09:30:52,205 DEBUG: View 3 : 0.407407407407
+2016-08-24 09:30:52,321 DEBUG: Best view : MiRNA__
+2016-08-24 09:30:54,169 DEBUG: Start: Iteration 30
+2016-08-24 09:30:54,186 DEBUG: View 0 : 0.475308641975
+2016-08-24 09:30:54,194 DEBUG: View 1 : 0.487654320988
+2016-08-24 09:30:54,232 DEBUG: View 2 : 0.58024691358
+2016-08-24 09:30:54,239 DEBUG: View 3 : 0.376543209877
+2016-08-24 09:30:54,355 DEBUG: Best view : RANSeq_
+2016-08-24 09:30:56,248 DEBUG: Start: Iteration 31
+2016-08-24 09:30:56,266 DEBUG: View 0 : 0.586419753086
+2016-08-24 09:30:56,274 DEBUG: View 1 : 0.407407407407
+2016-08-24 09:30:56,312 DEBUG: View 2 : 0.66049382716
+2016-08-24 09:30:56,320 DEBUG: View 3 : 0.549382716049
+2016-08-24 09:30:56,442 DEBUG: Best view : RANSeq_
+2016-08-24 09:30:58,403 DEBUG: Start: Iteration 32
+2016-08-24 09:30:58,420 DEBUG: View 0 : 0.469135802469
+2016-08-24 09:30:58,428 DEBUG: View 1 : 0.734567901235
+2016-08-24 09:30:58,465 DEBUG: View 2 : 0.567901234568
+2016-08-24 09:30:58,472 DEBUG: View 3 : 0.506172839506
+2016-08-24 09:30:58,592 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:00,607 DEBUG: Start: Iteration 33
+2016-08-24 09:31:00,623 DEBUG: View 0 : 0.493827160494
+2016-08-24 09:31:00,631 DEBUG: View 1 : 0.685185185185
+2016-08-24 09:31:00,668 DEBUG: View 2 : 0.41975308642
+2016-08-24 09:31:00,676 DEBUG: View 3 : 0.487654320988
+2016-08-24 09:31:00,798 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:02,869 DEBUG: Start: Iteration 34
+2016-08-24 09:31:02,885 DEBUG: View 0 : 0.425925925926
+2016-08-24 09:31:02,893 DEBUG: View 1 : 0.567901234568
+2016-08-24 09:31:02,931 DEBUG: View 2 : 0.425925925926
+2016-08-24 09:31:02,939 DEBUG: View 3 : 0.462962962963
+2016-08-24 09:31:03,064 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:05,264 DEBUG: Start: Iteration 35
+2016-08-24 09:31:05,281 DEBUG: View 0 : 0.611111111111
+2016-08-24 09:31:05,289 DEBUG: View 1 : 0.444444444444
+2016-08-24 09:31:05,327 DEBUG: View 2 : 0.456790123457
+2016-08-24 09:31:05,334 DEBUG: View 3 : 0.567901234568
+2016-08-24 09:31:05,465 DEBUG: Best view : Methyl_
+2016-08-24 09:31:07,724 DEBUG: Start: Iteration 36
+2016-08-24 09:31:07,741 DEBUG: View 0 : 0.296296296296
+2016-08-24 09:31:07,749 DEBUG: View 1 : 0.635802469136
+2016-08-24 09:31:07,787 DEBUG: View 2 : 0.487654320988
+2016-08-24 09:31:07,795 DEBUG: View 3 : 0.506172839506
+2016-08-24 09:31:07,928 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:10,248 DEBUG: Start: Iteration 37
+2016-08-24 09:31:10,264 DEBUG: View 0 : 0.364197530864
+2016-08-24 09:31:10,272 DEBUG: View 1 : 0.623456790123
+2016-08-24 09:31:10,309 DEBUG: View 2 : 0.518518518519
+2016-08-24 09:31:10,317 DEBUG: View 3 : 0.586419753086
+2016-08-24 09:31:10,448 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:12,764 DEBUG: Start: Iteration 38
+2016-08-24 09:31:12,781 DEBUG: View 0 : 0.567901234568
+2016-08-24 09:31:12,789 DEBUG: View 1 : 0.66049382716
+2016-08-24 09:31:12,825 DEBUG: View 2 : 0.561728395062
+2016-08-24 09:31:12,833 DEBUG: View 3 : 0.41975308642
+2016-08-24 09:31:12,966 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:15,340 DEBUG: Start: Iteration 39
+2016-08-24 09:31:15,356 DEBUG: View 0 : 0.432098765432
+2016-08-24 09:31:15,364 DEBUG: View 1 : 0.654320987654
+2016-08-24 09:31:15,402 DEBUG: View 2 : 0.574074074074
+2016-08-24 09:31:15,410 DEBUG: View 3 : 0.450617283951
+2016-08-24 09:31:15,550 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:17,981 DEBUG: Start: Iteration 40
+2016-08-24 09:31:17,998 DEBUG: View 0 : 0.598765432099
+2016-08-24 09:31:18,006 DEBUG: View 1 : 0.623456790123
+2016-08-24 09:31:18,043 DEBUG: View 2 : 0.555555555556
+2016-08-24 09:31:18,051 DEBUG: View 3 : 0.530864197531
+2016-08-24 09:31:18,189 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:20,681 DEBUG: Start: Iteration 41
+2016-08-24 09:31:20,699 DEBUG: View 0 : 0.5
+2016-08-24 09:31:20,706 DEBUG: View 1 : 0.716049382716
+2016-08-24 09:31:20,744 DEBUG: View 2 : 0.388888888889
+2016-08-24 09:31:20,751 DEBUG: View 3 : 0.617283950617
+2016-08-24 09:31:20,895 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:23,510 DEBUG: Start: Iteration 42
+2016-08-24 09:31:23,528 DEBUG: View 0 : 0.388888888889
+2016-08-24 09:31:23,536 DEBUG: View 1 : 0.654320987654
+2016-08-24 09:31:23,574 DEBUG: View 2 : 0.58024691358
+2016-08-24 09:31:23,582 DEBUG: View 3 : 0.407407407407
+2016-08-24 09:31:23,729 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:26,574 DEBUG: Start: Iteration 43
+2016-08-24 09:31:26,592 DEBUG: View 0 : 0.481481481481
+2016-08-24 09:31:26,600 DEBUG: View 1 : 0.604938271605
+2016-08-24 09:31:26,638 DEBUG: View 2 : 0.481481481481
+2016-08-24 09:31:26,646 DEBUG: View 3 : 0.407407407407
+2016-08-24 09:31:26,793 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:29,678 DEBUG: Start: Iteration 44
+2016-08-24 09:31:29,697 DEBUG: View 0 : 0.462962962963
+2016-08-24 09:31:29,706 DEBUG: View 1 : 0.561728395062
+2016-08-24 09:31:29,749 DEBUG: View 2 : 0.537037037037
+2016-08-24 09:31:29,758 DEBUG: View 3 : 0.543209876543
+2016-08-24 09:31:29,929 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:32,694 DEBUG: Start: Iteration 45
+2016-08-24 09:31:32,711 DEBUG: View 0 : 0.530864197531
+2016-08-24 09:31:32,719 DEBUG: View 1 : 0.679012345679
+2016-08-24 09:31:32,756 DEBUG: View 2 : 0.358024691358
+2016-08-24 09:31:32,764 DEBUG: View 3 : 0.518518518519
+2016-08-24 09:31:32,917 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:35,983 DEBUG: Start: Iteration 46
+2016-08-24 09:31:36,000 DEBUG: View 0 : 0.475308641975
+2016-08-24 09:31:36,008 DEBUG: View 1 : 0.382716049383
+2016-08-24 09:31:36,045 DEBUG: View 2 : 0.493827160494
+2016-08-24 09:31:36,053 DEBUG: View 3 : 0.475308641975
+2016-08-24 09:31:36,053 WARNING: All bad for iteration 45
+2016-08-24 09:31:36,209 DEBUG: Best view : RANSeq_
+2016-08-24 09:31:39,103 DEBUG: Start: Iteration 47
+2016-08-24 09:31:39,122 DEBUG: View 0 : 0.611111111111
+2016-08-24 09:31:39,130 DEBUG: View 1 : 0.512345679012
+2016-08-24 09:31:39,174 DEBUG: View 2 : 0.462962962963
+2016-08-24 09:31:39,182 DEBUG: View 3 : 0.506172839506
+2016-08-24 09:31:39,352 DEBUG: Best view : Methyl_
+2016-08-24 09:31:42,556 DEBUG: Start: Iteration 48
+2016-08-24 09:31:42,572 DEBUG: View 0 : 0.530864197531
+2016-08-24 09:31:42,580 DEBUG: View 1 : 0.604938271605
+2016-08-24 09:31:42,619 DEBUG: View 2 : 0.518518518519
+2016-08-24 09:31:42,628 DEBUG: View 3 : 0.407407407407
+2016-08-24 09:31:42,789 DEBUG: Best view : MiRNA__
+2016-08-24 09:31:46,061 DEBUG: Start: Iteration 49
+2016-08-24 09:31:46,078 DEBUG: View 0 : 0.413580246914
+2016-08-24 09:31:46,086 DEBUG: View 1 : 0.549382716049
+2016-08-24 09:31:46,124 DEBUG: View 2 : 0.62962962963
+2016-08-24 09:31:46,131 DEBUG: View 3 : 0.407407407407
+2016-08-24 09:31:46,294 DEBUG: Best view : RANSeq_
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093148-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093148-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..81280faf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093148-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,30 @@
+2016-08-24 09:31:48,286 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:31:48,286 INFO: Info: Labels used: No, Yes
+2016-08-24 09:31:48,287 INFO: Info: Length of dataset:347
+2016-08-24 09:31:48,288 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:31:48,288 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:31:48,289 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:31:48,289 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:31:48,289 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:31:48,290 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:31:48,290 INFO: Done: Read Database Files
+2016-08-24 09:31:48,290 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:31:48,293 INFO: Done: Determine validation split
+2016-08-24 09:31:48,293 INFO: Start: Determine 2 folds
+2016-08-24 09:31:48,304 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:31:48,304 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:31:48,304 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:31:48,304 INFO: Done: Determine folds
+2016-08-24 09:31:48,304 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:31:48,304 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:31:48,305 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:31:55,692 DEBUG: 0.579596541787Poulet
+2016-08-24 09:31:55,692 DEBUG: 0.592103746398Poulet
+2016-08-24 09:31:55,693 DEBUG: 0.599135446686Poulet
+2016-08-24 09:31:55,694 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:31:55,694 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:31:57,621 DEBUG: 0.502997118156Poulet
+2016-08-24 09:31:57,621 DEBUG: 0.571354466859Poulet
+2016-08-24 09:31:57,621 DEBUG: 0.575331412104Poulet
+2016-08-24 09:31:57,621 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:31:57,622 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093234-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093234-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..d2aff05e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093234-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,27 @@
+2016-08-24 09:32:34,093 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:32:34,093 INFO: Info: Labels used: No, Yes
+2016-08-24 09:32:34,094 INFO: Info: Length of dataset:347
+2016-08-24 09:32:34,095 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:32:34,095 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:32:34,096 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:32:34,096 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:32:34,096 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:32:34,097 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:32:34,097 INFO: Done: Read Database Files
+2016-08-24 09:32:34,097 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:32:34,100 INFO: Done: Determine validation split
+2016-08-24 09:32:34,100 INFO: Start: Determine 2 folds
+2016-08-24 09:32:34,111 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:32:34,111 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:32:34,111 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:32:34,111 INFO: Done: Determine folds
+2016-08-24 09:32:34,111 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:32:34,112 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:32:34,112 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:32:41,471 DEBUG: 0.585014409222Poulet
+2016-08-24 09:32:41,471 DEBUG: 0.596714697406Poulet
+2016-08-24 09:32:41,472 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:32:41,472 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:32:43,398 DEBUG: 0.58386167147Poulet
+2016-08-24 09:32:43,399 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:32:43,399 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093312-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093312-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..3862f8c6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093312-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,53 @@
+2016-08-24 09:33:12,122 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:33:12,123 INFO: Info: Labels used: No, Yes
+2016-08-24 09:33:12,123 INFO: Info: Length of dataset:347
+2016-08-24 09:33:12,124 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:33:12,124 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:33:12,125 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:33:12,125 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:33:12,125 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:33:12,126 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:33:12,126 INFO: Done: Read Database Files
+2016-08-24 09:33:12,126 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:33:12,129 INFO: Done: Determine validation split
+2016-08-24 09:33:12,129 INFO: Start: Determine 2 folds
+2016-08-24 09:33:12,143 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:33:12,143 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:33:12,143 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:33:12,143 INFO: Done: Determine folds
+2016-08-24 09:33:12,143 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:33:12,143 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:33:12,143 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:33:19,481 DEBUG: 0.596714697406Poulet
+2016-08-24 09:33:19,481 DEBUG: 0.583227665706Poulet
+2016-08-24 09:33:19,481 DEBUG: 0.591527377522Poulet
+2016-08-24 09:33:19,481 DEBUG: 0.58288184438Poulet
+2016-08-24 09:33:19,481 DEBUG: 0.535273775216Poulet
+2016-08-24 09:33:19,481 DEBUG: 0.515619596542Poulet
+2016-08-24 09:33:19,481 DEBUG: 0.523804034582Poulet
+2016-08-24 09:33:19,481 DEBUG: 0.521556195965Poulet
+2016-08-24 09:33:19,482 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:33:19,482 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:33:21,403 DEBUG: 0.58553314121Poulet
+2016-08-24 09:33:21,403 DEBUG: 0.554178674352Poulet
+2016-08-24 09:33:21,403 DEBUG: 0.53734870317Poulet
+2016-08-24 09:33:21,403 DEBUG: 0.575792507205Poulet
+2016-08-24 09:33:21,403 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:33:21,404 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 09:33:38,038 DEBUG: 0.576253602305Poulet
+2016-08-24 09:33:38,038 DEBUG: 0.566109510086Poulet
+2016-08-24 09:33:38,038 DEBUG: 0.577925072046Poulet
+2016-08-24 09:33:38,039 DEBUG: 0.58144092219Poulet
+2016-08-24 09:33:38,039 DEBUG: 0.502305475504Poulet
+2016-08-24 09:33:38,039 DEBUG: 0.501613832853Poulet
+2016-08-24 09:33:38,039 DEBUG: 0.50818443804Poulet
+2016-08-24 09:33:38,039 DEBUG: 0.52795389049Poulet
+2016-08-24 09:33:38,039 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:33:38,040 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 09:33:39,777 DEBUG: 0.551296829971Poulet
+2016-08-24 09:33:39,777 DEBUG: 0.591008645533Poulet
+2016-08-24 09:33:39,777 DEBUG: 0.582478386167Poulet
+2016-08-24 09:33:39,777 DEBUG: 0.567838616715Poulet
+2016-08-24 09:33:39,777 DEBUG: 0.517002881844Poulet
+2016-08-24 09:33:39,777 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:33:39,778 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093355-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093355-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..2c779d8c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093355-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,14742 @@
+2016-08-24 09:33:55,099 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:33:55,100 INFO: Info: Labels used: No, Yes
+2016-08-24 09:33:55,100 INFO: Info: Length of dataset:347
+2016-08-24 09:33:55,101 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:33:55,101 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:33:55,102 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:33:55,102 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:33:55,103 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:33:55,103 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:33:55,103 INFO: Done: Read Database Files
+2016-08-24 09:33:55,103 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:33:55,107 INFO: Done: Determine validation split
+2016-08-24 09:33:55,107 INFO: Start: Determine 2 folds
+2016-08-24 09:33:55,116 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:33:55,116 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:33:55,116 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:33:55,116 INFO: Done: Determine folds
+2016-08-24 09:33:55,117 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:33:55,117 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:33:55,117 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:34:02,435 DEBUG: 0.591988472622Poulet
+2016-08-24 09:34:02,435 DEBUG: 0.58386167147Poulet
+2016-08-24 09:34:02,435 DEBUG: 0.517752161383Poulet
+2016-08-24 09:34:02,435 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:34:02,436 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:34:04,356 DEBUG: 0.533083573487Poulet
+2016-08-24 09:34:04,356 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:34:04,357 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 09:34:20,901 DEBUG: 0.582997118156Poulet
+2016-08-24 09:34:20,902 DEBUG: 0.549682997118Poulet
+2016-08-24 09:34:20,902 DEBUG: 0.503746397695Poulet
+2016-08-24 09:34:20,902 DEBUG: 0.50674351585Poulet
+2016-08-24 09:34:20,902 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:34:20,903 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 09:34:22,667 DEBUG: 0.567319884726Poulet
+2016-08-24 09:34:22,667 DEBUG: 0.554409221902Poulet
+2016-08-24 09:34:22,667 DEBUG: 0.504553314121Poulet
+2016-08-24 09:34:22,667 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:34:22,668 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 09:34:59,961 DEBUG: 0.553544668588Poulet
+2016-08-24 09:34:59,961 DEBUG: 0.549452449568Poulet
+2016-08-24 09:34:59,961 DEBUG: 0.53325648415Poulet
+2016-08-24 09:34:59,962 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:34:59,962 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 09:34:59,962 INFO: Start: Fold number 1
+2016-08-24 09:35:01,610 DEBUG: Start: Iteration 1
+2016-08-24 09:35:01,630 DEBUG: View 0 : 0.615384615385
+2016-08-24 09:35:01,638 DEBUG: View 1 : 0.615384615385
+2016-08-24 09:35:01,679 DEBUG: View 2 : 0.615384615385
+2016-08-24 09:35:01,687 DEBUG: View 3 : 0.615384615385
+2016-08-24 09:35:01,730 DEBUG: Best view : Methyl_
+2016-08-24 09:35:01,811 DEBUG: Start: Iteration 2
+2016-08-24 09:35:01,829 DEBUG: View 0 : 0.666666666667
+2016-08-24 09:35:01,838 DEBUG: View 1 : 0.564102564103
+2016-08-24 09:35:01,876 DEBUG: View 2 : 0.564102564103
+2016-08-24 09:35:01,884 DEBUG: View 3 : 0.5
+2016-08-24 09:35:01,931 DEBUG: Best view : Methyl_
+2016-08-24 09:35:02,070 DEBUG: Start: Iteration 3
+2016-08-24 09:35:02,086 DEBUG: View 0 : 0.461538461538
+2016-08-24 09:35:02,094 DEBUG: View 1 : 0.538461538462
+2016-08-24 09:35:02,130 DEBUG: View 2 : 0.512820512821
+2016-08-24 09:35:02,138 DEBUG: View 3 : 0.5
+2016-08-24 09:35:02,191 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:02,387 DEBUG: Start: Iteration 4
+2016-08-24 09:35:02,404 DEBUG: View 0 : 0.538461538462
+2016-08-24 09:35:02,412 DEBUG: View 1 : 0.525641025641
+2016-08-24 09:35:02,448 DEBUG: View 2 : 0.455128205128
+2016-08-24 09:35:02,456 DEBUG: View 3 : 0.512820512821
+2016-08-24 09:35:02,512 DEBUG: Best view : Methyl_
+2016-08-24 09:35:02,768 DEBUG: Start: Iteration 5
+2016-08-24 09:35:02,784 DEBUG: View 0 : 0.448717948718
+2016-08-24 09:35:02,792 DEBUG: View 1 : 0.647435897436
+2016-08-24 09:35:02,828 DEBUG: View 2 : 0.455128205128
+2016-08-24 09:35:02,836 DEBUG: View 3 : 0.461538461538
+2016-08-24 09:35:02,895 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:03,209 DEBUG: Start: Iteration 6
+2016-08-24 09:35:03,225 DEBUG: View 0 : 0.628205128205
+2016-08-24 09:35:03,233 DEBUG: View 1 : 0.679487179487
+2016-08-24 09:35:03,269 DEBUG: View 2 : 0.391025641026
+2016-08-24 09:35:03,277 DEBUG: View 3 : 0.544871794872
+2016-08-24 09:35:03,339 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:03,710 DEBUG: Start: Iteration 7
+2016-08-24 09:35:03,727 DEBUG: View 0 : 0.538461538462
+2016-08-24 09:35:03,734 DEBUG: View 1 : 0.512820512821
+2016-08-24 09:35:03,771 DEBUG: View 2 : 0.391025641026
+2016-08-24 09:35:03,778 DEBUG: View 3 : 0.5
+2016-08-24 09:35:03,842 DEBUG: Best view : Methyl_
+2016-08-24 09:35:04,273 DEBUG: Start: Iteration 8
+2016-08-24 09:35:04,290 DEBUG: View 0 : 0.442307692308
+2016-08-24 09:35:04,297 DEBUG: View 1 : 0.608974358974
+2016-08-24 09:35:04,334 DEBUG: View 2 : 0.474358974359
+2016-08-24 09:35:04,341 DEBUG: View 3 : 0.50641025641
+2016-08-24 09:35:04,407 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:04,895 DEBUG: Start: Iteration 9
+2016-08-24 09:35:04,911 DEBUG: View 0 : 0.435897435897
+2016-08-24 09:35:04,919 DEBUG: View 1 : 0.525641025641
+2016-08-24 09:35:04,957 DEBUG: View 2 : 0.602564102564
+2016-08-24 09:35:04,964 DEBUG: View 3 : 0.608974358974
+2016-08-24 09:35:05,033 DEBUG: Best view : Clinic_
+2016-08-24 09:35:05,579 DEBUG: Start: Iteration 10
+2016-08-24 09:35:05,595 DEBUG: View 0 : 0.596153846154
+2016-08-24 09:35:05,603 DEBUG: View 1 : 0.371794871795
+2016-08-24 09:35:05,639 DEBUG: View 2 : 0.532051282051
+2016-08-24 09:35:05,646 DEBUG: View 3 : 0.442307692308
+2016-08-24 09:35:05,717 DEBUG: Best view : Methyl_
+2016-08-24 09:35:06,322 DEBUG: Start: Iteration 11
+2016-08-24 09:35:06,338 DEBUG: View 0 : 0.474358974359
+2016-08-24 09:35:06,346 DEBUG: View 1 : 0.608974358974
+2016-08-24 09:35:06,382 DEBUG: View 2 : 0.608974358974
+2016-08-24 09:35:06,389 DEBUG: View 3 : 0.557692307692
+2016-08-24 09:35:06,462 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:07,123 DEBUG: Start: Iteration 12
+2016-08-24 09:35:07,140 DEBUG: View 0 : 0.5
+2016-08-24 09:35:07,147 DEBUG: View 1 : 0.391025641026
+2016-08-24 09:35:07,184 DEBUG: View 2 : 0.551282051282
+2016-08-24 09:35:07,192 DEBUG: View 3 : 0.50641025641
+2016-08-24 09:35:07,266 DEBUG: Best view : RANSeq_
+2016-08-24 09:35:08,000 DEBUG: Start: Iteration 13
+2016-08-24 09:35:08,016 DEBUG: View 0 : 0.519230769231
+2016-08-24 09:35:08,024 DEBUG: View 1 : 0.647435897436
+2016-08-24 09:35:08,060 DEBUG: View 2 : 0.557692307692
+2016-08-24 09:35:08,068 DEBUG: View 3 : 0.634615384615
+2016-08-24 09:35:08,144 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:08,935 DEBUG: Start: Iteration 14
+2016-08-24 09:35:08,951 DEBUG: View 0 : 0.461538461538
+2016-08-24 09:35:08,959 DEBUG: View 1 : 0.519230769231
+2016-08-24 09:35:08,995 DEBUG: View 2 : 0.474358974359
+2016-08-24 09:35:09,003 DEBUG: View 3 : 0.435897435897
+2016-08-24 09:35:09,080 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:09,927 DEBUG: Start: Iteration 15
+2016-08-24 09:35:09,943 DEBUG: View 0 : 0.442307692308
+2016-08-24 09:35:09,951 DEBUG: View 1 : 0.474358974359
+2016-08-24 09:35:09,987 DEBUG: View 2 : 0.589743589744
+2016-08-24 09:35:09,995 DEBUG: View 3 : 0.410256410256
+2016-08-24 09:35:10,074 DEBUG: Best view : RANSeq_
+2016-08-24 09:35:10,994 DEBUG: Start: Iteration 16
+2016-08-24 09:35:11,011 DEBUG: View 0 : 0.455128205128
+2016-08-24 09:35:11,019 DEBUG: View 1 : 0.628205128205
+2016-08-24 09:35:11,056 DEBUG: View 2 : 0.532051282051
+2016-08-24 09:35:11,064 DEBUG: View 3 : 0.50641025641
+2016-08-24 09:35:11,146 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:12,120 DEBUG: Start: Iteration 17
+2016-08-24 09:35:12,136 DEBUG: View 0 : 0.621794871795
+2016-08-24 09:35:12,144 DEBUG: View 1 : 0.551282051282
+2016-08-24 09:35:12,181 DEBUG: View 2 : 0.49358974359
+2016-08-24 09:35:12,188 DEBUG: View 3 : 0.455128205128
+2016-08-24 09:35:12,273 DEBUG: Best view : Methyl_
+2016-08-24 09:35:13,308 DEBUG: Start: Iteration 18
+2016-08-24 09:35:13,325 DEBUG: View 0 : 0.557692307692
+2016-08-24 09:35:13,332 DEBUG: View 1 : 0.608974358974
+2016-08-24 09:35:13,369 DEBUG: View 2 : 0.49358974359
+2016-08-24 09:35:13,376 DEBUG: View 3 : 0.512820512821
+2016-08-24 09:35:13,464 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:14,557 DEBUG: Start: Iteration 19
+2016-08-24 09:35:14,573 DEBUG: View 0 : 0.416666666667
+2016-08-24 09:35:14,581 DEBUG: View 1 : 0.403846153846
+2016-08-24 09:35:14,618 DEBUG: View 2 : 0.589743589744
+2016-08-24 09:35:14,626 DEBUG: View 3 : 0.403846153846
+2016-08-24 09:35:14,714 DEBUG: Best view : RANSeq_
+2016-08-24 09:35:15,878 DEBUG: Start: Iteration 20
+2016-08-24 09:35:15,895 DEBUG: View 0 : 0.467948717949
+2016-08-24 09:35:15,902 DEBUG: View 1 : 0.685897435897
+2016-08-24 09:35:15,939 DEBUG: View 2 : 0.628205128205
+2016-08-24 09:35:15,946 DEBUG: View 3 : 0.49358974359
+2016-08-24 09:35:16,038 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:17,259 DEBUG: Start: Iteration 21
+2016-08-24 09:35:17,275 DEBUG: View 0 : 0.564102564103
+2016-08-24 09:35:17,283 DEBUG: View 1 : 0.711538461538
+2016-08-24 09:35:17,319 DEBUG: View 2 : 0.429487179487
+2016-08-24 09:35:17,327 DEBUG: View 3 : 0.532051282051
+2016-08-24 09:35:17,419 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:18,697 DEBUG: Start: Iteration 22
+2016-08-24 09:35:18,713 DEBUG: View 0 : 0.538461538462
+2016-08-24 09:35:18,721 DEBUG: View 1 : 0.634615384615
+2016-08-24 09:35:18,757 DEBUG: View 2 : 0.525641025641
+2016-08-24 09:35:18,765 DEBUG: View 3 : 0.570512820513
+2016-08-24 09:35:18,860 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:20,195 DEBUG: Start: Iteration 23
+2016-08-24 09:35:20,212 DEBUG: View 0 : 0.525641025641
+2016-08-24 09:35:20,219 DEBUG: View 1 : 0.641025641026
+2016-08-24 09:35:20,256 DEBUG: View 2 : 0.551282051282
+2016-08-24 09:35:20,264 DEBUG: View 3 : 0.602564102564
+2016-08-24 09:35:20,360 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:21,760 DEBUG: Start: Iteration 24
+2016-08-24 09:35:21,776 DEBUG: View 0 : 0.762820512821
+2016-08-24 09:35:21,784 DEBUG: View 1 : 0.628205128205
+2016-08-24 09:35:21,820 DEBUG: View 2 : 0.371794871795
+2016-08-24 09:35:21,828 DEBUG: View 3 : 0.461538461538
+2016-08-24 09:35:21,926 DEBUG: Best view : Methyl_
+2016-08-24 09:35:23,376 DEBUG: Start: Iteration 25
+2016-08-24 09:35:23,392 DEBUG: View 0 : 0.487179487179
+2016-08-24 09:35:23,400 DEBUG: View 1 : 0.391025641026
+2016-08-24 09:35:23,436 DEBUG: View 2 : 0.487179487179
+2016-08-24 09:35:23,444 DEBUG: View 3 : 0.403846153846
+2016-08-24 09:35:23,444 WARNING: All bad for iteration 24
+2016-08-24 09:35:23,545 DEBUG: Best view : Methyl_
+2016-08-24 09:35:25,059 DEBUG: Start: Iteration 26
+2016-08-24 09:35:25,076 DEBUG: View 0 : 0.602564102564
+2016-08-24 09:35:25,084 DEBUG: View 1 : 0.608974358974
+2016-08-24 09:35:25,121 DEBUG: View 2 : 0.5
+2016-08-24 09:35:25,129 DEBUG: View 3 : 0.467948717949
+2016-08-24 09:35:25,232 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:26,805 DEBUG: Start: Iteration 27
+2016-08-24 09:35:26,821 DEBUG: View 0 : 0.544871794872
+2016-08-24 09:35:26,829 DEBUG: View 1 : 0.416666666667
+2016-08-24 09:35:26,866 DEBUG: View 2 : 0.557692307692
+2016-08-24 09:35:26,873 DEBUG: View 3 : 0.570512820513
+2016-08-24 09:35:26,978 DEBUG: Best view : Clinic_
+2016-08-24 09:35:28,607 DEBUG: Start: Iteration 28
+2016-08-24 09:35:28,624 DEBUG: View 0 : 0.608974358974
+2016-08-24 09:35:28,632 DEBUG: View 1 : 0.519230769231
+2016-08-24 09:35:28,668 DEBUG: View 2 : 0.467948717949
+2016-08-24 09:35:28,676 DEBUG: View 3 : 0.50641025641
+2016-08-24 09:35:28,783 DEBUG: Best view : Methyl_
+2016-08-24 09:35:30,474 DEBUG: Start: Iteration 29
+2016-08-24 09:35:30,490 DEBUG: View 0 : 0.602564102564
+2016-08-24 09:35:30,498 DEBUG: View 1 : 0.480769230769
+2016-08-24 09:35:30,534 DEBUG: View 2 : 0.474358974359
+2016-08-24 09:35:30,541 DEBUG: View 3 : 0.589743589744
+2016-08-24 09:35:30,651 DEBUG: Best view : Methyl_
+2016-08-24 09:35:32,412 DEBUG: Start: Iteration 30
+2016-08-24 09:35:32,428 DEBUG: View 0 : 0.455128205128
+2016-08-24 09:35:32,436 DEBUG: View 1 : 0.429487179487
+2016-08-24 09:35:32,472 DEBUG: View 2 : 0.423076923077
+2016-08-24 09:35:32,479 DEBUG: View 3 : 0.647435897436
+2016-08-24 09:35:32,591 DEBUG: Best view : Clinic_
+2016-08-24 09:35:34,461 DEBUG: Start: Iteration 31
+2016-08-24 09:35:34,480 DEBUG: View 0 : 0.576923076923
+2016-08-24 09:35:34,488 DEBUG: View 1 : 0.615384615385
+2016-08-24 09:35:34,526 DEBUG: View 2 : 0.448717948718
+2016-08-24 09:35:34,533 DEBUG: View 3 : 0.442307692308
+2016-08-24 09:35:34,653 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:36,567 DEBUG: Start: Iteration 32
+2016-08-24 09:35:36,583 DEBUG: View 0 : 0.602564102564
+2016-08-24 09:35:36,591 DEBUG: View 1 : 0.647435897436
+2016-08-24 09:35:36,628 DEBUG: View 2 : 0.519230769231
+2016-08-24 09:35:36,635 DEBUG: View 3 : 0.602564102564
+2016-08-24 09:35:36,760 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:38,858 DEBUG: Start: Iteration 33
+2016-08-24 09:35:38,875 DEBUG: View 0 : 0.50641025641
+2016-08-24 09:35:38,883 DEBUG: View 1 : 0.371794871795
+2016-08-24 09:35:38,920 DEBUG: View 2 : 0.403846153846
+2016-08-24 09:35:38,928 DEBUG: View 3 : 0.641025641026
+2016-08-24 09:35:39,047 DEBUG: Best view : Clinic_
+2016-08-24 09:35:41,049 DEBUG: Start: Iteration 34
+2016-08-24 09:35:41,066 DEBUG: View 0 : 0.596153846154
+2016-08-24 09:35:41,074 DEBUG: View 1 : 0.641025641026
+2016-08-24 09:35:41,110 DEBUG: View 2 : 0.448717948718
+2016-08-24 09:35:41,118 DEBUG: View 3 : 0.397435897436
+2016-08-24 09:35:41,241 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:43,293 DEBUG: Start: Iteration 35
+2016-08-24 09:35:43,310 DEBUG: View 0 : 0.5
+2016-08-24 09:35:43,317 DEBUG: View 1 : 0.666666666667
+2016-08-24 09:35:43,354 DEBUG: View 2 : 0.557692307692
+2016-08-24 09:35:43,361 DEBUG: View 3 : 0.576923076923
+2016-08-24 09:35:43,483 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:45,576 DEBUG: Start: Iteration 36
+2016-08-24 09:35:45,593 DEBUG: View 0 : 0.435897435897
+2016-08-24 09:35:45,600 DEBUG: View 1 : 0.358974358974
+2016-08-24 09:35:45,636 DEBUG: View 2 : 0.487179487179
+2016-08-24 09:35:45,644 DEBUG: View 3 : 0.570512820513
+2016-08-24 09:35:45,768 DEBUG: Best view : Clinic_
+2016-08-24 09:35:47,921 DEBUG: Start: Iteration 37
+2016-08-24 09:35:47,937 DEBUG: View 0 : 0.583333333333
+2016-08-24 09:35:47,945 DEBUG: View 1 : 0.647435897436
+2016-08-24 09:35:47,981 DEBUG: View 2 : 0.583333333333
+2016-08-24 09:35:47,989 DEBUG: View 3 : 0.557692307692
+2016-08-24 09:35:48,116 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:50,347 DEBUG: Start: Iteration 38
+2016-08-24 09:35:50,364 DEBUG: View 0 : 0.423076923077
+2016-08-24 09:35:50,372 DEBUG: View 1 : 0.685897435897
+2016-08-24 09:35:50,409 DEBUG: View 2 : 0.50641025641
+2016-08-24 09:35:50,416 DEBUG: View 3 : 0.410256410256
+2016-08-24 09:35:50,547 DEBUG: Best view : MiRNA__
+2016-08-24 09:35:52,847 DEBUG: Start: Iteration 39
+2016-08-24 09:35:52,863 DEBUG: View 0 : 0.423076923077
+2016-08-24 09:35:52,871 DEBUG: View 1 : 0.532051282051
+2016-08-24 09:35:52,909 DEBUG: View 2 : 0.596153846154
+2016-08-24 09:35:52,917 DEBUG: View 3 : 0.621794871795
+2016-08-24 09:35:53,050 DEBUG: Best view : Clinic_
+2016-08-24 09:35:55,388 DEBUG: Start: Iteration 40
+2016-08-24 09:35:55,405 DEBUG: View 0 : 0.628205128205
+2016-08-24 09:35:55,413 DEBUG: View 1 : 0.474358974359
+2016-08-24 09:35:55,450 DEBUG: View 2 : 0.474358974359
+2016-08-24 09:35:55,459 DEBUG: View 3 : 0.448717948718
+2016-08-24 09:35:55,654 DEBUG: Best view : Methyl_
+2016-08-24 09:35:58,192 DEBUG: Start: Iteration 41
+2016-08-24 09:35:58,210 DEBUG: View 0 : 0.615384615385
+2016-08-24 09:35:58,219 DEBUG: View 1 : 0.628205128205
+2016-08-24 09:35:58,261 DEBUG: View 2 : 0.570512820513
+2016-08-24 09:35:58,270 DEBUG: View 3 : 0.435897435897
+2016-08-24 09:35:58,469 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:01,082 DEBUG: Start: Iteration 42
+2016-08-24 09:36:01,099 DEBUG: View 0 : 0.705128205128
+2016-08-24 09:36:01,107 DEBUG: View 1 : 0.647435897436
+2016-08-24 09:36:01,144 DEBUG: View 2 : 0.435897435897
+2016-08-24 09:36:01,152 DEBUG: View 3 : 0.602564102564
+2016-08-24 09:36:01,298 DEBUG: Best view : Methyl_
+2016-08-24 09:36:04,147 DEBUG: Start: Iteration 43
+2016-08-24 09:36:04,173 DEBUG: View 0 : 0.448717948718
+2016-08-24 09:36:04,182 DEBUG: View 1 : 0.416666666667
+2016-08-24 09:36:04,224 DEBUG: View 2 : 0.480769230769
+2016-08-24 09:36:04,236 DEBUG: View 3 : 0.551282051282
+2016-08-24 09:36:04,378 DEBUG: Best view : Clinic_
+2016-08-24 09:36:07,254 DEBUG: Start: Iteration 44
+2016-08-24 09:36:07,271 DEBUG: View 0 : 0.564102564103
+2016-08-24 09:36:07,278 DEBUG: View 1 : 0.660256410256
+2016-08-24 09:36:07,315 DEBUG: View 2 : 0.557692307692
+2016-08-24 09:36:07,322 DEBUG: View 3 : 0.557692307692
+2016-08-24 09:36:07,468 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:10,373 DEBUG: Start: Iteration 45
+2016-08-24 09:36:10,391 DEBUG: View 0 : 0.371794871795
+2016-08-24 09:36:10,400 DEBUG: View 1 : 0.673076923077
+2016-08-24 09:36:10,447 DEBUG: View 2 : 0.538461538462
+2016-08-24 09:36:10,456 DEBUG: View 3 : 0.487179487179
+2016-08-24 09:36:10,702 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:13,728 DEBUG: Start: Iteration 46
+2016-08-24 09:36:13,744 DEBUG: View 0 : 0.532051282051
+2016-08-24 09:36:13,752 DEBUG: View 1 : 0.576923076923
+2016-08-24 09:36:13,789 DEBUG: View 2 : 0.487179487179
+2016-08-24 09:36:13,797 DEBUG: View 3 : 0.628205128205
+2016-08-24 09:36:13,944 DEBUG: Best view : Clinic_
+2016-08-24 09:36:16,698 DEBUG: Start: Iteration 47
+2016-08-24 09:36:16,714 DEBUG: View 0 : 0.410256410256
+2016-08-24 09:36:16,722 DEBUG: View 1 : 0.532051282051
+2016-08-24 09:36:16,759 DEBUG: View 2 : 0.487179487179
+2016-08-24 09:36:16,767 DEBUG: View 3 : 0.448717948718
+2016-08-24 09:36:16,916 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:19,727 DEBUG: Start: Iteration 48
+2016-08-24 09:36:19,743 DEBUG: View 0 : 0.423076923077
+2016-08-24 09:36:19,751 DEBUG: View 1 : 0.615384615385
+2016-08-24 09:36:19,787 DEBUG: View 2 : 0.532051282051
+2016-08-24 09:36:19,794 DEBUG: View 3 : 0.583333333333
+2016-08-24 09:36:19,945 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:22,795 DEBUG: Start: Iteration 49
+2016-08-24 09:36:22,811 DEBUG: View 0 : 0.487179487179
+2016-08-24 09:36:22,819 DEBUG: View 1 : 0.608974358974
+2016-08-24 09:36:22,855 DEBUG: View 2 : 0.378205128205
+2016-08-24 09:36:22,863 DEBUG: View 3 : 0.596153846154
+2016-08-24 09:36:23,015 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:25,918 DEBUG: Start: Iteration 50
+2016-08-24 09:36:25,934 DEBUG: View 0 : 0.423076923077
+2016-08-24 09:36:25,942 DEBUG: View 1 : 0.365384615385
+2016-08-24 09:36:25,979 DEBUG: View 2 : 0.570512820513
+2016-08-24 09:36:25,987 DEBUG: View 3 : 0.544871794872
+2016-08-24 09:36:26,144 DEBUG: Best view : RANSeq_
+2016-08-24 09:36:29,125 DEBUG: Start: Iteration 51
+2016-08-24 09:36:29,141 DEBUG: View 0 : 0.602564102564
+2016-08-24 09:36:29,149 DEBUG: View 1 : 0.653846153846
+2016-08-24 09:36:29,185 DEBUG: View 2 : 0.397435897436
+2016-08-24 09:36:29,193 DEBUG: View 3 : 0.615384615385
+2016-08-24 09:36:29,350 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:32,554 DEBUG: Start: Iteration 52
+2016-08-24 09:36:32,572 DEBUG: View 0 : 0.621794871795
+2016-08-24 09:36:32,580 DEBUG: View 1 : 0.692307692308
+2016-08-24 09:36:32,617 DEBUG: View 2 : 0.435897435897
+2016-08-24 09:36:32,625 DEBUG: View 3 : 0.608974358974
+2016-08-24 09:36:32,784 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:35,871 INFO: Start: Classification
+2016-08-24 09:36:43,411 INFO: Done: Fold number 1
+2016-08-24 09:36:43,411 INFO: Start: Fold number 2
+2016-08-24 09:36:44,984 DEBUG: Start: Iteration 1
+2016-08-24 09:36:44,998 DEBUG: View 0 : 0.382165605096
+2016-08-24 09:36:45,006 DEBUG: View 1 : 0.617834394904
+2016-08-24 09:36:45,042 DEBUG: View 2 : 0.369426751592
+2016-08-24 09:36:45,049 DEBUG: View 3 : 0.617834394904
+2016-08-24 09:36:45,089 DEBUG: Best view : Methyl_
+2016-08-24 09:36:45,164 DEBUG: Start: Iteration 2
+2016-08-24 09:36:45,180 DEBUG: View 0 : 0.43949044586
+2016-08-24 09:36:45,188 DEBUG: View 1 : 0.649681528662
+2016-08-24 09:36:45,224 DEBUG: View 2 : 0.541401273885
+2016-08-24 09:36:45,231 DEBUG: View 3 : 0.509554140127
+2016-08-24 09:36:45,276 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:45,408 DEBUG: Start: Iteration 3
+2016-08-24 09:36:45,424 DEBUG: View 0 : 0.420382165605
+2016-08-24 09:36:45,432 DEBUG: View 1 : 0.630573248408
+2016-08-24 09:36:45,467 DEBUG: View 2 : 0.535031847134
+2016-08-24 09:36:45,475 DEBUG: View 3 : 0.388535031847
+2016-08-24 09:36:45,527 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:45,717 DEBUG: Start: Iteration 4
+2016-08-24 09:36:45,733 DEBUG: View 0 : 0.375796178344
+2016-08-24 09:36:45,741 DEBUG: View 1 : 0.573248407643
+2016-08-24 09:36:45,777 DEBUG: View 2 : 0.490445859873
+2016-08-24 09:36:45,784 DEBUG: View 3 : 0.503184713376
+2016-08-24 09:36:45,839 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:46,086 DEBUG: Start: Iteration 5
+2016-08-24 09:36:46,103 DEBUG: View 0 : 0.566878980892
+2016-08-24 09:36:46,110 DEBUG: View 1 : 0.585987261146
+2016-08-24 09:36:46,146 DEBUG: View 2 : 0.566878980892
+2016-08-24 09:36:46,154 DEBUG: View 3 : 0.579617834395
+2016-08-24 09:36:46,210 DEBUG: Best view : Clinic_
+2016-08-24 09:36:46,514 DEBUG: Start: Iteration 6
+2016-08-24 09:36:46,530 DEBUG: View 0 : 0.484076433121
+2016-08-24 09:36:46,538 DEBUG: View 1 : 0.496815286624
+2016-08-24 09:36:46,574 DEBUG: View 2 : 0.56050955414
+2016-08-24 09:36:46,581 DEBUG: View 3 : 0.445859872611
+2016-08-24 09:36:46,640 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:47,001 DEBUG: Start: Iteration 7
+2016-08-24 09:36:47,018 DEBUG: View 0 : 0.541401273885
+2016-08-24 09:36:47,025 DEBUG: View 1 : 0.624203821656
+2016-08-24 09:36:47,061 DEBUG: View 2 : 0.43949044586
+2016-08-24 09:36:47,069 DEBUG: View 3 : 0.375796178344
+2016-08-24 09:36:47,129 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:47,548 DEBUG: Start: Iteration 8
+2016-08-24 09:36:47,565 DEBUG: View 0 : 0.643312101911
+2016-08-24 09:36:47,572 DEBUG: View 1 : 0.312101910828
+2016-08-24 09:36:47,608 DEBUG: View 2 : 0.420382165605
+2016-08-24 09:36:47,615 DEBUG: View 3 : 0.541401273885
+2016-08-24 09:36:47,678 DEBUG: Best view : Methyl_
+2016-08-24 09:36:48,158 DEBUG: Start: Iteration 9
+2016-08-24 09:36:48,174 DEBUG: View 0 : 0.458598726115
+2016-08-24 09:36:48,182 DEBUG: View 1 : 0.407643312102
+2016-08-24 09:36:48,217 DEBUG: View 2 : 0.445859872611
+2016-08-24 09:36:48,224 DEBUG: View 3 : 0.458598726115
+2016-08-24 09:36:48,225 WARNING: All bad for iteration 8
+2016-08-24 09:36:48,290 DEBUG: Best view : Clinic_
+2016-08-24 09:36:48,826 DEBUG: Start: Iteration 10
+2016-08-24 09:36:48,843 DEBUG: View 0 : 0.547770700637
+2016-08-24 09:36:48,851 DEBUG: View 1 : 0.566878980892
+2016-08-24 09:36:48,887 DEBUG: View 2 : 0.407643312102
+2016-08-24 09:36:48,894 DEBUG: View 3 : 0.43949044586
+2016-08-24 09:36:48,962 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:49,554 DEBUG: Start: Iteration 11
+2016-08-24 09:36:49,571 DEBUG: View 0 : 0.535031847134
+2016-08-24 09:36:49,579 DEBUG: View 1 : 0.433121019108
+2016-08-24 09:36:49,614 DEBUG: View 2 : 0.484076433121
+2016-08-24 09:36:49,622 DEBUG: View 3 : 0.420382165605
+2016-08-24 09:36:49,691 DEBUG: Best view : RANSeq_
+2016-08-24 09:36:50,358 DEBUG: Start: Iteration 12
+2016-08-24 09:36:50,374 DEBUG: View 0 : 0.770700636943
+2016-08-24 09:36:50,382 DEBUG: View 1 : 0.56050955414
+2016-08-24 09:36:50,418 DEBUG: View 2 : 0.426751592357
+2016-08-24 09:36:50,425 DEBUG: View 3 : 0.433121019108
+2016-08-24 09:36:50,498 DEBUG: Best view : Methyl_
+2016-08-24 09:36:51,225 DEBUG: Start: Iteration 13
+2016-08-24 09:36:51,242 DEBUG: View 0 : 0.420382165605
+2016-08-24 09:36:51,249 DEBUG: View 1 : 0.343949044586
+2016-08-24 09:36:51,285 DEBUG: View 2 : 0.573248407643
+2016-08-24 09:36:51,293 DEBUG: View 3 : 0.394904458599
+2016-08-24 09:36:51,367 DEBUG: Best view : RANSeq_
+2016-08-24 09:36:52,162 DEBUG: Start: Iteration 14
+2016-08-24 09:36:52,179 DEBUG: View 0 : 0.445859872611
+2016-08-24 09:36:52,187 DEBUG: View 1 : 0.566878980892
+2016-08-24 09:36:52,223 DEBUG: View 2 : 0.579617834395
+2016-08-24 09:36:52,230 DEBUG: View 3 : 0.541401273885
+2016-08-24 09:36:52,308 DEBUG: Best view : RANSeq_
+2016-08-24 09:36:53,175 DEBUG: Start: Iteration 15
+2016-08-24 09:36:53,191 DEBUG: View 0 : 0.624203821656
+2016-08-24 09:36:53,199 DEBUG: View 1 : 0.656050955414
+2016-08-24 09:36:53,235 DEBUG: View 2 : 0.592356687898
+2016-08-24 09:36:53,242 DEBUG: View 3 : 0.503184713376
+2016-08-24 09:36:53,321 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:54,247 DEBUG: Start: Iteration 16
+2016-08-24 09:36:54,263 DEBUG: View 0 : 0.636942675159
+2016-08-24 09:36:54,271 DEBUG: View 1 : 0.592356687898
+2016-08-24 09:36:54,307 DEBUG: View 2 : 0.554140127389
+2016-08-24 09:36:54,314 DEBUG: View 3 : 0.484076433121
+2016-08-24 09:36:54,396 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:55,378 DEBUG: Start: Iteration 17
+2016-08-24 09:36:55,395 DEBUG: View 0 : 0.458598726115
+2016-08-24 09:36:55,402 DEBUG: View 1 : 0.624203821656
+2016-08-24 09:36:55,439 DEBUG: View 2 : 0.426751592357
+2016-08-24 09:36:55,446 DEBUG: View 3 : 0.414012738854
+2016-08-24 09:36:55,529 DEBUG: Best view : MiRNA__
+2016-08-24 09:36:56,570 DEBUG: Start: Iteration 18
+2016-08-24 09:36:56,586 DEBUG: View 0 : 0.535031847134
+2016-08-24 09:36:56,594 DEBUG: View 1 : 0.343949044586
+2016-08-24 09:36:56,630 DEBUG: View 2 : 0.605095541401
+2016-08-24 09:36:56,638 DEBUG: View 3 : 0.496815286624
+2016-08-24 09:36:56,724 DEBUG: Best view : RANSeq_
+2016-08-24 09:36:57,833 DEBUG: Start: Iteration 19
+2016-08-24 09:36:57,850 DEBUG: View 0 : 0.426751592357
+2016-08-24 09:36:57,857 DEBUG: View 1 : 0.414012738854
+2016-08-24 09:36:57,893 DEBUG: View 2 : 0.433121019108
+2016-08-24 09:36:57,901 DEBUG: View 3 : 0.56050955414
+2016-08-24 09:36:57,989 DEBUG: Best view : Clinic_
+2016-08-24 09:36:59,153 DEBUG: Start: Iteration 20
+2016-08-24 09:36:59,170 DEBUG: View 0 : 0.40127388535
+2016-08-24 09:36:59,178 DEBUG: View 1 : 0.630573248408
+2016-08-24 09:36:59,214 DEBUG: View 2 : 0.477707006369
+2016-08-24 09:36:59,221 DEBUG: View 3 : 0.624203821656
+2016-08-24 09:36:59,312 DEBUG: Best view : Clinic_
+2016-08-24 09:37:00,535 DEBUG: Start: Iteration 21
+2016-08-24 09:37:00,552 DEBUG: View 0 : 0.566878980892
+2016-08-24 09:37:00,559 DEBUG: View 1 : 0.541401273885
+2016-08-24 09:37:00,595 DEBUG: View 2 : 0.509554140127
+2016-08-24 09:37:00,602 DEBUG: View 3 : 0.515923566879
+2016-08-24 09:37:00,695 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:01,976 DEBUG: Start: Iteration 22
+2016-08-24 09:37:01,992 DEBUG: View 0 : 0.452229299363
+2016-08-24 09:37:02,000 DEBUG: View 1 : 0.566878980892
+2016-08-24 09:37:02,036 DEBUG: View 2 : 0.433121019108
+2016-08-24 09:37:02,043 DEBUG: View 3 : 0.484076433121
+2016-08-24 09:37:02,137 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:03,474 DEBUG: Start: Iteration 23
+2016-08-24 09:37:03,490 DEBUG: View 0 : 0.573248407643
+2016-08-24 09:37:03,498 DEBUG: View 1 : 0.624203821656
+2016-08-24 09:37:03,534 DEBUG: View 2 : 0.503184713376
+2016-08-24 09:37:03,541 DEBUG: View 3 : 0.394904458599
+2016-08-24 09:37:03,638 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:05,032 DEBUG: Start: Iteration 24
+2016-08-24 09:37:05,048 DEBUG: View 0 : 0.605095541401
+2016-08-24 09:37:05,056 DEBUG: View 1 : 0.643312101911
+2016-08-24 09:37:05,092 DEBUG: View 2 : 0.630573248408
+2016-08-24 09:37:05,099 DEBUG: View 3 : 0.541401273885
+2016-08-24 09:37:05,197 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:06,651 DEBUG: Start: Iteration 25
+2016-08-24 09:37:06,668 DEBUG: View 0 : 0.43949044586
+2016-08-24 09:37:06,675 DEBUG: View 1 : 0.350318471338
+2016-08-24 09:37:06,711 DEBUG: View 2 : 0.414012738854
+2016-08-24 09:37:06,719 DEBUG: View 3 : 0.611464968153
+2016-08-24 09:37:06,819 DEBUG: Best view : Clinic_
+2016-08-24 09:37:08,325 DEBUG: Start: Iteration 26
+2016-08-24 09:37:08,342 DEBUG: View 0 : 0.43949044586
+2016-08-24 09:37:08,349 DEBUG: View 1 : 0.675159235669
+2016-08-24 09:37:08,385 DEBUG: View 2 : 0.585987261146
+2016-08-24 09:37:08,393 DEBUG: View 3 : 0.573248407643
+2016-08-24 09:37:08,496 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:10,064 DEBUG: Start: Iteration 27
+2016-08-24 09:37:10,080 DEBUG: View 0 : 0.433121019108
+2016-08-24 09:37:10,088 DEBUG: View 1 : 0.496815286624
+2016-08-24 09:37:10,124 DEBUG: View 2 : 0.496815286624
+2016-08-24 09:37:10,131 DEBUG: View 3 : 0.515923566879
+2016-08-24 09:37:10,237 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:11,867 DEBUG: Start: Iteration 28
+2016-08-24 09:37:11,883 DEBUG: View 0 : 0.522292993631
+2016-08-24 09:37:11,891 DEBUG: View 1 : 0.464968152866
+2016-08-24 09:37:11,927 DEBUG: View 2 : 0.452229299363
+2016-08-24 09:37:11,935 DEBUG: View 3 : 0.420382165605
+2016-08-24 09:37:12,042 DEBUG: Best view : Methyl_
+2016-08-24 09:37:13,729 DEBUG: Start: Iteration 29
+2016-08-24 09:37:13,746 DEBUG: View 0 : 0.764331210191
+2016-08-24 09:37:13,753 DEBUG: View 1 : 0.40127388535
+2016-08-24 09:37:13,789 DEBUG: View 2 : 0.40127388535
+2016-08-24 09:37:13,797 DEBUG: View 3 : 0.566878980892
+2016-08-24 09:37:13,907 DEBUG: Best view : Methyl_
+2016-08-24 09:37:15,653 DEBUG: Start: Iteration 30
+2016-08-24 09:37:15,669 DEBUG: View 0 : 0.522292993631
+2016-08-24 09:37:15,677 DEBUG: View 1 : 0.649681528662
+2016-08-24 09:37:15,713 DEBUG: View 2 : 0.426751592357
+2016-08-24 09:37:15,721 DEBUG: View 3 : 0.59872611465
+2016-08-24 09:37:15,834 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:17,639 DEBUG: Start: Iteration 31
+2016-08-24 09:37:17,655 DEBUG: View 0 : 0.605095541401
+2016-08-24 09:37:17,663 DEBUG: View 1 : 0.477707006369
+2016-08-24 09:37:17,699 DEBUG: View 2 : 0.388535031847
+2016-08-24 09:37:17,706 DEBUG: View 3 : 0.605095541401
+2016-08-24 09:37:17,820 DEBUG: Best view : Methyl_
+2016-08-24 09:37:19,683 DEBUG: Start: Iteration 32
+2016-08-24 09:37:19,699 DEBUG: View 0 : 0.579617834395
+2016-08-24 09:37:19,707 DEBUG: View 1 : 0.445859872611
+2016-08-24 09:37:19,743 DEBUG: View 2 : 0.464968152866
+2016-08-24 09:37:19,751 DEBUG: View 3 : 0.458598726115
+2016-08-24 09:37:19,868 DEBUG: Best view : Methyl_
+2016-08-24 09:37:21,795 DEBUG: Start: Iteration 33
+2016-08-24 09:37:21,811 DEBUG: View 0 : 0.630573248408
+2016-08-24 09:37:21,819 DEBUG: View 1 : 0.656050955414
+2016-08-24 09:37:21,855 DEBUG: View 2 : 0.617834394904
+2016-08-24 09:37:21,866 DEBUG: View 3 : 0.40127388535
+2016-08-24 09:37:21,984 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:23,972 DEBUG: Start: Iteration 34
+2016-08-24 09:37:23,989 DEBUG: View 0 : 0.585987261146
+2016-08-24 09:37:23,996 DEBUG: View 1 : 0.624203821656
+2016-08-24 09:37:24,033 DEBUG: View 2 : 0.56050955414
+2016-08-24 09:37:24,044 DEBUG: View 3 : 0.554140127389
+2016-08-24 09:37:24,164 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:26,207 DEBUG: Start: Iteration 35
+2016-08-24 09:37:26,223 DEBUG: View 0 : 0.515923566879
+2016-08-24 09:37:26,231 DEBUG: View 1 : 0.68152866242
+2016-08-24 09:37:26,267 DEBUG: View 2 : 0.369426751592
+2016-08-24 09:37:26,275 DEBUG: View 3 : 0.452229299363
+2016-08-24 09:37:26,397 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:28,497 DEBUG: Start: Iteration 36
+2016-08-24 09:37:28,513 DEBUG: View 0 : 0.503184713376
+2016-08-24 09:37:28,521 DEBUG: View 1 : 0.458598726115
+2016-08-24 09:37:28,557 DEBUG: View 2 : 0.528662420382
+2016-08-24 09:37:28,564 DEBUG: View 3 : 0.624203821656
+2016-08-24 09:37:28,689 DEBUG: Best view : Clinic_
+2016-08-24 09:37:30,849 DEBUG: Start: Iteration 37
+2016-08-24 09:37:30,866 DEBUG: View 0 : 0.630573248408
+2016-08-24 09:37:30,873 DEBUG: View 1 : 0.662420382166
+2016-08-24 09:37:30,910 DEBUG: View 2 : 0.414012738854
+2016-08-24 09:37:30,917 DEBUG: View 3 : 0.43949044586
+2016-08-24 09:37:31,044 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:33,257 DEBUG: Start: Iteration 38
+2016-08-24 09:37:33,273 DEBUG: View 0 : 0.414012738854
+2016-08-24 09:37:33,281 DEBUG: View 1 : 0.414012738854
+2016-08-24 09:37:33,316 DEBUG: View 2 : 0.471337579618
+2016-08-24 09:37:33,324 DEBUG: View 3 : 0.503184713376
+2016-08-24 09:37:33,453 DEBUG: Best view : Clinic_
+2016-08-24 09:37:35,720 DEBUG: Start: Iteration 39
+2016-08-24 09:37:35,736 DEBUG: View 0 : 0.375796178344
+2016-08-24 09:37:35,744 DEBUG: View 1 : 0.471337579618
+2016-08-24 09:37:35,780 DEBUG: View 2 : 0.585987261146
+2016-08-24 09:37:35,787 DEBUG: View 3 : 0.547770700637
+2016-08-24 09:37:35,923 DEBUG: Best view : RANSeq_
+2016-08-24 09:37:38,261 DEBUG: Start: Iteration 40
+2016-08-24 09:37:38,278 DEBUG: View 0 : 0.554140127389
+2016-08-24 09:37:38,286 DEBUG: View 1 : 0.528662420382
+2016-08-24 09:37:38,322 DEBUG: View 2 : 0.43949044586
+2016-08-24 09:37:38,329 DEBUG: View 3 : 0.515923566879
+2016-08-24 09:37:38,463 DEBUG: Best view : Methyl_
+2016-08-24 09:37:40,864 DEBUG: Start: Iteration 41
+2016-08-24 09:37:40,880 DEBUG: View 0 : 0.611464968153
+2016-08-24 09:37:40,888 DEBUG: View 1 : 0.675159235669
+2016-08-24 09:37:40,924 DEBUG: View 2 : 0.535031847134
+2016-08-24 09:37:40,931 DEBUG: View 3 : 0.471337579618
+2016-08-24 09:37:41,067 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:43,527 DEBUG: Start: Iteration 42
+2016-08-24 09:37:43,544 DEBUG: View 0 : 0.656050955414
+2016-08-24 09:37:43,552 DEBUG: View 1 : 0.522292993631
+2016-08-24 09:37:43,587 DEBUG: View 2 : 0.554140127389
+2016-08-24 09:37:43,595 DEBUG: View 3 : 0.617834394904
+2016-08-24 09:37:43,733 DEBUG: Best view : Methyl_
+2016-08-24 09:37:46,249 DEBUG: Start: Iteration 43
+2016-08-24 09:37:46,266 DEBUG: View 0 : 0.573248407643
+2016-08-24 09:37:46,274 DEBUG: View 1 : 0.31847133758
+2016-08-24 09:37:46,310 DEBUG: View 2 : 0.445859872611
+2016-08-24 09:37:46,317 DEBUG: View 3 : 0.573248407643
+2016-08-24 09:37:46,457 DEBUG: Best view : Methyl_
+2016-08-24 09:37:49,037 DEBUG: Start: Iteration 44
+2016-08-24 09:37:49,053 DEBUG: View 0 : 0.662420382166
+2016-08-24 09:37:49,061 DEBUG: View 1 : 0.656050955414
+2016-08-24 09:37:49,097 DEBUG: View 2 : 0.509554140127
+2016-08-24 09:37:49,104 DEBUG: View 3 : 0.433121019108
+2016-08-24 09:37:49,246 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:51,883 DEBUG: Start: Iteration 45
+2016-08-24 09:37:51,900 DEBUG: View 0 : 0.630573248408
+2016-08-24 09:37:51,908 DEBUG: View 1 : 0.649681528662
+2016-08-24 09:37:51,943 DEBUG: View 2 : 0.452229299363
+2016-08-24 09:37:51,951 DEBUG: View 3 : 0.566878980892
+2016-08-24 09:37:52,096 DEBUG: Best view : MiRNA__
+2016-08-24 09:37:54,788 DEBUG: Start: Iteration 46
+2016-08-24 09:37:54,804 DEBUG: View 0 : 0.426751592357
+2016-08-24 09:37:54,812 DEBUG: View 1 : 0.471337579618
+2016-08-24 09:37:54,848 DEBUG: View 2 : 0.509554140127
+2016-08-24 09:37:54,855 DEBUG: View 3 : 0.585987261146
+2016-08-24 09:37:55,001 DEBUG: Best view : Clinic_
+2016-08-24 09:37:57,748 DEBUG: Start: Iteration 47
+2016-08-24 09:37:57,764 DEBUG: View 0 : 0.503184713376
+2016-08-24 09:37:57,772 DEBUG: View 1 : 0.40127388535
+2016-08-24 09:37:57,808 DEBUG: View 2 : 0.464968152866
+2016-08-24 09:37:57,815 DEBUG: View 3 : 0.43949044586
+2016-08-24 09:37:57,963 DEBUG: Best view : Methyl_
+2016-08-24 09:38:00,821 DEBUG: Start: Iteration 48
+2016-08-24 09:38:00,837 DEBUG: View 0 : 0.687898089172
+2016-08-24 09:38:00,845 DEBUG: View 1 : 0.726114649682
+2016-08-24 09:38:00,883 DEBUG: View 2 : 0.503184713376
+2016-08-24 09:38:00,891 DEBUG: View 3 : 0.630573248408
+2016-08-24 09:38:01,052 DEBUG: Best view : MiRNA__
+2016-08-24 09:38:04,075 DEBUG: Start: Iteration 49
+2016-08-24 09:38:04,092 DEBUG: View 0 : 0.496815286624
+2016-08-24 09:38:04,099 DEBUG: View 1 : 0.426751592357
+2016-08-24 09:38:04,135 DEBUG: View 2 : 0.420382165605
+2016-08-24 09:38:04,142 DEBUG: View 3 : 0.426751592357
+2016-08-24 09:38:04,142 WARNING: All bad for iteration 48
+2016-08-24 09:38:04,297 DEBUG: Best view : Methyl_
+2016-08-24 09:38:07,226 DEBUG: Start: Iteration 50
+2016-08-24 09:38:07,243 DEBUG: View 0 : 0.630573248408
+2016-08-24 09:38:07,250 DEBUG: View 1 : 0.777070063694
+2016-08-24 09:38:07,286 DEBUG: View 2 : 0.464968152866
+2016-08-24 09:38:07,294 DEBUG: View 3 : 0.605095541401
+2016-08-24 09:38:07,449 DEBUG: Best view : MiRNA__
+2016-08-24 09:38:10,439 DEBUG: Start: Iteration 51
+2016-08-24 09:38:10,456 DEBUG: View 0 : 0.757961783439
+2016-08-24 09:38:10,464 DEBUG: View 1 : 0.490445859873
+2016-08-24 09:38:10,499 DEBUG: View 2 : 0.464968152866
+2016-08-24 09:38:10,507 DEBUG: View 3 : 0.509554140127
+2016-08-24 09:38:10,664 DEBUG: Best view : Methyl_
+2016-08-24 09:38:13,867 DEBUG: Start: Iteration 52
+2016-08-24 09:38:13,885 DEBUG: View 0 : 0.496815286624
+2016-08-24 09:38:13,892 DEBUG: View 1 : 0.59872611465
+2016-08-24 09:38:13,928 DEBUG: View 2 : 0.407643312102
+2016-08-24 09:38:13,936 DEBUG: View 3 : 0.585987261146
+2016-08-24 09:38:14,100 DEBUG: Best view : MiRNA__
+2016-08-24 09:38:17,221 INFO: Start: Classification
+2016-08-24 09:38:24,786 INFO: Done: Fold number 2
+2016-08-24 09:38:24,786 INFO: Done: Classification
+2016-08-24 09:38:24,786 INFO: Info: Time for Classification: 269[s]
+2016-08-24 09:38:24,786 INFO: Start: Result Analysis for Mumbo
+2016-08-24 09:38:42,191 INFO: Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 74.1078719582
+ -On Test : 79.0983606557
+ -On Validation : 81.5533980583
+
+Dataset info :
+ -Database name : ModifiedMultiOmic
+ -Labels : No, Yes
+ -Views : Methyl, MiRNA, RNASEQ, Clinical
+ -2 folds
+ - Validation set length : 103 for learning rate : 0.7
+
+Classification configuration :
+ -Algorithm used : Mumbo
+ -Iterations : 1000
+ -Weak Classifiers : DecisionTree with depth 1.0, sub-sampled at 0.0075 on Methyl
+ -DecisionTree with depth 1.0, sub-sampled at 0.007 on MiRNA
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on RNASEQ
+ -DecisionTree with depth 1.0, sub-sampled at 0.0075 on Clinical
+
+ Mean average accuracies and stats for each fold :
+ - Fold 0
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0274615384615
+ - Percentage of time chosen : 0.96
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0292820512821
+ - Percentage of time chosen : 0.028
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.0261987179487
+ - Percentage of time chosen : 0.004
+ - On Clinic_ :
+ - Mean average Accuracy : 0.027391025641
+ - Percentage of time chosen : 0.008
+ - Fold 1
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0279617834395
+ - Percentage of time chosen : 0.961
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.028178343949
+ - Percentage of time chosen : 0.026
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.0253566878981
+ - Percentage of time chosen : 0.005
+ - On Clinic_ :
+ - Mean average Accuracy : 0.0265031847134
+ - Percentage of time chosen : 0.008
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
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+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 995
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 996
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 997
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 998
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 999
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 1000
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:02:40 0:00:07
+ Fold 2 0:04:22 0:00:07
+ Total 0:07:02 0:00:15
+ So a total classification time of 0:04:29.
+
+
+2016-08-24 09:38:43,134 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093842Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093842Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png
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diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093842Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093842Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt
new file mode 100644
index 00000000..6a91f6d6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-093842Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt
@@ -0,0 +1,14060 @@
+ Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 74.1078719582
+ -On Test : 79.0983606557
+ -On Validation : 81.5533980583
+
+Dataset info :
+ -Database name : ModifiedMultiOmic
+ -Labels : No, Yes
+ -Views : Methyl, MiRNA, RNASEQ, Clinical
+ -2 folds
+ - Validation set length : 103 for learning rate : 0.7
+
+Classification configuration :
+ -Algorithm used : Mumbo
+ -Iterations : 1000
+ -Weak Classifiers : DecisionTree with depth 1.0, sub-sampled at 0.0075 on Methyl
+ -DecisionTree with depth 1.0, sub-sampled at 0.007 on MiRNA
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on RNASEQ
+ -DecisionTree with depth 1.0, sub-sampled at 0.0075 on Clinical
+
+ Mean average accuracies and stats for each fold :
+ - Fold 0
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0274615384615
+ - Percentage of time chosen : 0.96
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0292820512821
+ - Percentage of time chosen : 0.028
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.0261987179487
+ - Percentage of time chosen : 0.004
+ - On Clinic_ :
+ - Mean average Accuracy : 0.027391025641
+ - Percentage of time chosen : 0.008
+ - Fold 1
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0279617834395
+ - Percentage of time chosen : 0.961
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.028178343949
+ - Percentage of time chosen : 0.026
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.0253566878981
+ - Percentage of time chosen : 0.005
+ - On Clinic_ :
+ - Mean average Accuracy : 0.0265031847134
+ - Percentage of time chosen : 0.008
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 66.6666666667
+ Accuracy on test : 69.6721311475
+ Accuracy on validation : 72.8155339806
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 64.9681528662
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 65.8174097665
+ Accuracy on test : 71.7213114754
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 66.6666666667
+ Accuracy on test : 69.6721311475
+ Accuracy on validation : 72.8155339806
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 64.9681528662
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 65.8174097665
+ Accuracy on test : 71.7213114754
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 56.4102564103
+ Accuracy on test : 65.5737704918
+ Accuracy on validation : 66.0194174757
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 63.0573248408
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 76.6990291262
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 59.7337906255
+ Accuracy on test : 69.262295082
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 66.0256410256
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 67.9611650485
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 72.8155339806
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 63.904540258
+ Accuracy on test : 72.9508196721
+ - Iteration 6
+ Fold 1
+ Accuracy on train : 65.3846153846
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 72.8155339806
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 63.5840274375
+ Accuracy on test : 72.9508196721
+ - Iteration 7
+ Fold 1
+ Accuracy on train : 68.5897435897
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 63.6942675159
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 72.8155339806
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 66.1420055528
+ Accuracy on test : 75.8196721311
+ - Iteration 8
+ Fold 1
+ Accuracy on train : 66.0256410256
+ Accuracy on test : 68.8524590164
+ Accuracy on validation : 71.8446601942
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 62.4203821656
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 72.8155339806
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 64.2230115956
+ Accuracy on test : 71.3114754098
+ - Iteration 9
+ Fold 1
+ Accuracy on train : 73.0769230769
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 75.7281553398
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 62.4203821656
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 72.8155339806
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 67.7486526213
+ Accuracy on test : 75.4098360656
+ - Iteration 10
+ Fold 1
+ Accuracy on train : 73.7179487179
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 78.640776699
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 65.6050955414
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 71.8446601942
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 69.6615221297
+ Accuracy on test : 77.0491803279
+ - Iteration 11
+ Fold 1
+ Accuracy on train : 71.7948717949
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 65.6050955414
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 71.8446601942
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 68.6999836681
+ Accuracy on test : 77.4590163934
+ - Iteration 12
+ Fold 1
+ Accuracy on train : 69.8717948718
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 76.6990291262
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 71.3375796178
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 70.6046872448
+ Accuracy on test : 77.868852459
+ - Iteration 13
+ Fold 1
+ Accuracy on train : 69.8717948718
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 76.6990291262
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 69.4267515924
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 77.6699029126
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 69.6492732321
+ Accuracy on test : 76.6393442623
+ - Iteration 14
+ Fold 1
+ Accuracy on train : 69.8717948718
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 70.7006369427
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 77.6699029126
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 70.2862159072
+ Accuracy on test : 76.6393442623
+ - Iteration 15
+ Fold 1
+ Accuracy on train : 70.5128205128
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 76.6990291262
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 68.7898089172
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 69.651314715
+ Accuracy on test : 78.6885245902
+ - Iteration 16
+ Fold 1
+ Accuracy on train : 68.5897435897
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 73.2484076433
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 70.9190756165
+ Accuracy on test : 77.868852459
+ - Iteration 17
+ Fold 1
+ Accuracy on train : 67.3076923077
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 76.6990291262
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 66.8789808917
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 67.0933365997
+ Accuracy on test : 77.4590163934
+ - Iteration 18
+ Fold 1
+ Accuracy on train : 62.8205128205
+ Accuracy on test : 74.5901639344
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 67.5159235669
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 76.6990291262
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 65.1682181937
+ Accuracy on test : 76.2295081967
+ - Iteration 19
+ Fold 1
+ Accuracy on train : 66.0256410256
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 74.7572815534
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 68.7898089172
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 76.6990291262
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 67.4077249714
+ Accuracy on test : 77.0491803279
+ - Iteration 20
+ Fold 1
+ Accuracy on train : 68.5897435897
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 66.2420382166
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 76.6990291262
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 67.4158909032
+ Accuracy on test : 78.2786885246
+ - Iteration 21
+ Fold 1
+ Accuracy on train : 71.7948717949
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 76.6990291262
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 66.2420382166
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 69.0184550057
+ Accuracy on test : 78.2786885246
+ - Iteration 22
+ Fold 1
+ Accuracy on train : 73.7179487179
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 68.152866242
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 70.93540748
+ Accuracy on test : 79.9180327869
+ - Iteration 23
+ Fold 1
+ Accuracy on train : 71.7948717949
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 66.2420382166
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 69.0184550057
+ Accuracy on test : 78.6885245902
+ - Iteration 24
+ Fold 1
+ Accuracy on train : 74.358974359
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 65.6050955414
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 69.9820349502
+ Accuracy on test : 79.9180327869
+ - Iteration 25
+ Fold 1
+ Accuracy on train : 74.358974359
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 64.9681528662
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 72.8155339806
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 69.6635636126
+ Accuracy on test : 80.3278688525
+ - Iteration 26
+ Fold 1
+ Accuracy on train : 78.2051282051
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 83.4951456311
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 67.5159235669
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 72.860525886
+ Accuracy on test : 80.3278688525
+ - Iteration 27
+ Fold 1
+ Accuracy on train : 74.358974359
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 81.5533980583
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 67.5159235669
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 70.9374489629
+ Accuracy on test : 79.9180327869
+ - Iteration 28
+ Fold 1
+ Accuracy on train : 75.641025641
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 82.5242718447
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 67.5159235669
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 71.578474604
+ Accuracy on test : 80.3278688525
+ - Iteration 29
+ Fold 1
+ Accuracy on train : 73.7179487179
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 68.7898089172
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 75.7281553398
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 71.2538788176
+ Accuracy on test : 79.5081967213
+ - Iteration 30
+ Fold 1
+ Accuracy on train : 71.1538461538
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 71.3375796178
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 71.2457128858
+ Accuracy on test : 80.3278688525
+ - Iteration 31
+ Fold 1
+ Accuracy on train : 75.0
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 81.5533980583
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 73.2484076433
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 78.640776699
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 74.1242038217
+ Accuracy on test : 81.5573770492
+ - Iteration 32
+ Fold 1
+ Accuracy on train : 75.641025641
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 81.5533980583
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 75.1592356688
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 75.4001306549
+ Accuracy on test : 81.1475409836
+ - Iteration 33
+ Fold 1
+ Accuracy on train : 74.358974359
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 81.5533980583
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 71.974522293
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 73.166748326
+ Accuracy on test : 79.9180327869
+ - Iteration 34
+ Fold 1
+ Accuracy on train : 71.1538461538
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 71.3375796178
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 71.2457128858
+ Accuracy on test : 79.5081967213
+ - Iteration 35
+ Fold 1
+ Accuracy on train : 73.7179487179
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 71.974522293
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 72.8462355055
+ Accuracy on test : 79.5081967213
+ - Iteration 36
+ Fold 1
+ Accuracy on train : 72.4358974359
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 70.7006369427
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 74.7572815534
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 71.5682671893
+ Accuracy on test : 78.6885245902
+ - Iteration 37
+ Fold 1
+ Accuracy on train : 73.0769230769
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 71.974522293
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 72.525722685
+ Accuracy on test : 78.2786885246
+ - Iteration 38
+ Fold 1
+ Accuracy on train : 73.0769230769
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 73.2484076433
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 73.1626653601
+ Accuracy on test : 79.9180327869
+ - Iteration 39
+ Fold 1
+ Accuracy on train : 72.4358974359
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 74.5222929936
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 79.6116504854
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 73.4790952148
+ Accuracy on test : 79.9180327869
+ - Iteration 40
+ Fold 1
+ Accuracy on train : 73.0769230769
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 73.8853503185
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 78.640776699
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 73.4811366977
+ Accuracy on test : 80.737704918
+ - Iteration 41
+ Fold 1
+ Accuracy on train : 71.7948717949
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 74.5222929936
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 73.1585823943
+ Accuracy on test : 78.2786885246
+ - Iteration 42
+ Fold 1
+ Accuracy on train : 73.0769230769
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 74.5222929936
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 73.7996080353
+ Accuracy on test : 80.3278688525
+ - Iteration 43
+ Fold 1
+ Accuracy on train : 72.4358974359
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 73.8853503185
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 73.1606238772
+ Accuracy on test : 79.5081967213
+ - Iteration 44
+ Fold 1
+ Accuracy on train : 73.0769230769
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 73.2484076433
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 73.1626653601
+ Accuracy on test : 79.0983606557
+ - Iteration 45
+ Fold 1
+ Accuracy on train : 73.0769230769
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 73.8853503185
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 73.4811366977
+ Accuracy on test : 80.737704918
+ - Iteration 46
+ Fold 1
+ Accuracy on train : 72.4358974359
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 73.2484076433
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 72.8421525396
+ Accuracy on test : 77.868852459
+ - Iteration 47
+ Fold 1
+ Accuracy on train : 71.7948717949
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 73.8853503185
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 80.5825242718
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 72.8401110567
+ Accuracy on test : 78.2786885246
+ - Iteration 48
+ Fold 1
+ Accuracy on train : 70.5128205128
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 73.8853503185
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 81.5533980583
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 72.1990854156
+ Accuracy on test : 79.0983606557
+ - Iteration 49
+ Fold 1
+ Accuracy on train : 72.4358974359
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 73.8853503185
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 81.5533980583
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 73.1606238772
+ Accuracy on test : 79.5081967213
+ - Iteration 50
+ Fold 1
+ Accuracy on train : 71.1538461538
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 80.5825242718
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 73.8853503185
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ - Mean :
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+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 991
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 992
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 993
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 994
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 995
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 996
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 997
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 998
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 999
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+ - Iteration 1000
+ Fold 1
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.7834394904
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.6609505145
+ Accuracy on test : 72.9508196721
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:02:40 0:00:07
+ Fold 2 0:04:22 0:00:07
+ Total 0:07:02 0:00:15
+ So a total classification time of 0:04:29.
+
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-094408-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-094408-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..f87aee13
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-094408-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,531 @@
+2016-08-24 09:44:08,361 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:44:08,361 INFO: Info: Labels used: No, Yes
+2016-08-24 09:44:08,361 INFO: Info: Length of dataset:347
+2016-08-24 09:44:08,363 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:44:08,363 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:44:08,363 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:44:08,364 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:44:08,364 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:44:08,365 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:44:08,365 INFO: Done: Read Database Files
+2016-08-24 09:44:08,365 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:44:08,368 INFO: Done: Determine validation split
+2016-08-24 09:44:08,368 INFO: Start: Determine 2 folds
+2016-08-24 09:44:08,378 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:44:08,378 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:44:08,378 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:44:08,379 INFO: Done: Determine folds
+2016-08-24 09:44:08,379 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:44:08,379 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:44:08,379 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:44:15,803 DEBUG: 0.585648414986Poulet
+2016-08-24 09:44:15,803 DEBUG: 0.560864553314Poulet
+2016-08-24 09:44:15,803 DEBUG: 0.511642651297Poulet
+2016-08-24 09:44:15,803 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:44:15,804 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:44:17,725 DEBUG: 0.543054755043Poulet
+2016-08-24 09:44:17,725 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:44:17,725 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 09:44:34,897 DEBUG: 0.577463976945Poulet
+2016-08-24 09:44:34,898 DEBUG: 0.563400576369Poulet
+2016-08-24 09:44:34,898 DEBUG: 0.511930835735Poulet
+2016-08-24 09:44:34,899 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:44:34,899 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 09:44:36,702 DEBUG: 0.56265129683Poulet
+2016-08-24 09:44:36,702 DEBUG: 0.561383285303Poulet
+2016-08-24 09:44:36,702 DEBUG: 0.501844380403Poulet
+2016-08-24 09:44:36,702 DEBUG: 0.514351585014Poulet
+2016-08-24 09:44:36,702 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:44:36,703 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 09:45:24,687 DEBUG: 0.541556195965Poulet
+2016-08-24 09:45:24,687 DEBUG: 0.528357348703Poulet
+2016-08-24 09:45:24,689 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:45:24,689 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 09:45:24,689 INFO: Start: Fold number 1
+2016-08-24 09:45:26,374 DEBUG: Start: Iteration 1
+2016-08-24 09:45:26,392 DEBUG: View 0 : 0.379746835443
+2016-08-24 09:45:26,400 DEBUG: View 1 : 0.620253164557
+2016-08-24 09:45:26,431 DEBUG: View 2 : 0.620253164557
+2016-08-24 09:45:26,442 DEBUG: View 3 : 0.620253164557
+2016-08-24 09:45:26,485 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:26,561 DEBUG: Start: Iteration 2
+2016-08-24 09:45:26,579 DEBUG: View 0 : 0.443037974684
+2016-08-24 09:45:26,587 DEBUG: View 1 : 0.563291139241
+2016-08-24 09:45:26,628 DEBUG: View 2 : 0.563291139241
+2016-08-24 09:45:26,636 DEBUG: View 3 : 0.651898734177
+2016-08-24 09:45:26,693 DEBUG: Best view : Clinic_
+2016-08-24 09:45:26,830 DEBUG: Start: Iteration 3
+2016-08-24 09:45:26,847 DEBUG: View 0 : 0.550632911392
+2016-08-24 09:45:26,855 DEBUG: View 1 : 0.563291139241
+2016-08-24 09:45:26,892 DEBUG: View 2 : 0.53164556962
+2016-08-24 09:45:26,902 DEBUG: View 3 : 0.462025316456
+2016-08-24 09:45:26,957 DEBUG: Best view : Clinic_
+2016-08-24 09:45:27,145 DEBUG: Start: Iteration 4
+2016-08-24 09:45:27,161 DEBUG: View 0 : 0.569620253165
+2016-08-24 09:45:27,169 DEBUG: View 1 : 0.329113924051
+2016-08-24 09:45:27,206 DEBUG: View 2 : 0.525316455696
+2016-08-24 09:45:27,214 DEBUG: View 3 : 0.588607594937
+2016-08-24 09:45:27,272 DEBUG: Best view : Clinic_
+2016-08-24 09:45:27,520 DEBUG: Start: Iteration 5
+2016-08-24 09:45:27,537 DEBUG: View 0 : 0.443037974684
+2016-08-24 09:45:27,545 DEBUG: View 1 : 0.569620253165
+2016-08-24 09:45:27,582 DEBUG: View 2 : 0.537974683544
+2016-08-24 09:45:27,590 DEBUG: View 3 : 0.518987341772
+2016-08-24 09:45:27,648 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:27,951 DEBUG: Start: Iteration 6
+2016-08-24 09:45:27,968 DEBUG: View 0 : 0.386075949367
+2016-08-24 09:45:27,976 DEBUG: View 1 : 0.651898734177
+2016-08-24 09:45:28,012 DEBUG: View 2 : 0.430379746835
+2016-08-24 09:45:28,020 DEBUG: View 3 : 0.443037974684
+2016-08-24 09:45:28,080 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:28,444 DEBUG: Start: Iteration 7
+2016-08-24 09:45:28,460 DEBUG: View 0 : 0.664556962025
+2016-08-24 09:45:28,468 DEBUG: View 1 : 0.443037974684
+2016-08-24 09:45:28,505 DEBUG: View 2 : 0.525316455696
+2016-08-24 09:45:28,512 DEBUG: View 3 : 0.487341772152
+2016-08-24 09:45:28,575 DEBUG: Best view : Methyl_
+2016-08-24 09:45:28,997 DEBUG: Start: Iteration 8
+2016-08-24 09:45:29,014 DEBUG: View 0 : 0.424050632911
+2016-08-24 09:45:29,021 DEBUG: View 1 : 0.417721518987
+2016-08-24 09:45:29,058 DEBUG: View 2 : 0.556962025316
+2016-08-24 09:45:29,066 DEBUG: View 3 : 0.594936708861
+2016-08-24 09:45:29,131 DEBUG: Best view : Clinic_
+2016-08-24 09:45:29,628 DEBUG: Start: Iteration 9
+2016-08-24 09:45:29,645 DEBUG: View 0 : 0.512658227848
+2016-08-24 09:45:29,653 DEBUG: View 1 : 0.417721518987
+2016-08-24 09:45:29,691 DEBUG: View 2 : 0.639240506329
+2016-08-24 09:45:29,699 DEBUG: View 3 : 0.411392405063
+2016-08-24 09:45:29,769 DEBUG: Best view : RANSeq_
+2016-08-24 09:45:30,333 DEBUG: Start: Iteration 10
+2016-08-24 09:45:30,349 DEBUG: View 0 : 0.506329113924
+2016-08-24 09:45:30,357 DEBUG: View 1 : 0.639240506329
+2016-08-24 09:45:30,394 DEBUG: View 2 : 0.424050632911
+2016-08-24 09:45:30,402 DEBUG: View 3 : 0.436708860759
+2016-08-24 09:45:30,471 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:31,083 DEBUG: Start: Iteration 11
+2016-08-24 09:45:31,100 DEBUG: View 0 : 0.626582278481
+2016-08-24 09:45:31,107 DEBUG: View 1 : 0.354430379747
+2016-08-24 09:45:31,144 DEBUG: View 2 : 0.398734177215
+2016-08-24 09:45:31,152 DEBUG: View 3 : 0.607594936709
+2016-08-24 09:45:31,223 DEBUG: Best view : Methyl_
+2016-08-24 09:45:31,904 DEBUG: Start: Iteration 12
+2016-08-24 09:45:31,921 DEBUG: View 0 : 0.689873417722
+2016-08-24 09:45:31,929 DEBUG: View 1 : 0.481012658228
+2016-08-24 09:45:31,965 DEBUG: View 2 : 0.46835443038
+2016-08-24 09:45:31,973 DEBUG: View 3 : 0.493670886076
+2016-08-24 09:45:32,047 DEBUG: Best view : Methyl_
+2016-08-24 09:45:32,812 DEBUG: Start: Iteration 13
+2016-08-24 09:45:32,829 DEBUG: View 0 : 0.417721518987
+2016-08-24 09:45:32,837 DEBUG: View 1 : 0.639240506329
+2016-08-24 09:45:32,874 DEBUG: View 2 : 0.405063291139
+2016-08-24 09:45:32,882 DEBUG: View 3 : 0.620253164557
+2016-08-24 09:45:32,957 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:33,749 DEBUG: Start: Iteration 14
+2016-08-24 09:45:33,765 DEBUG: View 0 : 0.481012658228
+2016-08-24 09:45:33,773 DEBUG: View 1 : 0.278481012658
+2016-08-24 09:45:33,809 DEBUG: View 2 : 0.379746835443
+2016-08-24 09:45:33,817 DEBUG: View 3 : 0.392405063291
+2016-08-24 09:45:33,818 WARNING: WARNING: All bad for iteration 13
+2016-08-24 09:45:33,896 DEBUG: Best view : Methyl_
+2016-08-24 09:45:34,783 DEBUG: Start: Iteration 15
+2016-08-24 09:45:34,801 DEBUG: View 0 : 0.436708860759
+2016-08-24 09:45:34,809 DEBUG: View 1 : 0.563291139241
+2016-08-24 09:45:34,849 DEBUG: View 2 : 0.544303797468
+2016-08-24 09:45:34,858 DEBUG: View 3 : 0.474683544304
+2016-08-24 09:45:34,952 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:35,984 DEBUG: Start: Iteration 16
+2016-08-24 09:45:36,002 DEBUG: View 0 : 0.550632911392
+2016-08-24 09:45:36,010 DEBUG: View 1 : 0.443037974684
+2016-08-24 09:45:36,049 DEBUG: View 2 : 0.544303797468
+2016-08-24 09:45:36,057 DEBUG: View 3 : 0.506329113924
+2016-08-24 09:45:36,141 DEBUG: Best view : RANSeq_
+2016-08-24 09:45:37,137 DEBUG: Start: Iteration 17
+2016-08-24 09:45:37,154 DEBUG: View 0 : 0.392405063291
+2016-08-24 09:45:37,162 DEBUG: View 1 : 0.670886075949
+2016-08-24 09:45:37,199 DEBUG: View 2 : 0.443037974684
+2016-08-24 09:45:37,207 DEBUG: View 3 : 0.398734177215
+2016-08-24 09:45:37,294 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:38,383 DEBUG: Start: Iteration 18
+2016-08-24 09:45:38,400 DEBUG: View 0 : 0.462025316456
+2016-08-24 09:45:38,407 DEBUG: View 1 : 0.620253164557
+2016-08-24 09:45:38,444 DEBUG: View 2 : 0.493670886076
+2016-08-24 09:45:38,452 DEBUG: View 3 : 0.556962025316
+2016-08-24 09:45:38,541 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:39,640 DEBUG: Start: Iteration 19
+2016-08-24 09:45:39,656 DEBUG: View 0 : 0.689873417722
+2016-08-24 09:45:39,664 DEBUG: View 1 : 0.544303797468
+2016-08-24 09:45:39,701 DEBUG: View 2 : 0.594936708861
+2016-08-24 09:45:39,708 DEBUG: View 3 : 0.594936708861
+2016-08-24 09:45:39,799 DEBUG: Best view : Methyl_
+2016-08-24 09:45:40,989 DEBUG: Start: Iteration 20
+2016-08-24 09:45:41,005 DEBUG: View 0 : 0.550632911392
+2016-08-24 09:45:41,013 DEBUG: View 1 : 0.613924050633
+2016-08-24 09:45:41,050 DEBUG: View 2 : 0.613924050633
+2016-08-24 09:45:41,058 DEBUG: View 3 : 0.607594936709
+2016-08-24 09:45:41,151 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:42,389 DEBUG: Start: Iteration 21
+2016-08-24 09:45:42,405 DEBUG: View 0 : 0.651898734177
+2016-08-24 09:45:42,413 DEBUG: View 1 : 0.664556962025
+2016-08-24 09:45:42,450 DEBUG: View 2 : 0.563291139241
+2016-08-24 09:45:42,458 DEBUG: View 3 : 0.417721518987
+2016-08-24 09:45:42,554 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:43,837 DEBUG: Start: Iteration 22
+2016-08-24 09:45:43,853 DEBUG: View 0 : 0.537974683544
+2016-08-24 09:45:43,861 DEBUG: View 1 : 0.607594936709
+2016-08-24 09:45:43,899 DEBUG: View 2 : 0.582278481013
+2016-08-24 09:45:43,906 DEBUG: View 3 : 0.493670886076
+2016-08-24 09:45:44,006 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:45,380 DEBUG: Start: Iteration 23
+2016-08-24 09:45:45,397 DEBUG: View 0 : 0.626582278481
+2016-08-24 09:45:45,405 DEBUG: View 1 : 0.462025316456
+2016-08-24 09:45:45,443 DEBUG: View 2 : 0.613924050633
+2016-08-24 09:45:45,451 DEBUG: View 3 : 0.487341772152
+2016-08-24 09:45:45,552 DEBUG: Best view : Methyl_
+2016-08-24 09:45:46,972 DEBUG: Start: Iteration 24
+2016-08-24 09:45:46,991 DEBUG: View 0 : 0.474683544304
+2016-08-24 09:45:46,999 DEBUG: View 1 : 0.607594936709
+2016-08-24 09:45:47,037 DEBUG: View 2 : 0.556962025316
+2016-08-24 09:45:47,045 DEBUG: View 3 : 0.405063291139
+2016-08-24 09:45:47,149 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:48,783 DEBUG: Start: Iteration 25
+2016-08-24 09:45:48,799 DEBUG: View 0 : 0.5
+2016-08-24 09:45:48,807 DEBUG: View 1 : 0.512658227848
+2016-08-24 09:45:48,844 DEBUG: View 2 : 0.487341772152
+2016-08-24 09:45:48,852 DEBUG: View 3 : 0.474683544304
+2016-08-24 09:45:48,956 DEBUG: Best view : Methyl_
+2016-08-24 09:45:50,533 DEBUG: Start: Iteration 26
+2016-08-24 09:45:50,550 DEBUG: View 0 : 0.645569620253
+2016-08-24 09:45:50,558 DEBUG: View 1 : 0.651898734177
+2016-08-24 09:45:50,595 DEBUG: View 2 : 0.411392405063
+2016-08-24 09:45:50,603 DEBUG: View 3 : 0.582278481013
+2016-08-24 09:45:50,707 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:52,409 DEBUG: Start: Iteration 27
+2016-08-24 09:45:52,430 DEBUG: View 0 : 0.588607594937
+2016-08-24 09:45:52,439 DEBUG: View 1 : 0.601265822785
+2016-08-24 09:45:52,483 DEBUG: View 2 : 0.588607594937
+2016-08-24 09:45:52,492 DEBUG: View 3 : 0.449367088608
+2016-08-24 09:45:52,615 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:54,284 DEBUG: Start: Iteration 28
+2016-08-24 09:45:54,301 DEBUG: View 0 : 0.46835443038
+2016-08-24 09:45:54,309 DEBUG: View 1 : 0.405063291139
+2016-08-24 09:45:54,345 DEBUG: View 2 : 0.455696202532
+2016-08-24 09:45:54,353 DEBUG: View 3 : 0.436708860759
+2016-08-24 09:45:54,354 WARNING: WARNING: All bad for iteration 27
+2016-08-24 09:45:54,463 DEBUG: Best view : Methyl_
+2016-08-24 09:45:56,154 DEBUG: Start: Iteration 29
+2016-08-24 09:45:56,170 DEBUG: View 0 : 0.405063291139
+2016-08-24 09:45:56,178 DEBUG: View 1 : 0.632911392405
+2016-08-24 09:45:56,214 DEBUG: View 2 : 0.417721518987
+2016-08-24 09:45:56,222 DEBUG: View 3 : 0.474683544304
+2016-08-24 09:45:56,334 DEBUG: Best view : MiRNA__
+2016-08-24 09:45:58,088 DEBUG: Start: Iteration 30
+2016-08-24 09:45:58,104 DEBUG: View 0 : 0.5
+2016-08-24 09:45:58,112 DEBUG: View 1 : 0.398734177215
+2016-08-24 09:45:58,149 DEBUG: View 2 : 0.455696202532
+2016-08-24 09:45:58,157 DEBUG: View 3 : 0.417721518987
+2016-08-24 09:45:58,270 DEBUG: Best view : Methyl_
+2016-08-24 09:46:00,083 DEBUG: Start: Iteration 31
+2016-08-24 09:46:00,100 DEBUG: View 0 : 0.462025316456
+2016-08-24 09:46:00,108 DEBUG: View 1 : 0.424050632911
+2016-08-24 09:46:00,144 DEBUG: View 2 : 0.417721518987
+2016-08-24 09:46:00,152 DEBUG: View 3 : 0.436708860759
+2016-08-24 09:46:00,152 WARNING: WARNING: All bad for iteration 30
+2016-08-24 09:46:00,269 DEBUG: Best view : Methyl_
+2016-08-24 09:46:02,316 DEBUG: Start: Iteration 32
+2016-08-24 09:46:02,332 DEBUG: View 0 : 0.632911392405
+2016-08-24 09:46:02,340 DEBUG: View 1 : 0.727848101266
+2016-08-24 09:46:02,377 DEBUG: View 2 : 0.626582278481
+2016-08-24 09:46:02,385 DEBUG: View 3 : 0.449367088608
+2016-08-24 09:46:02,504 DEBUG: Best view : MiRNA__
+2016-08-24 09:46:04,444 DEBUG: Start: Iteration 33
+2016-08-24 09:46:04,461 DEBUG: View 0 : 0.556962025316
+2016-08-24 09:46:04,469 DEBUG: View 1 : 0.626582278481
+2016-08-24 09:46:04,505 DEBUG: View 2 : 0.506329113924
+2016-08-24 09:46:04,513 DEBUG: View 3 : 0.525316455696
+2016-08-24 09:46:04,633 DEBUG: Best view : MiRNA__
+2016-08-24 09:46:06,628 DEBUG: Start: Iteration 34
+2016-08-24 09:46:06,645 DEBUG: View 0 : 0.53164556962
+2016-08-24 09:46:06,652 DEBUG: View 1 : 0.506329113924
+2016-08-24 09:46:06,689 DEBUG: View 2 : 0.430379746835
+2016-08-24 09:46:06,697 DEBUG: View 3 : 0.481012658228
+2016-08-24 09:46:06,820 DEBUG: Best view : Methyl_
+2016-08-24 09:46:08,877 DEBUG: Start: Iteration 35
+2016-08-24 09:46:08,894 DEBUG: View 0 : 0.430379746835
+2016-08-24 09:46:08,901 DEBUG: View 1 : 0.544303797468
+2016-08-24 09:46:08,938 DEBUG: View 2 : 0.474683544304
+2016-08-24 09:46:08,945 DEBUG: View 3 : 0.405063291139
+2016-08-24 09:46:09,070 DEBUG: Best view : MiRNA__
+2016-08-24 09:46:11,182 DEBUG: Start: Iteration 36
+2016-08-24 09:46:11,198 DEBUG: View 0 : 0.569620253165
+2016-08-24 09:46:11,206 DEBUG: View 1 : 0.664556962025
+2016-08-24 09:46:11,243 DEBUG: View 2 : 0.651898734177
+2016-08-24 09:46:11,251 DEBUG: View 3 : 0.594936708861
+2016-08-24 09:46:11,378 DEBUG: Best view : MiRNA__
+2016-08-24 09:46:13,547 DEBUG: Start: Iteration 37
+2016-08-24 09:46:13,563 DEBUG: View 0 : 0.474683544304
+2016-08-24 09:46:13,571 DEBUG: View 1 : 0.651898734177
+2016-08-24 09:46:13,607 DEBUG: View 2 : 0.392405063291
+2016-08-24 09:46:13,615 DEBUG: View 3 : 0.392405063291
+2016-08-24 09:46:13,744 DEBUG: Best view : MiRNA__
+2016-08-24 09:46:15,973 DEBUG: Start: Iteration 38
+2016-08-24 09:46:15,989 DEBUG: View 0 : 0.651898734177
+2016-08-24 09:46:15,997 DEBUG: View 1 : 0.639240506329
+2016-08-24 09:46:16,034 DEBUG: View 2 : 0.487341772152
+2016-08-24 09:46:16,042 DEBUG: View 3 : 0.582278481013
+2016-08-24 09:46:16,175 DEBUG: Best view : Methyl_
+2016-08-24 09:46:18,469 DEBUG: Start: Iteration 39
+2016-08-24 09:46:18,485 DEBUG: View 0 : 0.537974683544
+2016-08-24 09:46:18,493 DEBUG: View 1 : 0.392405063291
+2016-08-24 09:46:18,529 DEBUG: View 2 : 0.525316455696
+2016-08-24 09:46:18,537 DEBUG: View 3 : 0.46835443038
+2016-08-24 09:46:18,671 DEBUG: Best view : Methyl_
+2016-08-24 09:46:21,024 DEBUG: Start: Iteration 40
+2016-08-24 09:46:21,041 DEBUG: View 0 : 0.639240506329
+2016-08-24 09:46:21,049 DEBUG: View 1 : 0.582278481013
+2016-08-24 09:46:21,085 DEBUG: View 2 : 0.474683544304
+2016-08-24 09:46:21,093 DEBUG: View 3 : 0.481012658228
+2016-08-24 09:46:21,229 DEBUG: Best view : Methyl_
+2016-08-24 09:46:23,647 DEBUG: Start: Iteration 41
+2016-08-24 09:46:23,663 DEBUG: View 0 : 0.481012658228
+2016-08-24 09:46:23,671 DEBUG: View 1 : 0.664556962025
+2016-08-24 09:46:23,708 DEBUG: View 2 : 0.5
+2016-08-24 09:46:23,715 DEBUG: View 3 : 0.392405063291
+2016-08-24 09:46:23,854 DEBUG: Best view : MiRNA__
+2016-08-24 09:46:26,328 DEBUG: Start: Iteration 42
+2016-08-24 09:46:26,345 DEBUG: View 0 : 0.506329113924
+2016-08-24 09:46:26,352 DEBUG: View 1 : 0.582278481013
+2016-08-24 09:46:26,389 DEBUG: View 2 : 0.430379746835
+2016-08-24 09:46:26,397 DEBUG: View 3 : 0.632911392405
+2016-08-24 09:46:26,539 DEBUG: Best view : Clinic_
+2016-08-24 09:46:29,075 DEBUG: Start: Iteration 43
+2016-08-24 09:46:29,091 DEBUG: View 0 : 0.398734177215
+2016-08-24 09:46:29,099 DEBUG: View 1 : 0.5
+2016-08-24 09:46:29,136 DEBUG: View 2 : 0.651898734177
+2016-08-24 09:46:29,143 DEBUG: View 3 : 0.424050632911
+2016-08-24 09:46:29,287 DEBUG: Best view : RANSeq_
+2016-08-24 09:46:31,887 DEBUG: Start: Iteration 44
+2016-08-24 09:46:31,904 DEBUG: View 0 : 0.506329113924
+2016-08-24 09:46:31,912 DEBUG: View 1 : 0.594936708861
+2016-08-24 09:46:31,949 DEBUG: View 2 : 0.348101265823
+2016-08-24 09:46:31,956 DEBUG: View 3 : 0.462025316456
+2016-08-24 09:46:32,102 DEBUG: Best view : MiRNA__
+2016-08-24 09:46:34,802 DEBUG: Start: Iteration 45
+2016-08-24 09:46:34,819 DEBUG: View 0 : 0.436708860759
+2016-08-24 09:46:34,826 DEBUG: View 1 : 0.601265822785
+2016-08-24 09:46:34,863 DEBUG: View 2 : 0.575949367089
+2016-08-24 09:46:34,870 DEBUG: View 3 : 0.537974683544
+2016-08-24 09:46:35,017 DEBUG: Best view : MiRNA__
+2016-08-24 09:46:37,732 DEBUG: Start: Iteration 46
+2016-08-24 09:46:37,748 DEBUG: View 0 : 0.430379746835
+2016-08-24 09:46:37,756 DEBUG: View 1 : 0.651898734177
+2016-08-24 09:46:37,792 DEBUG: View 2 : 0.398734177215
+2016-08-24 09:46:37,800 DEBUG: View 3 : 0.594936708861
+2016-08-24 09:46:37,949 DEBUG: Best view : MiRNA__
+2016-08-24 09:46:40,720 DEBUG: Start: Iteration 47
+2016-08-24 09:46:40,737 DEBUG: View 0 : 0.537974683544
+2016-08-24 09:46:40,744 DEBUG: View 1 : 0.373417721519
+2016-08-24 09:46:40,781 DEBUG: View 2 : 0.424050632911
+2016-08-24 09:46:40,789 DEBUG: View 3 : 0.506329113924
+2016-08-24 09:46:40,941 DEBUG: Best view : Methyl_
+2016-08-24 09:46:43,774 DEBUG: Start: Iteration 48
+2016-08-24 09:46:43,791 DEBUG: View 0 : 0.455696202532
+2016-08-24 09:46:43,799 DEBUG: View 1 : 0.677215189873
+2016-08-24 09:46:43,835 DEBUG: View 2 : 0.601265822785
+2016-08-24 09:46:43,843 DEBUG: View 3 : 0.443037974684
+2016-08-24 09:46:43,997 DEBUG: Best view : MiRNA__
+2016-08-24 09:46:46,889 DEBUG: Start: Iteration 49
+2016-08-24 09:46:46,906 DEBUG: View 0 : 0.443037974684
+2016-08-24 09:46:46,913 DEBUG: View 1 : 0.29746835443
+2016-08-24 09:46:46,950 DEBUG: View 2 : 0.582278481013
+2016-08-24 09:46:46,958 DEBUG: View 3 : 0.613924050633
+2016-08-24 09:46:47,114 DEBUG: Best view : Clinic_
+2016-08-24 09:46:50,150 DEBUG: Start: Iteration 50
+2016-08-24 09:46:50,167 DEBUG: View 0 : 0.436708860759
+2016-08-24 09:46:50,175 DEBUG: View 1 : 0.537974683544
+2016-08-24 09:46:50,211 DEBUG: View 2 : 0.569620253165
+2016-08-24 09:46:50,219 DEBUG: View 3 : 0.405063291139
+2016-08-24 09:46:50,378 DEBUG: Best view : RANSeq_
+2016-08-24 09:46:53,398 DEBUG: Start: Iteration 51
+2016-08-24 09:46:53,415 DEBUG: View 0 : 0.569620253165
+2016-08-24 09:46:53,423 DEBUG: View 1 : 0.417721518987
+2016-08-24 09:46:53,459 DEBUG: View 2 : 0.386075949367
+2016-08-24 09:46:53,467 DEBUG: View 3 : 0.563291139241
+2016-08-24 09:46:53,629 DEBUG: Best view : Methyl_
+2016-08-24 09:46:56,894 DEBUG: Start: Iteration 52
+2016-08-24 09:46:56,913 DEBUG: View 0 : 0.53164556962
+2016-08-24 09:46:56,921 DEBUG: View 1 : 0.405063291139
+2016-08-24 09:46:56,958 DEBUG: View 2 : 0.443037974684
+2016-08-24 09:46:56,966 DEBUG: View 3 : 0.405063291139
+2016-08-24 09:46:57,131 DEBUG: Best view : Methyl_
+2016-08-24 09:47:00,275 INFO: Start: Classification
+2016-08-24 09:47:07,807 INFO: Done: Fold number 1
+2016-08-24 09:47:07,807 INFO: Start: Fold number 2
+2016-08-24 09:47:09,379 DEBUG: Start: Iteration 1
+2016-08-24 09:47:09,398 DEBUG: View 0 : 0.446540880503
+2016-08-24 09:47:09,405 DEBUG: View 1 : 0.377358490566
+2016-08-24 09:47:09,433 DEBUG: View 2 : 0.622641509434
+2016-08-24 09:47:09,441 DEBUG: View 3 : 0.622641509434
+2016-08-24 09:47:09,482 DEBUG: Best view : RANSeq_
+2016-08-24 09:47:09,568 DEBUG: Start: Iteration 2
+2016-08-24 09:47:09,585 DEBUG: View 0 : 0.459119496855
+2016-08-24 09:47:09,593 DEBUG: View 1 : 0.477987421384
+2016-08-24 09:47:09,630 DEBUG: View 2 : 0.572327044025
+2016-08-24 09:47:09,637 DEBUG: View 3 : 0.389937106918
+2016-08-24 09:47:09,682 DEBUG: Best view : RANSeq_
+2016-08-24 09:47:09,840 DEBUG: Start: Iteration 3
+2016-08-24 09:47:09,856 DEBUG: View 0 : 0.534591194969
+2016-08-24 09:47:09,864 DEBUG: View 1 : 0.610062893082
+2016-08-24 09:47:09,900 DEBUG: View 2 : 0.383647798742
+2016-08-24 09:47:09,908 DEBUG: View 3 : 0.452830188679
+2016-08-24 09:47:09,960 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:10,177 DEBUG: Start: Iteration 4
+2016-08-24 09:47:10,194 DEBUG: View 0 : 0.566037735849
+2016-08-24 09:47:10,201 DEBUG: View 1 : 0.427672955975
+2016-08-24 09:47:10,239 DEBUG: View 2 : 0.408805031447
+2016-08-24 09:47:10,246 DEBUG: View 3 : 0.660377358491
+2016-08-24 09:47:10,301 DEBUG: Best view : Clinic_
+2016-08-24 09:47:10,577 DEBUG: Start: Iteration 5
+2016-08-24 09:47:10,594 DEBUG: View 0 : 0.509433962264
+2016-08-24 09:47:10,601 DEBUG: View 1 : 0.660377358491
+2016-08-24 09:47:10,638 DEBUG: View 2 : 0.490566037736
+2016-08-24 09:47:10,646 DEBUG: View 3 : 0.553459119497
+2016-08-24 09:47:10,703 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:11,037 DEBUG: Start: Iteration 6
+2016-08-24 09:47:11,053 DEBUG: View 0 : 0.660377358491
+2016-08-24 09:47:11,061 DEBUG: View 1 : 0.396226415094
+2016-08-24 09:47:11,098 DEBUG: View 2 : 0.553459119497
+2016-08-24 09:47:11,106 DEBUG: View 3 : 0.540880503145
+2016-08-24 09:47:11,167 DEBUG: Best view : Methyl_
+2016-08-24 09:47:11,562 DEBUG: Start: Iteration 7
+2016-08-24 09:47:11,578 DEBUG: View 0 : 0.339622641509
+2016-08-24 09:47:11,586 DEBUG: View 1 : 0.62893081761
+2016-08-24 09:47:11,623 DEBUG: View 2 : 0.566037735849
+2016-08-24 09:47:11,630 DEBUG: View 3 : 0.465408805031
+2016-08-24 09:47:11,692 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:12,144 DEBUG: Start: Iteration 8
+2016-08-24 09:47:12,160 DEBUG: View 0 : 0.471698113208
+2016-08-24 09:47:12,168 DEBUG: View 1 : 0.654088050314
+2016-08-24 09:47:12,205 DEBUG: View 2 : 0.383647798742
+2016-08-24 09:47:12,212 DEBUG: View 3 : 0.59748427673
+2016-08-24 09:47:12,276 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:12,787 DEBUG: Start: Iteration 9
+2016-08-24 09:47:12,803 DEBUG: View 0 : 0.622641509434
+2016-08-24 09:47:12,811 DEBUG: View 1 : 0.383647798742
+2016-08-24 09:47:12,848 DEBUG: View 2 : 0.389937106918
+2016-08-24 09:47:12,856 DEBUG: View 3 : 0.584905660377
+2016-08-24 09:47:12,923 DEBUG: Best view : Methyl_
+2016-08-24 09:47:13,494 DEBUG: Start: Iteration 10
+2016-08-24 09:47:13,511 DEBUG: View 0 : 0.433962264151
+2016-08-24 09:47:13,519 DEBUG: View 1 : 0.685534591195
+2016-08-24 09:47:13,556 DEBUG: View 2 : 0.452830188679
+2016-08-24 09:47:13,563 DEBUG: View 3 : 0.40251572327
+2016-08-24 09:47:13,632 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:14,261 DEBUG: Start: Iteration 11
+2016-08-24 09:47:14,278 DEBUG: View 0 : 0.584905660377
+2016-08-24 09:47:14,286 DEBUG: View 1 : 0.566037735849
+2016-08-24 09:47:14,322 DEBUG: View 2 : 0.477987421384
+2016-08-24 09:47:14,330 DEBUG: View 3 : 0.440251572327
+2016-08-24 09:47:14,401 DEBUG: Best view : Methyl_
+2016-08-24 09:47:15,092 DEBUG: Start: Iteration 12
+2016-08-24 09:47:15,109 DEBUG: View 0 : 0.301886792453
+2016-08-24 09:47:15,116 DEBUG: View 1 : 0.37106918239
+2016-08-24 09:47:15,153 DEBUG: View 2 : 0.59748427673
+2016-08-24 09:47:15,160 DEBUG: View 3 : 0.528301886792
+2016-08-24 09:47:15,233 DEBUG: Best view : RANSeq_
+2016-08-24 09:47:16,016 DEBUG: Start: Iteration 13
+2016-08-24 09:47:16,033 DEBUG: View 0 : 0.427672955975
+2016-08-24 09:47:16,040 DEBUG: View 1 : 0.660377358491
+2016-08-24 09:47:16,077 DEBUG: View 2 : 0.377358490566
+2016-08-24 09:47:16,084 DEBUG: View 3 : 0.452830188679
+2016-08-24 09:47:16,162 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:17,170 DEBUG: Start: Iteration 14
+2016-08-24 09:47:17,190 DEBUG: View 0 : 0.622641509434
+2016-08-24 09:47:17,199 DEBUG: View 1 : 0.641509433962
+2016-08-24 09:47:17,242 DEBUG: View 2 : 0.584905660377
+2016-08-24 09:47:17,251 DEBUG: View 3 : 0.528301886792
+2016-08-24 09:47:17,335 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:18,276 DEBUG: Start: Iteration 15
+2016-08-24 09:47:18,294 DEBUG: View 0 : 0.440251572327
+2016-08-24 09:47:18,302 DEBUG: View 1 : 0.283018867925
+2016-08-24 09:47:18,343 DEBUG: View 2 : 0.377358490566
+2016-08-24 09:47:18,351 DEBUG: View 3 : 0.522012578616
+2016-08-24 09:47:18,437 DEBUG: Best view : Clinic_
+2016-08-24 09:47:19,473 DEBUG: Start: Iteration 16
+2016-08-24 09:47:19,491 DEBUG: View 0 : 0.509433962264
+2016-08-24 09:47:19,499 DEBUG: View 1 : 0.522012578616
+2016-08-24 09:47:19,535 DEBUG: View 2 : 0.452830188679
+2016-08-24 09:47:19,543 DEBUG: View 3 : 0.40251572327
+2016-08-24 09:47:19,626 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:20,644 DEBUG: Start: Iteration 17
+2016-08-24 09:47:20,661 DEBUG: View 0 : 0.415094339623
+2016-08-24 09:47:20,669 DEBUG: View 1 : 0.452830188679
+2016-08-24 09:47:20,707 DEBUG: View 2 : 0.471698113208
+2016-08-24 09:47:20,714 DEBUG: View 3 : 0.48427672956
+2016-08-24 09:47:20,715 WARNING: WARNING: All bad for iteration 16
+2016-08-24 09:47:20,803 DEBUG: Best view : Clinic_
+2016-08-24 09:47:21,871 DEBUG: Start: Iteration 18
+2016-08-24 09:47:21,889 DEBUG: View 0 : 0.396226415094
+2016-08-24 09:47:21,897 DEBUG: View 1 : 0.62893081761
+2016-08-24 09:47:21,935 DEBUG: View 2 : 0.553459119497
+2016-08-24 09:47:21,942 DEBUG: View 3 : 0.534591194969
+2016-08-24 09:47:22,033 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:23,205 DEBUG: Start: Iteration 19
+2016-08-24 09:47:23,222 DEBUG: View 0 : 0.415094339623
+2016-08-24 09:47:23,230 DEBUG: View 1 : 0.710691823899
+2016-08-24 09:47:23,268 DEBUG: View 2 : 0.660377358491
+2016-08-24 09:47:23,276 DEBUG: View 3 : 0.534591194969
+2016-08-24 09:47:23,370 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:24,573 DEBUG: Start: Iteration 20
+2016-08-24 09:47:24,590 DEBUG: View 0 : 0.459119496855
+2016-08-24 09:47:24,598 DEBUG: View 1 : 0.616352201258
+2016-08-24 09:47:24,636 DEBUG: View 2 : 0.465408805031
+2016-08-24 09:47:24,643 DEBUG: View 3 : 0.415094339623
+2016-08-24 09:47:24,737 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:25,998 DEBUG: Start: Iteration 21
+2016-08-24 09:47:26,016 DEBUG: View 0 : 0.48427672956
+2016-08-24 09:47:26,024 DEBUG: View 1 : 0.616352201258
+2016-08-24 09:47:26,063 DEBUG: View 2 : 0.415094339623
+2016-08-24 09:47:26,071 DEBUG: View 3 : 0.553459119497
+2016-08-24 09:47:26,172 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:27,510 DEBUG: Start: Iteration 22
+2016-08-24 09:47:27,527 DEBUG: View 0 : 0.547169811321
+2016-08-24 09:47:27,534 DEBUG: View 1 : 0.584905660377
+2016-08-24 09:47:27,571 DEBUG: View 2 : 0.540880503145
+2016-08-24 09:47:27,579 DEBUG: View 3 : 0.440251572327
+2016-08-24 09:47:27,682 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:29,064 DEBUG: Start: Iteration 23
+2016-08-24 09:47:29,081 DEBUG: View 0 : 0.534591194969
+2016-08-24 09:47:29,088 DEBUG: View 1 : 0.704402515723
+2016-08-24 09:47:29,125 DEBUG: View 2 : 0.48427672956
+2016-08-24 09:47:29,132 DEBUG: View 3 : 0.471698113208
+2016-08-24 09:47:29,232 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:30,679 DEBUG: Start: Iteration 24
+2016-08-24 09:47:30,696 DEBUG: View 0 : 0.679245283019
+2016-08-24 09:47:30,704 DEBUG: View 1 : 0.691823899371
+2016-08-24 09:47:30,742 DEBUG: View 2 : 0.559748427673
+2016-08-24 09:47:30,750 DEBUG: View 3 : 0.471698113208
+2016-08-24 09:47:30,856 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:32,537 DEBUG: Start: Iteration 25
+2016-08-24 09:47:32,554 DEBUG: View 0 : 0.465408805031
+2016-08-24 09:47:32,562 DEBUG: View 1 : 0.452830188679
+2016-08-24 09:47:32,599 DEBUG: View 2 : 0.40251572327
+2016-08-24 09:47:32,606 DEBUG: View 3 : 0.540880503145
+2016-08-24 09:47:32,710 DEBUG: Best view : Clinic_
+2016-08-24 09:47:34,272 DEBUG: Start: Iteration 26
+2016-08-24 09:47:34,292 DEBUG: View 0 : 0.446540880503
+2016-08-24 09:47:34,303 DEBUG: View 1 : 0.389937106918
+2016-08-24 09:47:34,343 DEBUG: View 2 : 0.509433962264
+2016-08-24 09:47:34,351 DEBUG: View 3 : 0.553459119497
+2016-08-24 09:47:34,466 DEBUG: Best view : Clinic_
+2016-08-24 09:47:36,204 DEBUG: Start: Iteration 27
+2016-08-24 09:47:36,223 DEBUG: View 0 : 0.522012578616
+2016-08-24 09:47:36,234 DEBUG: View 1 : 0.685534591195
+2016-08-24 09:47:36,278 DEBUG: View 2 : 0.578616352201
+2016-08-24 09:47:36,287 DEBUG: View 3 : 0.496855345912
+2016-08-24 09:47:36,415 DEBUG: Best view : MiRNA__
+2016-08-24 09:47:38,235 DEBUG: Start: Iteration 28
+2016-08-24 09:47:38,252 DEBUG: View 0 : 0.660377358491
+2016-08-24 09:47:38,260 DEBUG: View 1 : 0.679245283019
+2016-08-24 09:47:38,297 DEBUG: View 2 : 0.559748427673
+2016-08-24 09:47:38,305 DEBUG: View 3 : 0.641509433962
+2016-08-24 09:47:38,415 DEBUG: Best view : MiRNA__
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-094740-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-094740-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..34b92ceb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-094740-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,15348 @@
+2016-08-24 09:47:40,713 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 09:47:40,714 INFO: Info: Labels used: No, Yes
+2016-08-24 09:47:40,714 INFO: Info: Length of dataset:347
+2016-08-24 09:47:40,716 INFO: ### Main Programm for Multiview Classification
+2016-08-24 09:47:40,716 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 09:47:40,716 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 09:47:40,717 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 09:47:40,717 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 09:47:40,718 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 09:47:40,718 INFO: Done: Read Database Files
+2016-08-24 09:47:40,718 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 09:47:40,721 INFO: Done: Determine validation split
+2016-08-24 09:47:40,721 INFO: Start: Determine 2 folds
+2016-08-24 09:47:40,733 INFO: Info: Length of Learning Sets: 122
+2016-08-24 09:47:40,733 INFO: Info: Length of Testing Sets: 122
+2016-08-24 09:47:40,733 INFO: Info: Length of Validation Set: 103
+2016-08-24 09:47:40,733 INFO: Done: Determine folds
+2016-08-24 09:47:40,733 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 09:47:40,733 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 09:47:40,733 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 09:47:48,128 DEBUG: 0.589798270893Poulet
+2016-08-24 09:47:48,128 DEBUG: 0.521498559078Poulet
+2016-08-24 09:47:48,128 DEBUG: 0.521556195965Poulet
+2016-08-24 09:47:48,128 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:47:48,129 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 09:47:50,083 DEBUG: 0.585360230548Poulet
+2016-08-24 09:47:50,083 DEBUG: 0.573025936599Poulet
+2016-08-24 09:47:50,083 DEBUG: 0.55613832853Poulet
+2016-08-24 09:47:50,083 DEBUG: 0.54507204611Poulet
+2016-08-24 09:47:50,083 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:47:50,084 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 09:48:06,845 DEBUG: 0.559365994236Poulet
+2016-08-24 09:48:06,845 DEBUG: 0.530201729107Poulet
+2016-08-24 09:48:06,846 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:48:06,847 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 09:48:08,580 DEBUG: 0.584034582133Poulet
+2016-08-24 09:48:08,580 DEBUG: 0.556945244957Poulet
+2016-08-24 09:48:08,581 DEBUG: 0.508876080692Poulet
+2016-08-24 09:48:08,581 DEBUG: 0.523170028818Poulet
+2016-08-24 09:48:08,581 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:48:08,581 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 09:49:01,049 DEBUG: 0.563976945245Poulet
+2016-08-24 09:49:01,050 DEBUG: 0.553371757925Poulet
+2016-08-24 09:49:01,050 DEBUG: 0.501268011527Poulet
+2016-08-24 09:49:01,055 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 09:49:01,055 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 09:49:01,055 INFO: Start: Fold number 1
+2016-08-24 09:49:03,043 DEBUG: Start: Iteration 1
+2016-08-24 09:49:03,080 DEBUG: View 0 : 0.601226993865
+2016-08-24 09:49:03,088 DEBUG: View 1 : 0.631901840491
+2016-08-24 09:49:03,116 DEBUG: View 2 : 0.368098159509
+2016-08-24 09:49:03,124 DEBUG: View 3 : 0.368098159509
+2016-08-24 09:49:03,167 DEBUG: Best view : Methyl_
+2016-08-24 09:49:03,245 DEBUG: Start: Iteration 2
+2016-08-24 09:49:03,262 DEBUG: View 0 : 0.558282208589
+2016-08-24 09:49:03,270 DEBUG: View 1 : 0.625766871166
+2016-08-24 09:49:03,307 DEBUG: View 2 : 0.570552147239
+2016-08-24 09:49:03,315 DEBUG: View 3 : 0.466257668712
+2016-08-24 09:49:03,362 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:03,499 DEBUG: Start: Iteration 3
+2016-08-24 09:49:03,516 DEBUG: View 0 : 0.460122699387
+2016-08-24 09:49:03,524 DEBUG: View 1 : 0.680981595092
+2016-08-24 09:49:03,561 DEBUG: View 2 : 0.435582822086
+2016-08-24 09:49:03,569 DEBUG: View 3 : 0.625766871166
+2016-08-24 09:49:03,624 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:03,821 DEBUG: Start: Iteration 4
+2016-08-24 09:49:03,838 DEBUG: View 0 : 0.503067484663
+2016-08-24 09:49:03,846 DEBUG: View 1 : 0.546012269939
+2016-08-24 09:49:03,883 DEBUG: View 2 : 0.429447852761
+2016-08-24 09:49:03,890 DEBUG: View 3 : 0.533742331288
+2016-08-24 09:49:03,947 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:04,204 DEBUG: Start: Iteration 5
+2016-08-24 09:49:04,221 DEBUG: View 0 : 0.662576687117
+2016-08-24 09:49:04,228 DEBUG: View 1 : 0.674846625767
+2016-08-24 09:49:04,266 DEBUG: View 2 : 0.466257668712
+2016-08-24 09:49:04,273 DEBUG: View 3 : 0.429447852761
+2016-08-24 09:49:04,332 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:04,648 DEBUG: Start: Iteration 6
+2016-08-24 09:49:04,665 DEBUG: View 0 : 0.398773006135
+2016-08-24 09:49:04,673 DEBUG: View 1 : 0.527607361963
+2016-08-24 09:49:04,710 DEBUG: View 2 : 0.552147239264
+2016-08-24 09:49:04,717 DEBUG: View 3 : 0.625766871166
+2016-08-24 09:49:04,779 DEBUG: Best view : Clinic_
+2016-08-24 09:49:05,154 DEBUG: Start: Iteration 7
+2016-08-24 09:49:05,170 DEBUG: View 0 : 0.680981595092
+2016-08-24 09:49:05,178 DEBUG: View 1 : 0.478527607362
+2016-08-24 09:49:05,215 DEBUG: View 2 : 0.478527607362
+2016-08-24 09:49:05,223 DEBUG: View 3 : 0.429447852761
+2016-08-24 09:49:05,287 DEBUG: Best view : Methyl_
+2016-08-24 09:49:05,725 DEBUG: Start: Iteration 8
+2016-08-24 09:49:05,741 DEBUG: View 0 : 0.527607361963
+2016-08-24 09:49:05,749 DEBUG: View 1 : 0.429447852761
+2016-08-24 09:49:05,787 DEBUG: View 2 : 0.539877300613
+2016-08-24 09:49:05,795 DEBUG: View 3 : 0.472392638037
+2016-08-24 09:49:05,861 DEBUG: Best view : Methyl_
+2016-08-24 09:49:06,363 DEBUG: Start: Iteration 9
+2016-08-24 09:49:06,380 DEBUG: View 0 : 0.460122699387
+2016-08-24 09:49:06,388 DEBUG: View 1 : 0.546012269939
+2016-08-24 09:49:06,425 DEBUG: View 2 : 0.490797546012
+2016-08-24 09:49:06,433 DEBUG: View 3 : 0.398773006135
+2016-08-24 09:49:06,501 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:07,061 DEBUG: Start: Iteration 10
+2016-08-24 09:49:07,077 DEBUG: View 0 : 0.558282208589
+2016-08-24 09:49:07,085 DEBUG: View 1 : 0.60736196319
+2016-08-24 09:49:07,123 DEBUG: View 2 : 0.41717791411
+2016-08-24 09:49:07,130 DEBUG: View 3 : 0.411042944785
+2016-08-24 09:49:07,201 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:07,822 DEBUG: Start: Iteration 11
+2016-08-24 09:49:07,838 DEBUG: View 0 : 0.638036809816
+2016-08-24 09:49:07,846 DEBUG: View 1 : 0.40490797546
+2016-08-24 09:49:07,884 DEBUG: View 2 : 0.60736196319
+2016-08-24 09:49:07,891 DEBUG: View 3 : 0.429447852761
+2016-08-24 09:49:07,965 DEBUG: Best view : Methyl_
+2016-08-24 09:49:08,649 DEBUG: Start: Iteration 12
+2016-08-24 09:49:08,665 DEBUG: View 0 : 0.564417177914
+2016-08-24 09:49:08,673 DEBUG: View 1 : 0.429447852761
+2016-08-24 09:49:08,710 DEBUG: View 2 : 0.570552147239
+2016-08-24 09:49:08,718 DEBUG: View 3 : 0.601226993865
+2016-08-24 09:49:08,794 DEBUG: Best view : Methyl_
+2016-08-24 09:49:09,540 DEBUG: Start: Iteration 13
+2016-08-24 09:49:09,557 DEBUG: View 0 : 0.576687116564
+2016-08-24 09:49:09,565 DEBUG: View 1 : 0.625766871166
+2016-08-24 09:49:09,604 DEBUG: View 2 : 0.484662576687
+2016-08-24 09:49:09,612 DEBUG: View 3 : 0.552147239264
+2016-08-24 09:49:09,691 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:10,498 DEBUG: Start: Iteration 14
+2016-08-24 09:49:10,515 DEBUG: View 0 : 0.39263803681
+2016-08-24 09:49:10,522 DEBUG: View 1 : 0.60736196319
+2016-08-24 09:49:10,562 DEBUG: View 2 : 0.40490797546
+2016-08-24 09:49:10,570 DEBUG: View 3 : 0.552147239264
+2016-08-24 09:49:10,650 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:11,517 DEBUG: Start: Iteration 15
+2016-08-24 09:49:11,534 DEBUG: View 0 : 0.59509202454
+2016-08-24 09:49:11,541 DEBUG: View 1 : 0.288343558282
+2016-08-24 09:49:11,581 DEBUG: View 2 : 0.576687116564
+2016-08-24 09:49:11,589 DEBUG: View 3 : 0.41717791411
+2016-08-24 09:49:11,672 DEBUG: Best view : Methyl_
+2016-08-24 09:49:12,603 DEBUG: Start: Iteration 16
+2016-08-24 09:49:12,619 DEBUG: View 0 : 0.38036809816
+2016-08-24 09:49:12,627 DEBUG: View 1 : 0.58282208589
+2016-08-24 09:49:12,667 DEBUG: View 2 : 0.515337423313
+2016-08-24 09:49:12,675 DEBUG: View 3 : 0.466257668712
+2016-08-24 09:49:12,760 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:13,748 DEBUG: Start: Iteration 17
+2016-08-24 09:49:13,764 DEBUG: View 0 : 0.441717791411
+2016-08-24 09:49:13,772 DEBUG: View 1 : 0.374233128834
+2016-08-24 09:49:13,812 DEBUG: View 2 : 0.478527607362
+2016-08-24 09:49:13,820 DEBUG: View 3 : 0.546012269939
+2016-08-24 09:49:13,907 DEBUG: Best view : Clinic_
+2016-08-24 09:49:14,953 DEBUG: Start: Iteration 18
+2016-08-24 09:49:14,970 DEBUG: View 0 : 0.650306748466
+2016-08-24 09:49:14,978 DEBUG: View 1 : 0.269938650307
+2016-08-24 09:49:15,018 DEBUG: View 2 : 0.644171779141
+2016-08-24 09:49:15,026 DEBUG: View 3 : 0.398773006135
+2016-08-24 09:49:15,115 DEBUG: Best view : Methyl_
+2016-08-24 09:49:16,227 DEBUG: Start: Iteration 19
+2016-08-24 09:49:16,244 DEBUG: View 0 : 0.662576687117
+2016-08-24 09:49:16,252 DEBUG: View 1 : 0.398773006135
+2016-08-24 09:49:16,291 DEBUG: View 2 : 0.411042944785
+2016-08-24 09:49:16,299 DEBUG: View 3 : 0.613496932515
+2016-08-24 09:49:16,391 DEBUG: Best view : Methyl_
+2016-08-24 09:49:17,580 DEBUG: Start: Iteration 20
+2016-08-24 09:49:17,597 DEBUG: View 0 : 0.588957055215
+2016-08-24 09:49:17,605 DEBUG: View 1 : 0.429447852761
+2016-08-24 09:49:17,645 DEBUG: View 2 : 0.601226993865
+2016-08-24 09:49:17,653 DEBUG: View 3 : 0.39263803681
+2016-08-24 09:49:17,747 DEBUG: Best view : Methyl_
+2016-08-24 09:49:18,983 DEBUG: Start: Iteration 21
+2016-08-24 09:49:19,000 DEBUG: View 0 : 0.41717791411
+2016-08-24 09:49:19,008 DEBUG: View 1 : 0.515337423313
+2016-08-24 09:49:19,048 DEBUG: View 2 : 0.515337423313
+2016-08-24 09:49:19,056 DEBUG: View 3 : 0.40490797546
+2016-08-24 09:49:19,152 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:20,448 DEBUG: Start: Iteration 22
+2016-08-24 09:49:20,464 DEBUG: View 0 : 0.39263803681
+2016-08-24 09:49:20,472 DEBUG: View 1 : 0.730061349693
+2016-08-24 09:49:20,511 DEBUG: View 2 : 0.558282208589
+2016-08-24 09:49:20,520 DEBUG: View 3 : 0.650306748466
+2016-08-24 09:49:20,619 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:21,975 DEBUG: Start: Iteration 23
+2016-08-24 09:49:21,992 DEBUG: View 0 : 0.527607361963
+2016-08-24 09:49:22,000 DEBUG: View 1 : 0.576687116564
+2016-08-24 09:49:22,039 DEBUG: View 2 : 0.631901840491
+2016-08-24 09:49:22,047 DEBUG: View 3 : 0.503067484663
+2016-08-24 09:49:22,149 DEBUG: Best view : RANSeq_
+2016-08-24 09:49:23,580 DEBUG: Start: Iteration 24
+2016-08-24 09:49:23,596 DEBUG: View 0 : 0.58282208589
+2016-08-24 09:49:23,604 DEBUG: View 1 : 0.533742331288
+2016-08-24 09:49:23,644 DEBUG: View 2 : 0.576687116564
+2016-08-24 09:49:23,653 DEBUG: View 3 : 0.447852760736
+2016-08-24 09:49:23,756 DEBUG: Best view : RANSeq_
+2016-08-24 09:49:25,260 DEBUG: Start: Iteration 25
+2016-08-24 09:49:25,277 DEBUG: View 0 : 0.58282208589
+2016-08-24 09:49:25,285 DEBUG: View 1 : 0.60736196319
+2016-08-24 09:49:25,324 DEBUG: View 2 : 0.423312883436
+2016-08-24 09:49:25,333 DEBUG: View 3 : 0.60736196319
+2016-08-24 09:49:25,439 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:26,999 DEBUG: Start: Iteration 26
+2016-08-24 09:49:27,016 DEBUG: View 0 : 0.588957055215
+2016-08-24 09:49:27,024 DEBUG: View 1 : 0.539877300613
+2016-08-24 09:49:27,063 DEBUG: View 2 : 0.546012269939
+2016-08-24 09:49:27,071 DEBUG: View 3 : 0.625766871166
+2016-08-24 09:49:27,179 DEBUG: Best view : Clinic_
+2016-08-24 09:49:28,797 DEBUG: Start: Iteration 27
+2016-08-24 09:49:28,814 DEBUG: View 0 : 0.552147239264
+2016-08-24 09:49:28,822 DEBUG: View 1 : 0.674846625767
+2016-08-24 09:49:28,862 DEBUG: View 2 : 0.662576687117
+2016-08-24 09:49:28,870 DEBUG: View 3 : 0.496932515337
+2016-08-24 09:49:28,980 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:30,657 DEBUG: Start: Iteration 28
+2016-08-24 09:49:30,674 DEBUG: View 0 : 0.472392638037
+2016-08-24 09:49:30,681 DEBUG: View 1 : 0.674846625767
+2016-08-24 09:49:30,721 DEBUG: View 2 : 0.588957055215
+2016-08-24 09:49:30,729 DEBUG: View 3 : 0.564417177914
+2016-08-24 09:49:30,841 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:32,609 DEBUG: Start: Iteration 29
+2016-08-24 09:49:32,625 DEBUG: View 0 : 0.717791411043
+2016-08-24 09:49:32,633 DEBUG: View 1 : 0.650306748466
+2016-08-24 09:49:32,673 DEBUG: View 2 : 0.546012269939
+2016-08-24 09:49:32,681 DEBUG: View 3 : 0.411042944785
+2016-08-24 09:49:32,796 DEBUG: Best view : Methyl_
+2016-08-24 09:49:34,618 DEBUG: Start: Iteration 30
+2016-08-24 09:49:34,635 DEBUG: View 0 : 0.41717791411
+2016-08-24 09:49:34,643 DEBUG: View 1 : 0.355828220859
+2016-08-24 09:49:34,684 DEBUG: View 2 : 0.521472392638
+2016-08-24 09:49:34,692 DEBUG: View 3 : 0.509202453988
+2016-08-24 09:49:34,811 DEBUG: Best view : RANSeq_
+2016-08-24 09:49:36,776 DEBUG: Start: Iteration 31
+2016-08-24 09:49:36,793 DEBUG: View 0 : 0.539877300613
+2016-08-24 09:49:36,801 DEBUG: View 1 : 0.386503067485
+2016-08-24 09:49:36,841 DEBUG: View 2 : 0.374233128834
+2016-08-24 09:49:36,850 DEBUG: View 3 : 0.533742331288
+2016-08-24 09:49:36,978 DEBUG: Best view : Methyl_
+2016-08-24 09:49:38,949 DEBUG: Start: Iteration 32
+2016-08-24 09:49:38,966 DEBUG: View 0 : 0.423312883436
+2016-08-24 09:49:38,974 DEBUG: View 1 : 0.478527607362
+2016-08-24 09:49:39,014 DEBUG: View 2 : 0.466257668712
+2016-08-24 09:49:39,022 DEBUG: View 3 : 0.631901840491
+2016-08-24 09:49:39,143 DEBUG: Best view : Clinic_
+2016-08-24 09:49:41,144 DEBUG: Start: Iteration 33
+2016-08-24 09:49:41,161 DEBUG: View 0 : 0.515337423313
+2016-08-24 09:49:41,169 DEBUG: View 1 : 0.509202453988
+2016-08-24 09:49:41,208 DEBUG: View 2 : 0.447852760736
+2016-08-24 09:49:41,217 DEBUG: View 3 : 0.460122699387
+2016-08-24 09:49:41,341 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:43,399 DEBUG: Start: Iteration 34
+2016-08-24 09:49:43,415 DEBUG: View 0 : 0.601226993865
+2016-08-24 09:49:43,423 DEBUG: View 1 : 0.613496932515
+2016-08-24 09:49:43,463 DEBUG: View 2 : 0.435582822086
+2016-08-24 09:49:43,471 DEBUG: View 3 : 0.39263803681
+2016-08-24 09:49:43,596 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:45,710 DEBUG: Start: Iteration 35
+2016-08-24 09:49:45,726 DEBUG: View 0 : 0.521472392638
+2016-08-24 09:49:45,734 DEBUG: View 1 : 0.503067484663
+2016-08-24 09:49:45,774 DEBUG: View 2 : 0.386503067485
+2016-08-24 09:49:45,782 DEBUG: View 3 : 0.411042944785
+2016-08-24 09:49:45,910 DEBUG: Best view : Methyl_
+2016-08-24 09:49:48,090 DEBUG: Start: Iteration 36
+2016-08-24 09:49:48,107 DEBUG: View 0 : 0.539877300613
+2016-08-24 09:49:48,115 DEBUG: View 1 : 0.478527607362
+2016-08-24 09:49:48,155 DEBUG: View 2 : 0.576687116564
+2016-08-24 09:49:48,163 DEBUG: View 3 : 0.539877300613
+2016-08-24 09:49:48,293 DEBUG: Best view : RANSeq_
+2016-08-24 09:49:50,546 DEBUG: Start: Iteration 37
+2016-08-24 09:49:50,562 DEBUG: View 0 : 0.447852760736
+2016-08-24 09:49:50,570 DEBUG: View 1 : 0.705521472393
+2016-08-24 09:49:50,610 DEBUG: View 2 : 0.509202453988
+2016-08-24 09:49:50,618 DEBUG: View 3 : 0.570552147239
+2016-08-24 09:49:50,750 DEBUG: Best view : MiRNA__
+2016-08-24 09:49:53,060 DEBUG: Start: Iteration 38
+2016-08-24 09:49:53,077 DEBUG: View 0 : 0.509202453988
+2016-08-24 09:49:53,085 DEBUG: View 1 : 0.558282208589
+2016-08-24 09:49:53,124 DEBUG: View 2 : 0.423312883436
+2016-08-24 09:49:53,133 DEBUG: View 3 : 0.61963190184
+2016-08-24 09:49:53,267 DEBUG: Best view : Clinic_
+2016-08-24 09:49:55,637 DEBUG: Start: Iteration 39
+2016-08-24 09:49:55,653 DEBUG: View 0 : 0.337423312883
+2016-08-24 09:49:55,661 DEBUG: View 1 : 0.38036809816
+2016-08-24 09:49:55,701 DEBUG: View 2 : 0.423312883436
+2016-08-24 09:49:55,709 DEBUG: View 3 : 0.601226993865
+2016-08-24 09:49:55,846 DEBUG: Best view : Clinic_
+2016-08-24 09:49:58,274 DEBUG: Start: Iteration 40
+2016-08-24 09:49:58,290 DEBUG: View 0 : 0.60736196319
+2016-08-24 09:49:58,298 DEBUG: View 1 : 0.509202453988
+2016-08-24 09:49:58,338 DEBUG: View 2 : 0.631901840491
+2016-08-24 09:49:58,346 DEBUG: View 3 : 0.361963190184
+2016-08-24 09:49:58,485 DEBUG: Best view : Methyl_
+2016-08-24 09:50:00,977 DEBUG: Start: Iteration 41
+2016-08-24 09:50:00,993 DEBUG: View 0 : 0.496932515337
+2016-08-24 09:50:01,001 DEBUG: View 1 : 0.58282208589
+2016-08-24 09:50:01,042 DEBUG: View 2 : 0.411042944785
+2016-08-24 09:50:01,050 DEBUG: View 3 : 0.429447852761
+2016-08-24 09:50:01,192 DEBUG: Best view : MiRNA__
+2016-08-24 09:50:03,745 DEBUG: Start: Iteration 42
+2016-08-24 09:50:03,761 DEBUG: View 0 : 0.521472392638
+2016-08-24 09:50:03,769 DEBUG: View 1 : 0.650306748466
+2016-08-24 09:50:03,810 DEBUG: View 2 : 0.625766871166
+2016-08-24 09:50:03,818 DEBUG: View 3 : 0.539877300613
+2016-08-24 09:50:03,961 DEBUG: Best view : MiRNA__
+2016-08-24 09:50:06,572 DEBUG: Start: Iteration 43
+2016-08-24 09:50:06,589 DEBUG: View 0 : 0.521472392638
+2016-08-24 09:50:06,597 DEBUG: View 1 : 0.423312883436
+2016-08-24 09:50:06,637 DEBUG: View 2 : 0.441717791411
+2016-08-24 09:50:06,646 DEBUG: View 3 : 0.558282208589
+2016-08-24 09:50:06,792 DEBUG: Best view : Methyl_
+2016-08-24 09:50:09,466 DEBUG: Start: Iteration 44
+2016-08-24 09:50:09,483 DEBUG: View 0 : 0.466257668712
+2016-08-24 09:50:09,491 DEBUG: View 1 : 0.472392638037
+2016-08-24 09:50:09,532 DEBUG: View 2 : 0.441717791411
+2016-08-24 09:50:09,541 DEBUG: View 3 : 0.398773006135
+2016-08-24 09:50:09,541 WARNING: WARNING: All bad for iteration 43
+2016-08-24 09:50:09,689 DEBUG: Best view : MiRNA__
+2016-08-24 09:50:12,423 DEBUG: Start: Iteration 45
+2016-08-24 09:50:12,440 DEBUG: View 0 : 0.38036809816
+2016-08-24 09:50:12,447 DEBUG: View 1 : 0.558282208589
+2016-08-24 09:50:12,489 DEBUG: View 2 : 0.411042944785
+2016-08-24 09:50:12,498 DEBUG: View 3 : 0.625766871166
+2016-08-24 09:50:12,649 DEBUG: Best view : Clinic_
+2016-08-24 09:50:15,438 DEBUG: Start: Iteration 46
+2016-08-24 09:50:15,455 DEBUG: View 0 : 0.730061349693
+2016-08-24 09:50:15,463 DEBUG: View 1 : 0.374233128834
+2016-08-24 09:50:15,505 DEBUG: View 2 : 0.576687116564
+2016-08-24 09:50:15,514 DEBUG: View 3 : 0.613496932515
+2016-08-24 09:50:15,667 DEBUG: Best view : Methyl_
+2016-08-24 09:50:18,525 DEBUG: Start: Iteration 47
+2016-08-24 09:50:18,542 DEBUG: View 0 : 0.423312883436
+2016-08-24 09:50:18,549 DEBUG: View 1 : 0.656441717791
+2016-08-24 09:50:18,591 DEBUG: View 2 : 0.429447852761
+2016-08-24 09:50:18,600 DEBUG: View 3 : 0.460122699387
+2016-08-24 09:50:18,756 DEBUG: Best view : MiRNA__
+2016-08-24 09:50:21,672 DEBUG: Start: Iteration 48
+2016-08-24 09:50:21,688 DEBUG: View 0 : 0.355828220859
+2016-08-24 09:50:21,696 DEBUG: View 1 : 0.39263803681
+2016-08-24 09:50:21,740 DEBUG: View 2 : 0.411042944785
+2016-08-24 09:50:21,749 DEBUG: View 3 : 0.435582822086
+2016-08-24 09:50:21,749 WARNING: WARNING: All bad for iteration 47
+2016-08-24 09:50:21,907 DEBUG: Best view : Clinic_
+2016-08-24 09:50:24,889 DEBUG: Start: Iteration 49
+2016-08-24 09:50:24,906 DEBUG: View 0 : 0.521472392638
+2016-08-24 09:50:24,913 DEBUG: View 1 : 0.680981595092
+2016-08-24 09:50:24,957 DEBUG: View 2 : 0.38036809816
+2016-08-24 09:50:24,966 DEBUG: View 3 : 0.558282208589
+2016-08-24 09:50:25,137 DEBUG: Best view : MiRNA__
+2016-08-24 09:50:28,192 DEBUG: Start: Iteration 50
+2016-08-24 09:50:28,209 DEBUG: View 0 : 0.40490797546
+2016-08-24 09:50:28,217 DEBUG: View 1 : 0.650306748466
+2016-08-24 09:50:28,261 DEBUG: View 2 : 0.625766871166
+2016-08-24 09:50:28,270 DEBUG: View 3 : 0.429447852761
+2016-08-24 09:50:28,433 DEBUG: Best view : MiRNA__
+2016-08-24 09:50:31,590 DEBUG: Start: Iteration 51
+2016-08-24 09:50:31,607 DEBUG: View 0 : 0.40490797546
+2016-08-24 09:50:31,615 DEBUG: View 1 : 0.447852760736
+2016-08-24 09:50:31,660 DEBUG: View 2 : 0.490797546012
+2016-08-24 09:50:31,669 DEBUG: View 3 : 0.644171779141
+2016-08-24 09:50:31,836 DEBUG: Best view : Clinic_
+2016-08-24 09:50:35,243 DEBUG: Start: Iteration 52
+2016-08-24 09:50:35,259 DEBUG: View 0 : 0.588957055215
+2016-08-24 09:50:35,267 DEBUG: View 1 : 0.38036809816
+2016-08-24 09:50:35,313 DEBUG: View 2 : 0.509202453988
+2016-08-24 09:50:35,322 DEBUG: View 3 : 0.564417177914
+2016-08-24 09:50:35,492 DEBUG: Best view : Methyl_
+2016-08-24 09:50:38,719 DEBUG: Start: Iteration 53
+2016-08-24 09:50:38,736 DEBUG: View 0 : 0.521472392638
+2016-08-24 09:50:38,744 DEBUG: View 1 : 0.263803680982
+2016-08-24 09:50:38,789 DEBUG: View 2 : 0.453987730061
+2016-08-24 09:50:38,798 DEBUG: View 3 : 0.41717791411
+2016-08-24 09:50:38,970 DEBUG: Best view : Methyl_
+2016-08-24 09:50:42,262 DEBUG: Start: Iteration 54
+2016-08-24 09:50:42,278 DEBUG: View 0 : 0.546012269939
+2016-08-24 09:50:42,286 DEBUG: View 1 : 0.460122699387
+2016-08-24 09:50:42,334 DEBUG: View 2 : 0.539877300613
+2016-08-24 09:50:42,343 DEBUG: View 3 : 0.564417177914
+2016-08-24 09:50:42,517 DEBUG: Best view : Clinic_
+2016-08-24 09:50:45,859 DEBUG: Start: Iteration 55
+2016-08-24 09:50:45,876 DEBUG: View 0 : 0.38036809816
+2016-08-24 09:50:45,884 DEBUG: View 1 : 0.374233128834
+2016-08-24 09:50:45,932 DEBUG: View 2 : 0.484662576687
+2016-08-24 09:50:45,942 DEBUG: View 3 : 0.60736196319
+2016-08-24 09:50:46,117 DEBUG: Best view : Clinic_
+2016-08-24 09:50:49,519 DEBUG: Start: Iteration 56
+2016-08-24 09:50:49,536 DEBUG: View 0 : 0.564417177914
+2016-08-24 09:50:49,543 DEBUG: View 1 : 0.736196319018
+2016-08-24 09:50:49,592 DEBUG: View 2 : 0.515337423313
+2016-08-24 09:50:49,601 DEBUG: View 3 : 0.527607361963
+2016-08-24 09:50:49,778 DEBUG: Best view : MiRNA__
+2016-08-24 09:50:53,243 DEBUG: Start: Iteration 57
+2016-08-24 09:50:53,259 DEBUG: View 0 : 0.668711656442
+2016-08-24 09:50:53,267 DEBUG: View 1 : 0.687116564417
+2016-08-24 09:50:53,315 DEBUG: View 2 : 0.631901840491
+2016-08-24 09:50:53,325 DEBUG: View 3 : 0.576687116564
+2016-08-24 09:50:53,505 DEBUG: Best view : MiRNA__
+2016-08-24 09:50:57,031 DEBUG: Start: Iteration 58
+2016-08-24 09:50:57,047 DEBUG: View 0 : 0.466257668712
+2016-08-24 09:50:57,055 DEBUG: View 1 : 0.650306748466
+2016-08-24 09:50:57,104 DEBUG: View 2 : 0.503067484663
+2016-08-24 09:50:57,113 DEBUG: View 3 : 0.61963190184
+2016-08-24 09:50:57,296 DEBUG: Best view : MiRNA__
+2016-08-24 09:51:00,872 DEBUG: Start: Iteration 59
+2016-08-24 09:51:00,889 DEBUG: View 0 : 0.423312883436
+2016-08-24 09:51:00,897 DEBUG: View 1 : 0.472392638037
+2016-08-24 09:51:00,945 DEBUG: View 2 : 0.453987730061
+2016-08-24 09:51:00,954 DEBUG: View 3 : 0.39263803681
+2016-08-24 09:51:00,954 WARNING: WARNING: All bad for iteration 58
+2016-08-24 09:51:01,138 DEBUG: Best view : RANSeq_
+2016-08-24 09:51:04,914 DEBUG: Start: Iteration 60
+2016-08-24 09:51:04,931 DEBUG: View 0 : 0.570552147239
+2016-08-24 09:51:04,939 DEBUG: View 1 : 0.355828220859
+2016-08-24 09:51:04,987 DEBUG: View 2 : 0.552147239264
+2016-08-24 09:51:04,997 DEBUG: View 3 : 0.570552147239
+2016-08-24 09:51:05,188 DEBUG: Best view : Methyl_
+2016-08-24 09:51:08,910 DEBUG: Start: Iteration 61
+2016-08-24 09:51:08,927 DEBUG: View 0 : 0.58282208589
+2016-08-24 09:51:08,934 DEBUG: View 1 : 0.319018404908
+2016-08-24 09:51:08,981 DEBUG: View 2 : 0.374233128834
+2016-08-24 09:51:08,990 DEBUG: View 3 : 0.558282208589
+2016-08-24 09:51:09,179 DEBUG: Best view : Methyl_
+2016-08-24 09:51:12,965 DEBUG: Start: Iteration 62
+2016-08-24 09:51:12,981 DEBUG: View 0 : 0.570552147239
+2016-08-24 09:51:12,989 DEBUG: View 1 : 0.558282208589
+2016-08-24 09:51:13,036 DEBUG: View 2 : 0.503067484663
+2016-08-24 09:51:13,046 DEBUG: View 3 : 0.539877300613
+2016-08-24 09:51:13,236 DEBUG: Best view : Methyl_
+2016-08-24 09:51:17,077 DEBUG: Start: Iteration 63
+2016-08-24 09:51:17,094 DEBUG: View 0 : 0.441717791411
+2016-08-24 09:51:17,102 DEBUG: View 1 : 0.61963190184
+2016-08-24 09:51:17,150 DEBUG: View 2 : 0.472392638037
+2016-08-24 09:51:17,159 DEBUG: View 3 : 0.447852760736
+2016-08-24 09:51:17,355 DEBUG: Best view : MiRNA__
+2016-08-24 09:51:21,260 DEBUG: Start: Iteration 64
+2016-08-24 09:51:21,276 DEBUG: View 0 : 0.631901840491
+2016-08-24 09:51:21,284 DEBUG: View 1 : 0.631901840491
+2016-08-24 09:51:21,333 DEBUG: View 2 : 0.521472392638
+2016-08-24 09:51:21,342 DEBUG: View 3 : 0.472392638037
+2016-08-24 09:51:21,536 DEBUG: Best view : Methyl_
+2016-08-24 09:51:25,514 DEBUG: Start: Iteration 65
+2016-08-24 09:51:25,531 DEBUG: View 0 : 0.558282208589
+2016-08-24 09:51:25,538 DEBUG: View 1 : 0.472392638037
+2016-08-24 09:51:25,586 DEBUG: View 2 : 0.386503067485
+2016-08-24 09:51:25,595 DEBUG: View 3 : 0.613496932515
+2016-08-24 09:51:25,791 DEBUG: Best view : Clinic_
+2016-08-24 09:51:29,825 DEBUG: Start: Iteration 66
+2016-08-24 09:51:29,842 DEBUG: View 0 : 0.546012269939
+2016-08-24 09:51:29,850 DEBUG: View 1 : 0.466257668712
+2016-08-24 09:51:29,898 DEBUG: View 2 : 0.503067484663
+2016-08-24 09:51:29,907 DEBUG: View 3 : 0.374233128834
+2016-08-24 09:51:30,105 DEBUG: Best view : Methyl_
+2016-08-24 09:51:34,197 DEBUG: Start: Iteration 67
+2016-08-24 09:51:34,214 DEBUG: View 0 : 0.509202453988
+2016-08-24 09:51:34,222 DEBUG: View 1 : 0.625766871166
+2016-08-24 09:51:34,270 DEBUG: View 2 : 0.558282208589
+2016-08-24 09:51:34,279 DEBUG: View 3 : 0.60736196319
+2016-08-24 09:51:34,480 DEBUG: Best view : MiRNA__
+2016-08-24 09:51:38,632 DEBUG: Start: Iteration 68
+2016-08-24 09:51:38,649 DEBUG: View 0 : 0.662576687117
+2016-08-24 09:51:38,657 DEBUG: View 1 : 0.662576687117
+2016-08-24 09:51:38,704 DEBUG: View 2 : 0.478527607362
+2016-08-24 09:51:38,713 DEBUG: View 3 : 0.447852760736
+2016-08-24 09:51:38,916 DEBUG: Best view : Methyl_
+2016-08-24 09:51:43,129 DEBUG: Start: Iteration 69
+2016-08-24 09:51:43,145 DEBUG: View 0 : 0.644171779141
+2016-08-24 09:51:43,153 DEBUG: View 1 : 0.355828220859
+2016-08-24 09:51:43,201 DEBUG: View 2 : 0.386503067485
+2016-08-24 09:51:43,210 DEBUG: View 3 : 0.59509202454
+2016-08-24 09:51:43,415 DEBUG: Best view : Methyl_
+2016-08-24 09:51:47,699 DEBUG: Start: Iteration 70
+2016-08-24 09:51:47,715 DEBUG: View 0 : 0.466257668712
+2016-08-24 09:51:47,723 DEBUG: View 1 : 0.717791411043
+2016-08-24 09:51:47,771 DEBUG: View 2 : 0.411042944785
+2016-08-24 09:51:47,780 DEBUG: View 3 : 0.472392638037
+2016-08-24 09:51:47,988 DEBUG: Best view : MiRNA__
+2016-08-24 09:51:52,330 DEBUG: Start: Iteration 71
+2016-08-24 09:51:52,347 DEBUG: View 0 : 0.411042944785
+2016-08-24 09:51:52,354 DEBUG: View 1 : 0.58282208589
+2016-08-24 09:51:52,402 DEBUG: View 2 : 0.490797546012
+2016-08-24 09:51:52,411 DEBUG: View 3 : 0.552147239264
+2016-08-24 09:51:52,621 DEBUG: Best view : MiRNA__
+2016-08-24 09:51:57,005 DEBUG: Start: Iteration 72
+2016-08-24 09:51:57,022 DEBUG: View 0 : 0.411042944785
+2016-08-24 09:51:57,030 DEBUG: View 1 : 0.705521472393
+2016-08-24 09:51:57,078 DEBUG: View 2 : 0.564417177914
+2016-08-24 09:51:57,087 DEBUG: View 3 : 0.613496932515
+2016-08-24 09:51:57,298 DEBUG: Best view : MiRNA__
+2016-08-24 09:52:01,758 DEBUG: Start: Iteration 73
+2016-08-24 09:52:01,774 DEBUG: View 0 : 0.564417177914
+2016-08-24 09:52:01,782 DEBUG: View 1 : 0.644171779141
+2016-08-24 09:52:01,828 DEBUG: View 2 : 0.533742331288
+2016-08-24 09:52:01,837 DEBUG: View 3 : 0.546012269939
+2016-08-24 09:52:02,052 DEBUG: Best view : MiRNA__
+2016-08-24 09:52:06,564 DEBUG: Start: Iteration 74
+2016-08-24 09:52:06,580 DEBUG: View 0 : 0.39263803681
+2016-08-24 09:52:06,588 DEBUG: View 1 : 0.251533742331
+2016-08-24 09:52:06,637 DEBUG: View 2 : 0.466257668712
+2016-08-24 09:52:06,646 DEBUG: View 3 : 0.601226993865
+2016-08-24 09:52:06,862 DEBUG: Best view : Clinic_
+2016-08-24 09:52:11,562 DEBUG: Start: Iteration 75
+2016-08-24 09:52:11,579 DEBUG: View 0 : 0.58282208589
+2016-08-24 09:52:11,588 DEBUG: View 1 : 0.650306748466
+2016-08-24 09:52:11,636 DEBUG: View 2 : 0.466257668712
+2016-08-24 09:52:11,646 DEBUG: View 3 : 0.564417177914
+2016-08-24 09:52:11,883 DEBUG: Best view : MiRNA__
+2016-08-24 09:52:17,020 DEBUG: Start: Iteration 76
+2016-08-24 09:52:17,038 DEBUG: View 0 : 0.447852760736
+2016-08-24 09:52:17,046 DEBUG: View 1 : 0.374233128834
+2016-08-24 09:52:17,097 DEBUG: View 2 : 0.564417177914
+2016-08-24 09:52:17,105 DEBUG: View 3 : 0.60736196319
+2016-08-24 09:52:17,339 DEBUG: Best view : Clinic_
+2016-08-24 09:52:22,234 DEBUG: Start: Iteration 77
+2016-08-24 09:52:22,251 DEBUG: View 0 : 0.588957055215
+2016-08-24 09:52:22,259 DEBUG: View 1 : 0.558282208589
+2016-08-24 09:52:22,297 DEBUG: View 2 : 0.656441717791
+2016-08-24 09:52:22,305 DEBUG: View 3 : 0.59509202454
+2016-08-24 09:52:22,535 DEBUG: Best view : RANSeq_
+2016-08-24 09:52:27,433 DEBUG: Start: Iteration 78
+2016-08-24 09:52:27,450 DEBUG: View 0 : 0.411042944785
+2016-08-24 09:52:27,458 DEBUG: View 1 : 0.484662576687
+2016-08-24 09:52:27,496 DEBUG: View 2 : 0.466257668712
+2016-08-24 09:52:27,503 DEBUG: View 3 : 0.58282208589
+2016-08-24 09:52:27,729 DEBUG: Best view : Clinic_
+2016-08-24 09:52:32,667 DEBUG: Start: Iteration 79
+2016-08-24 09:52:32,684 DEBUG: View 0 : 0.411042944785
+2016-08-24 09:52:32,692 DEBUG: View 1 : 0.59509202454
+2016-08-24 09:52:32,730 DEBUG: View 2 : 0.61963190184
+2016-08-24 09:52:32,737 DEBUG: View 3 : 0.613496932515
+2016-08-24 09:52:32,967 DEBUG: Best view : RANSeq_
+2016-08-24 09:52:38,035 DEBUG: Start: Iteration 80
+2016-08-24 09:52:38,053 DEBUG: View 0 : 0.503067484663
+2016-08-24 09:52:38,061 DEBUG: View 1 : 0.61963190184
+2016-08-24 09:52:38,101 DEBUG: View 2 : 0.558282208589
+2016-08-24 09:52:38,109 DEBUG: View 3 : 0.576687116564
+2016-08-24 09:52:38,351 DEBUG: Best view : MiRNA__
+2016-08-24 09:52:43,584 DEBUG: Start: Iteration 81
+2016-08-24 09:52:43,601 DEBUG: View 0 : 0.472392638037
+2016-08-24 09:52:43,609 DEBUG: View 1 : 0.509202453988
+2016-08-24 09:52:43,648 DEBUG: View 2 : 0.386503067485
+2016-08-24 09:52:43,656 DEBUG: View 3 : 0.564417177914
+2016-08-24 09:52:43,897 DEBUG: Best view : Clinic_
+2016-08-24 09:52:49,124 DEBUG: Start: Iteration 82
+2016-08-24 09:52:49,141 DEBUG: View 0 : 0.398773006135
+2016-08-24 09:52:49,150 DEBUG: View 1 : 0.625766871166
+2016-08-24 09:52:49,191 DEBUG: View 2 : 0.59509202454
+2016-08-24 09:52:49,199 DEBUG: View 3 : 0.58282208589
+2016-08-24 09:52:49,451 DEBUG: Best view : MiRNA__
+2016-08-24 09:52:54,668 DEBUG: Start: Iteration 83
+2016-08-24 09:52:54,685 DEBUG: View 0 : 0.251533742331
+2016-08-24 09:52:54,693 DEBUG: View 1 : 0.539877300613
+2016-08-24 09:52:54,731 DEBUG: View 2 : 0.429447852761
+2016-08-24 09:52:54,739 DEBUG: View 3 : 0.423312883436
+2016-08-24 09:52:54,986 DEBUG: Best view : MiRNA__
+2016-08-24 09:53:00,332 DEBUG: Start: Iteration 84
+2016-08-24 09:53:00,349 DEBUG: View 0 : 0.411042944785
+2016-08-24 09:53:00,357 DEBUG: View 1 : 0.368098159509
+2016-08-24 09:53:00,394 DEBUG: View 2 : 0.521472392638
+2016-08-24 09:53:00,402 DEBUG: View 3 : 0.576687116564
+2016-08-24 09:53:00,642 DEBUG: Best view : Clinic_
+2016-08-24 09:53:05,890 DEBUG: Start: Iteration 85
+2016-08-24 09:53:05,908 DEBUG: View 0 : 0.343558282209
+2016-08-24 09:53:05,916 DEBUG: View 1 : 0.625766871166
+2016-08-24 09:53:05,955 DEBUG: View 2 : 0.472392638037
+2016-08-24 09:53:05,963 DEBUG: View 3 : 0.564417177914
+2016-08-24 09:53:06,217 DEBUG: Best view : MiRNA__
+2016-08-24 09:53:11,740 DEBUG: Start: Iteration 86
+2016-08-24 09:53:11,758 DEBUG: View 0 : 0.398773006135
+2016-08-24 09:53:11,766 DEBUG: View 1 : 0.539877300613
+2016-08-24 09:53:11,804 DEBUG: View 2 : 0.478527607362
+2016-08-24 09:53:11,812 DEBUG: View 3 : 0.429447852761
+2016-08-24 09:53:12,066 DEBUG: Best view : MiRNA__
+2016-08-24 09:53:17,553 DEBUG: Start: Iteration 87
+2016-08-24 09:53:17,570 DEBUG: View 0 : 0.386503067485
+2016-08-24 09:53:17,579 DEBUG: View 1 : 0.503067484663
+2016-08-24 09:53:17,617 DEBUG: View 2 : 0.539877300613
+2016-08-24 09:53:17,625 DEBUG: View 3 : 0.490797546012
+2016-08-24 09:53:17,893 DEBUG: Best view : MiRNA__
+2016-08-24 09:53:23,454 DEBUG: Start: Iteration 88
+2016-08-24 09:53:23,472 DEBUG: View 0 : 0.423312883436
+2016-08-24 09:53:23,480 DEBUG: View 1 : 0.705521472393
+2016-08-24 09:53:23,520 DEBUG: View 2 : 0.472392638037
+2016-08-24 09:53:23,528 DEBUG: View 3 : 0.41717791411
+2016-08-24 09:53:23,792 DEBUG: Best view : MiRNA__
+2016-08-24 09:53:29,579 DEBUG: Start: Iteration 89
+2016-08-24 09:53:29,598 DEBUG: View 0 : 0.38036809816
+2016-08-24 09:53:29,607 DEBUG: View 1 : 0.343558282209
+2016-08-24 09:53:29,663 DEBUG: View 2 : 0.429447852761
+2016-08-24 09:53:29,671 DEBUG: View 3 : 0.435582822086
+2016-08-24 09:53:29,671 WARNING: WARNING: All bad for iteration 88
+2016-08-24 09:53:29,966 DEBUG: Best view : Clinic_
+2016-08-24 09:53:35,846 DEBUG: Start: Iteration 90
+2016-08-24 09:53:35,863 DEBUG: View 0 : 0.601226993865
+2016-08-24 09:53:35,871 DEBUG: View 1 : 0.644171779141
+2016-08-24 09:53:35,919 DEBUG: View 2 : 0.478527607362
+2016-08-24 09:53:35,927 DEBUG: View 3 : 0.61963190184
+2016-08-24 09:53:36,194 DEBUG: Best view : MiRNA__
+2016-08-24 09:53:41,790 DEBUG: Start: Iteration 91
+2016-08-24 09:53:41,807 DEBUG: View 0 : 0.613496932515
+2016-08-24 09:53:41,815 DEBUG: View 1 : 0.558282208589
+2016-08-24 09:53:41,852 DEBUG: View 2 : 0.588957055215
+2016-08-24 09:53:41,860 DEBUG: View 3 : 0.441717791411
+2016-08-24 09:53:42,117 DEBUG: Best view : Methyl_
+2016-08-24 09:53:47,786 DEBUG: Start: Iteration 92
+2016-08-24 09:53:47,803 DEBUG: View 0 : 0.668711656442
+2016-08-24 09:53:47,811 DEBUG: View 1 : 0.515337423313
+2016-08-24 09:53:47,848 DEBUG: View 2 : 0.638036809816
+2016-08-24 09:53:47,856 DEBUG: View 3 : 0.38036809816
+2016-08-24 09:53:48,114 DEBUG: Best view : Methyl_
+2016-08-24 09:53:53,801 DEBUG: Start: Iteration 93
+2016-08-24 09:53:53,818 DEBUG: View 0 : 0.552147239264
+2016-08-24 09:53:53,825 DEBUG: View 1 : 0.631901840491
+2016-08-24 09:53:53,863 DEBUG: View 2 : 0.558282208589
+2016-08-24 09:53:53,871 DEBUG: View 3 : 0.447852760736
+2016-08-24 09:53:54,140 DEBUG: Best view : MiRNA__
+2016-08-24 09:53:59,928 DEBUG: Start: Iteration 94
+2016-08-24 09:53:59,945 DEBUG: View 0 : 0.460122699387
+2016-08-24 09:53:59,953 DEBUG: View 1 : 0.638036809816
+2016-08-24 09:53:59,990 DEBUG: View 2 : 0.576687116564
+2016-08-24 09:53:59,998 DEBUG: View 3 : 0.398773006135
+2016-08-24 09:54:00,264 DEBUG: Best view : MiRNA__
+2016-08-24 09:54:06,103 DEBUG: Start: Iteration 95
+2016-08-24 09:54:06,120 DEBUG: View 0 : 0.59509202454
+2016-08-24 09:54:06,128 DEBUG: View 1 : 0.638036809816
+2016-08-24 09:54:06,165 DEBUG: View 2 : 0.429447852761
+2016-08-24 09:54:06,173 DEBUG: View 3 : 0.496932515337
+2016-08-24 09:54:06,440 DEBUG: Best view : MiRNA__
+2016-08-24 09:54:12,344 DEBUG: Start: Iteration 96
+2016-08-24 09:54:12,361 DEBUG: View 0 : 0.601226993865
+2016-08-24 09:54:12,368 DEBUG: View 1 : 0.521472392638
+2016-08-24 09:54:12,406 DEBUG: View 2 : 0.58282208589
+2016-08-24 09:54:12,414 DEBUG: View 3 : 0.533742331288
+2016-08-24 09:54:12,683 DEBUG: Best view : Methyl_
+2016-08-24 09:54:18,660 DEBUG: Start: Iteration 97
+2016-08-24 09:54:18,677 DEBUG: View 0 : 0.374233128834
+2016-08-24 09:54:18,684 DEBUG: View 1 : 0.742331288344
+2016-08-24 09:54:18,722 DEBUG: View 2 : 0.423312883436
+2016-08-24 09:54:18,730 DEBUG: View 3 : 0.638036809816
+2016-08-24 09:54:19,002 DEBUG: Best view : MiRNA__
+2016-08-24 09:54:25,028 DEBUG: Start: Iteration 98
+2016-08-24 09:54:25,045 DEBUG: View 0 : 0.355828220859
+2016-08-24 09:54:25,053 DEBUG: View 1 : 0.558282208589
+2016-08-24 09:54:25,096 DEBUG: View 2 : 0.472392638037
+2016-08-24 09:54:25,104 DEBUG: View 3 : 0.558282208589
+2016-08-24 09:54:25,377 DEBUG: Best view : MiRNA__
+2016-08-24 09:54:31,445 DEBUG: Start: Iteration 99
+2016-08-24 09:54:31,462 DEBUG: View 0 : 0.58282208589
+2016-08-24 09:54:31,469 DEBUG: View 1 : 0.58282208589
+2016-08-24 09:54:31,507 DEBUG: View 2 : 0.58282208589
+2016-08-24 09:54:31,515 DEBUG: View 3 : 0.564417177914
+2016-08-24 09:54:31,788 DEBUG: Best view : Methyl_
+2016-08-24 09:54:37,994 DEBUG: Start: Iteration 100
+2016-08-24 09:54:38,010 DEBUG: View 0 : 0.496932515337
+2016-08-24 09:54:38,018 DEBUG: View 1 : 0.546012269939
+2016-08-24 09:54:38,056 DEBUG: View 2 : 0.521472392638
+2016-08-24 09:54:38,064 DEBUG: View 3 : 0.40490797546
+2016-08-24 09:54:38,339 DEBUG: Best view : MiRNA__
+2016-08-24 09:54:44,470 DEBUG: Start: Iteration 101
+2016-08-24 09:54:44,487 DEBUG: View 0 : 0.490797546012
+2016-08-24 09:54:44,495 DEBUG: View 1 : 0.533742331288
+2016-08-24 09:54:44,532 DEBUG: View 2 : 0.490797546012
+2016-08-24 09:54:44,539 DEBUG: View 3 : 0.546012269939
+2016-08-24 09:54:44,817 DEBUG: Best view : MiRNA__
+2016-08-24 09:54:51,232 DEBUG: Start: Iteration 102
+2016-08-24 09:54:51,252 DEBUG: View 0 : 0.300613496933
+2016-08-24 09:54:51,261 DEBUG: View 1 : 0.368098159509
+2016-08-24 09:54:51,315 DEBUG: View 2 : 0.509202453988
+2016-08-24 09:54:51,324 DEBUG: View 3 : 0.60736196319
+2016-08-24 09:54:51,621 DEBUG: Best view : Clinic_
+2016-08-24 09:54:57,874 INFO: Start: Classification
+2016-08-24 09:55:12,569 INFO: Done: Fold number 1
+2016-08-24 09:55:12,569 INFO: Start: Fold number 2
+2016-08-24 09:55:14,073 DEBUG: Start: Iteration 1
+2016-08-24 09:55:14,088 DEBUG: View 0 : 0.602649006623
+2016-08-24 09:55:14,095 DEBUG: View 1 : 0.602649006623
+2016-08-24 09:55:14,126 DEBUG: View 2 : 0.602649006623
+2016-08-24 09:55:14,133 DEBUG: View 3 : 0.602649006623
+2016-08-24 09:55:14,172 DEBUG: Best view : Methyl_
+2016-08-24 09:55:14,242 DEBUG: Start: Iteration 2
+2016-08-24 09:55:14,258 DEBUG: View 0 : 0.331125827815
+2016-08-24 09:55:14,265 DEBUG: View 1 : 0.251655629139
+2016-08-24 09:55:14,301 DEBUG: View 2 : 0.390728476821
+2016-08-24 09:55:14,308 DEBUG: View 3 : 0.384105960265
+2016-08-24 09:55:14,308 WARNING: WARNING: All bad for iteration 1
+2016-08-24 09:55:14,351 DEBUG: Best view : RANSeq_
+2016-08-24 09:55:14,492 DEBUG: Start: Iteration 3
+2016-08-24 09:55:14,508 DEBUG: View 0 : 0.53642384106
+2016-08-24 09:55:14,515 DEBUG: View 1 : 0.735099337748
+2016-08-24 09:55:14,550 DEBUG: View 2 : 0.516556291391
+2016-08-24 09:55:14,558 DEBUG: View 3 : 0.456953642384
+2016-08-24 09:55:14,603 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:14,797 DEBUG: Start: Iteration 4
+2016-08-24 09:55:14,813 DEBUG: View 0 : 0.814569536424
+2016-08-24 09:55:14,820 DEBUG: View 1 : 0.377483443709
+2016-08-24 09:55:14,856 DEBUG: View 2 : 0.523178807947
+2016-08-24 09:55:14,863 DEBUG: View 3 : 0.443708609272
+2016-08-24 09:55:14,916 DEBUG: Best view : Methyl_
+2016-08-24 09:55:15,168 DEBUG: Start: Iteration 5
+2016-08-24 09:55:15,184 DEBUG: View 0 : 0.609271523179
+2016-08-24 09:55:15,192 DEBUG: View 1 : 0.708609271523
+2016-08-24 09:55:15,227 DEBUG: View 2 : 0.516556291391
+2016-08-24 09:55:15,234 DEBUG: View 3 : 0.456953642384
+2016-08-24 09:55:15,288 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:15,596 DEBUG: Start: Iteration 6
+2016-08-24 09:55:15,612 DEBUG: View 0 : 0.615894039735
+2016-08-24 09:55:15,619 DEBUG: View 1 : 0.635761589404
+2016-08-24 09:55:15,654 DEBUG: View 2 : 0.503311258278
+2016-08-24 09:55:15,661 DEBUG: View 3 : 0.476821192053
+2016-08-24 09:55:15,717 DEBUG: Best view : Methyl_
+2016-08-24 09:55:16,081 DEBUG: Start: Iteration 7
+2016-08-24 09:55:16,097 DEBUG: View 0 : 0.384105960265
+2016-08-24 09:55:16,104 DEBUG: View 1 : 0.53642384106
+2016-08-24 09:55:16,140 DEBUG: View 2 : 0.476821192053
+2016-08-24 09:55:16,147 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:55:16,206 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:16,624 DEBUG: Start: Iteration 8
+2016-08-24 09:55:16,639 DEBUG: View 0 : 0.456953642384
+2016-08-24 09:55:16,647 DEBUG: View 1 : 0.470198675497
+2016-08-24 09:55:16,682 DEBUG: View 2 : 0.450331125828
+2016-08-24 09:55:16,689 DEBUG: View 3 : 0.450331125828
+2016-08-24 09:55:16,689 WARNING: WARNING: All bad for iteration 7
+2016-08-24 09:55:16,751 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:17,223 DEBUG: Start: Iteration 9
+2016-08-24 09:55:17,239 DEBUG: View 0 : 0.456953642384
+2016-08-24 09:55:17,247 DEBUG: View 1 : 0.602649006623
+2016-08-24 09:55:17,282 DEBUG: View 2 : 0.569536423841
+2016-08-24 09:55:17,289 DEBUG: View 3 : 0.430463576159
+2016-08-24 09:55:17,353 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:17,881 DEBUG: Start: Iteration 10
+2016-08-24 09:55:17,896 DEBUG: View 0 : 0.622516556291
+2016-08-24 09:55:17,904 DEBUG: View 1 : 0.58940397351
+2016-08-24 09:55:17,939 DEBUG: View 2 : 0.543046357616
+2016-08-24 09:55:17,947 DEBUG: View 3 : 0.562913907285
+2016-08-24 09:55:18,013 DEBUG: Best view : Methyl_
+2016-08-24 09:55:18,596 DEBUG: Start: Iteration 11
+2016-08-24 09:55:18,613 DEBUG: View 0 : 0.450331125828
+2016-08-24 09:55:18,620 DEBUG: View 1 : 0.622516556291
+2016-08-24 09:55:18,655 DEBUG: View 2 : 0.41059602649
+2016-08-24 09:55:18,662 DEBUG: View 3 : 0.417218543046
+2016-08-24 09:55:18,730 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:19,382 DEBUG: Start: Iteration 12
+2016-08-24 09:55:19,398 DEBUG: View 0 : 0.562913907285
+2016-08-24 09:55:19,405 DEBUG: View 1 : 0.576158940397
+2016-08-24 09:55:19,441 DEBUG: View 2 : 0.456953642384
+2016-08-24 09:55:19,449 DEBUG: View 3 : 0.41059602649
+2016-08-24 09:55:19,519 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:20,210 DEBUG: Start: Iteration 13
+2016-08-24 09:55:20,226 DEBUG: View 0 : 0.483443708609
+2016-08-24 09:55:20,233 DEBUG: View 1 : 0.635761589404
+2016-08-24 09:55:20,269 DEBUG: View 2 : 0.476821192053
+2016-08-24 09:55:20,276 DEBUG: View 3 : 0.483443708609
+2016-08-24 09:55:20,349 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:21,098 DEBUG: Start: Iteration 14
+2016-08-24 09:55:21,114 DEBUG: View 0 : 0.523178807947
+2016-08-24 09:55:21,121 DEBUG: View 1 : 0.582781456954
+2016-08-24 09:55:21,157 DEBUG: View 2 : 0.470198675497
+2016-08-24 09:55:21,164 DEBUG: View 3 : 0.53642384106
+2016-08-24 09:55:21,239 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:22,041 DEBUG: Start: Iteration 15
+2016-08-24 09:55:22,056 DEBUG: View 0 : 0.350993377483
+2016-08-24 09:55:22,064 DEBUG: View 1 : 0.668874172185
+2016-08-24 09:55:22,099 DEBUG: View 2 : 0.476821192053
+2016-08-24 09:55:22,106 DEBUG: View 3 : 0.569536423841
+2016-08-24 09:55:22,183 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:23,038 DEBUG: Start: Iteration 16
+2016-08-24 09:55:23,054 DEBUG: View 0 : 0.596026490066
+2016-08-24 09:55:23,061 DEBUG: View 1 : 0.543046357616
+2016-08-24 09:55:23,097 DEBUG: View 2 : 0.629139072848
+2016-08-24 09:55:23,104 DEBUG: View 3 : 0.649006622517
+2016-08-24 09:55:23,183 DEBUG: Best view : Clinic_
+2016-08-24 09:55:24,094 DEBUG: Start: Iteration 17
+2016-08-24 09:55:24,111 DEBUG: View 0 : 0.456953642384
+2016-08-24 09:55:24,118 DEBUG: View 1 : 0.615894039735
+2016-08-24 09:55:24,154 DEBUG: View 2 : 0.490066225166
+2016-08-24 09:55:24,161 DEBUG: View 3 : 0.516556291391
+2016-08-24 09:55:24,242 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:25,204 DEBUG: Start: Iteration 18
+2016-08-24 09:55:25,220 DEBUG: View 0 : 0.562913907285
+2016-08-24 09:55:25,227 DEBUG: View 1 : 0.450331125828
+2016-08-24 09:55:25,263 DEBUG: View 2 : 0.450331125828
+2016-08-24 09:55:25,270 DEBUG: View 3 : 0.483443708609
+2016-08-24 09:55:25,354 DEBUG: Best view : Methyl_
+2016-08-24 09:55:26,372 DEBUG: Start: Iteration 19
+2016-08-24 09:55:26,388 DEBUG: View 0 : 0.364238410596
+2016-08-24 09:55:26,396 DEBUG: View 1 : 0.668874172185
+2016-08-24 09:55:26,431 DEBUG: View 2 : 0.53642384106
+2016-08-24 09:55:26,438 DEBUG: View 3 : 0.529801324503
+2016-08-24 09:55:26,523 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:27,596 DEBUG: Start: Iteration 20
+2016-08-24 09:55:27,612 DEBUG: View 0 : 0.317880794702
+2016-08-24 09:55:27,620 DEBUG: View 1 : 0.403973509934
+2016-08-24 09:55:27,655 DEBUG: View 2 : 0.529801324503
+2016-08-24 09:55:27,663 DEBUG: View 3 : 0.629139072848
+2016-08-24 09:55:27,751 DEBUG: Best view : Clinic_
+2016-08-24 09:55:28,884 DEBUG: Start: Iteration 21
+2016-08-24 09:55:28,900 DEBUG: View 0 : 0.549668874172
+2016-08-24 09:55:28,907 DEBUG: View 1 : 0.649006622517
+2016-08-24 09:55:28,945 DEBUG: View 2 : 0.529801324503
+2016-08-24 09:55:28,952 DEBUG: View 3 : 0.503311258278
+2016-08-24 09:55:29,041 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:30,227 DEBUG: Start: Iteration 22
+2016-08-24 09:55:30,243 DEBUG: View 0 : 0.483443708609
+2016-08-24 09:55:30,250 DEBUG: View 1 : 0.556291390728
+2016-08-24 09:55:30,285 DEBUG: View 2 : 0.622516556291
+2016-08-24 09:55:30,293 DEBUG: View 3 : 0.46357615894
+2016-08-24 09:55:30,385 DEBUG: Best view : RANSeq_
+2016-08-24 09:55:31,630 DEBUG: Start: Iteration 23
+2016-08-24 09:55:31,646 DEBUG: View 0 : 0.609271523179
+2016-08-24 09:55:31,653 DEBUG: View 1 : 0.629139072848
+2016-08-24 09:55:31,688 DEBUG: View 2 : 0.556291390728
+2016-08-24 09:55:31,696 DEBUG: View 3 : 0.629139072848
+2016-08-24 09:55:31,789 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:33,091 DEBUG: Start: Iteration 24
+2016-08-24 09:55:33,107 DEBUG: View 0 : 0.503311258278
+2016-08-24 09:55:33,114 DEBUG: View 1 : 0.668874172185
+2016-08-24 09:55:33,149 DEBUG: View 2 : 0.470198675497
+2016-08-24 09:55:33,157 DEBUG: View 3 : 0.476821192053
+2016-08-24 09:55:33,252 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:34,607 DEBUG: Start: Iteration 25
+2016-08-24 09:55:34,623 DEBUG: View 0 : 0.562913907285
+2016-08-24 09:55:34,630 DEBUG: View 1 : 0.284768211921
+2016-08-24 09:55:34,666 DEBUG: View 2 : 0.556291390728
+2016-08-24 09:55:34,673 DEBUG: View 3 : 0.456953642384
+2016-08-24 09:55:34,771 DEBUG: Best view : Methyl_
+2016-08-24 09:55:36,182 DEBUG: Start: Iteration 26
+2016-08-24 09:55:36,198 DEBUG: View 0 : 0.390728476821
+2016-08-24 09:55:36,205 DEBUG: View 1 : 0.576158940397
+2016-08-24 09:55:36,241 DEBUG: View 2 : 0.390728476821
+2016-08-24 09:55:36,248 DEBUG: View 3 : 0.569536423841
+2016-08-24 09:55:36,348 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:37,816 DEBUG: Start: Iteration 27
+2016-08-24 09:55:37,832 DEBUG: View 0 : 0.377483443709
+2016-08-24 09:55:37,839 DEBUG: View 1 : 0.509933774834
+2016-08-24 09:55:37,875 DEBUG: View 2 : 0.543046357616
+2016-08-24 09:55:37,882 DEBUG: View 3 : 0.417218543046
+2016-08-24 09:55:37,984 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:39,506 DEBUG: Start: Iteration 28
+2016-08-24 09:55:39,522 DEBUG: View 0 : 0.556291390728
+2016-08-24 09:55:39,530 DEBUG: View 1 : 0.397350993377
+2016-08-24 09:55:39,565 DEBUG: View 2 : 0.576158940397
+2016-08-24 09:55:39,572 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:55:39,676 DEBUG: Best view : RANSeq_
+2016-08-24 09:55:41,263 DEBUG: Start: Iteration 29
+2016-08-24 09:55:41,279 DEBUG: View 0 : 0.655629139073
+2016-08-24 09:55:41,286 DEBUG: View 1 : 0.728476821192
+2016-08-24 09:55:41,321 DEBUG: View 2 : 0.615894039735
+2016-08-24 09:55:41,329 DEBUG: View 3 : 0.470198675497
+2016-08-24 09:55:41,436 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:43,079 DEBUG: Start: Iteration 30
+2016-08-24 09:55:43,095 DEBUG: View 0 : 0.569536423841
+2016-08-24 09:55:43,102 DEBUG: View 1 : 0.344370860927
+2016-08-24 09:55:43,137 DEBUG: View 2 : 0.470198675497
+2016-08-24 09:55:43,144 DEBUG: View 3 : 0.470198675497
+2016-08-24 09:55:43,253 DEBUG: Best view : Methyl_
+2016-08-24 09:55:44,951 DEBUG: Start: Iteration 31
+2016-08-24 09:55:44,967 DEBUG: View 0 : 0.562913907285
+2016-08-24 09:55:44,975 DEBUG: View 1 : 0.218543046358
+2016-08-24 09:55:45,009 DEBUG: View 2 : 0.53642384106
+2016-08-24 09:55:45,017 DEBUG: View 3 : 0.516556291391
+2016-08-24 09:55:45,128 DEBUG: Best view : Methyl_
+2016-08-24 09:55:46,886 DEBUG: Start: Iteration 32
+2016-08-24 09:55:46,902 DEBUG: View 0 : 0.470198675497
+2016-08-24 09:55:46,909 DEBUG: View 1 : 0.35761589404
+2016-08-24 09:55:46,944 DEBUG: View 2 : 0.509933774834
+2016-08-24 09:55:46,952 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:55:47,064 DEBUG: Best view : Clinic_
+2016-08-24 09:55:48,874 DEBUG: Start: Iteration 33
+2016-08-24 09:55:48,889 DEBUG: View 0 : 0.569536423841
+2016-08-24 09:55:48,897 DEBUG: View 1 : 0.596026490066
+2016-08-24 09:55:48,932 DEBUG: View 2 : 0.377483443709
+2016-08-24 09:55:48,939 DEBUG: View 3 : 0.556291390728
+2016-08-24 09:55:49,054 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:50,918 DEBUG: Start: Iteration 34
+2016-08-24 09:55:50,934 DEBUG: View 0 : 0.450331125828
+2016-08-24 09:55:50,942 DEBUG: View 1 : 0.549668874172
+2016-08-24 09:55:50,976 DEBUG: View 2 : 0.503311258278
+2016-08-24 09:55:50,984 DEBUG: View 3 : 0.41059602649
+2016-08-24 09:55:51,099 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:53,017 DEBUG: Start: Iteration 35
+2016-08-24 09:55:53,032 DEBUG: View 0 : 0.430463576159
+2016-08-24 09:55:53,040 DEBUG: View 1 : 0.668874172185
+2016-08-24 09:55:53,075 DEBUG: View 2 : 0.596026490066
+2016-08-24 09:55:53,082 DEBUG: View 3 : 0.423841059603
+2016-08-24 09:55:53,199 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:55,212 DEBUG: Start: Iteration 36
+2016-08-24 09:55:55,229 DEBUG: View 0 : 0.675496688742
+2016-08-24 09:55:55,236 DEBUG: View 1 : 0.450331125828
+2016-08-24 09:55:55,271 DEBUG: View 2 : 0.576158940397
+2016-08-24 09:55:55,279 DEBUG: View 3 : 0.58940397351
+2016-08-24 09:55:55,400 DEBUG: Best view : Methyl_
+2016-08-24 09:55:57,436 DEBUG: Start: Iteration 37
+2016-08-24 09:55:57,451 DEBUG: View 0 : 0.569536423841
+2016-08-24 09:55:57,459 DEBUG: View 1 : 0.649006622517
+2016-08-24 09:55:57,494 DEBUG: View 2 : 0.543046357616
+2016-08-24 09:55:57,501 DEBUG: View 3 : 0.490066225166
+2016-08-24 09:55:57,623 DEBUG: Best view : MiRNA__
+2016-08-24 09:55:59,706 DEBUG: Start: Iteration 38
+2016-08-24 09:55:59,722 DEBUG: View 0 : 0.549668874172
+2016-08-24 09:55:59,730 DEBUG: View 1 : 0.64238410596
+2016-08-24 09:55:59,765 DEBUG: View 2 : 0.490066225166
+2016-08-24 09:55:59,773 DEBUG: View 3 : 0.403973509934
+2016-08-24 09:55:59,897 DEBUG: Best view : MiRNA__
+2016-08-24 09:56:02,038 DEBUG: Start: Iteration 39
+2016-08-24 09:56:02,053 DEBUG: View 0 : 0.476821192053
+2016-08-24 09:56:02,061 DEBUG: View 1 : 0.622516556291
+2016-08-24 09:56:02,096 DEBUG: View 2 : 0.602649006623
+2016-08-24 09:56:02,103 DEBUG: View 3 : 0.496688741722
+2016-08-24 09:56:02,230 DEBUG: Best view : MiRNA__
+2016-08-24 09:56:04,427 DEBUG: Start: Iteration 40
+2016-08-24 09:56:04,443 DEBUG: View 0 : 0.476821192053
+2016-08-24 09:56:04,451 DEBUG: View 1 : 0.569536423841
+2016-08-24 09:56:04,486 DEBUG: View 2 : 0.576158940397
+2016-08-24 09:56:04,493 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:56:04,621 DEBUG: Best view : RANSeq_
+2016-08-24 09:56:06,880 DEBUG: Start: Iteration 41
+2016-08-24 09:56:06,896 DEBUG: View 0 : 0.556291390728
+2016-08-24 09:56:06,903 DEBUG: View 1 : 0.668874172185
+2016-08-24 09:56:06,939 DEBUG: View 2 : 0.596026490066
+2016-08-24 09:56:06,946 DEBUG: View 3 : 0.569536423841
+2016-08-24 09:56:07,075 DEBUG: Best view : MiRNA__
+2016-08-24 09:56:09,389 DEBUG: Start: Iteration 42
+2016-08-24 09:56:09,405 DEBUG: View 0 : 0.543046357616
+2016-08-24 09:56:09,413 DEBUG: View 1 : 0.344370860927
+2016-08-24 09:56:09,448 DEBUG: View 2 : 0.437086092715
+2016-08-24 09:56:09,455 DEBUG: View 3 : 0.490066225166
+2016-08-24 09:56:09,587 DEBUG: Best view : Methyl_
+2016-08-24 09:56:11,958 DEBUG: Start: Iteration 43
+2016-08-24 09:56:11,973 DEBUG: View 0 : 0.596026490066
+2016-08-24 09:56:11,981 DEBUG: View 1 : 0.668874172185
+2016-08-24 09:56:12,016 DEBUG: View 2 : 0.456953642384
+2016-08-24 09:56:12,024 DEBUG: View 3 : 0.556291390728
+2016-08-24 09:56:12,158 DEBUG: Best view : MiRNA__
+2016-08-24 09:56:14,594 DEBUG: Start: Iteration 44
+2016-08-24 09:56:14,610 DEBUG: View 0 : 0.430463576159
+2016-08-24 09:56:14,617 DEBUG: View 1 : 0.483443708609
+2016-08-24 09:56:14,653 DEBUG: View 2 : 0.430463576159
+2016-08-24 09:56:14,660 DEBUG: View 3 : 0.476821192053
+2016-08-24 09:56:14,660 WARNING: WARNING: All bad for iteration 43
+2016-08-24 09:56:14,797 DEBUG: Best view : MiRNA__
+2016-08-24 09:56:17,274 DEBUG: Start: Iteration 45
+2016-08-24 09:56:17,291 DEBUG: View 0 : 0.470198675497
+2016-08-24 09:56:17,298 DEBUG: View 1 : 0.443708609272
+2016-08-24 09:56:17,333 DEBUG: View 2 : 0.58940397351
+2016-08-24 09:56:17,341 DEBUG: View 3 : 0.496688741722
+2016-08-24 09:56:17,480 DEBUG: Best view : RANSeq_
+2016-08-24 09:56:20,029 DEBUG: Start: Iteration 46
+2016-08-24 09:56:20,045 DEBUG: View 0 : 0.496688741722
+2016-08-24 09:56:20,052 DEBUG: View 1 : 0.576158940397
+2016-08-24 09:56:20,088 DEBUG: View 2 : 0.423841059603
+2016-08-24 09:56:20,095 DEBUG: View 3 : 0.423841059603
+2016-08-24 09:56:20,235 DEBUG: Best view : MiRNA__
+2016-08-24 09:56:22,835 DEBUG: Start: Iteration 47
+2016-08-24 09:56:22,851 DEBUG: View 0 : 0.337748344371
+2016-08-24 09:56:22,858 DEBUG: View 1 : 0.145695364238
+2016-08-24 09:56:22,894 DEBUG: View 2 : 0.523178807947
+2016-08-24 09:56:22,901 DEBUG: View 3 : 0.556291390728
+2016-08-24 09:56:23,043 DEBUG: Best view : Clinic_
+2016-08-24 09:56:25,695 DEBUG: Start: Iteration 48
+2016-08-24 09:56:25,710 DEBUG: View 0 : 0.476821192053
+2016-08-24 09:56:25,718 DEBUG: View 1 : 0.35761589404
+2016-08-24 09:56:25,753 DEBUG: View 2 : 0.483443708609
+2016-08-24 09:56:25,760 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:56:25,906 DEBUG: Best view : Clinic_
+2016-08-24 09:56:28,607 DEBUG: Start: Iteration 49
+2016-08-24 09:56:28,623 DEBUG: View 0 : 0.390728476821
+2016-08-24 09:56:28,631 DEBUG: View 1 : 0.668874172185
+2016-08-24 09:56:28,665 DEBUG: View 2 : 0.490066225166
+2016-08-24 09:56:28,673 DEBUG: View 3 : 0.490066225166
+2016-08-24 09:56:28,820 DEBUG: Best view : MiRNA__
+2016-08-24 09:56:31,591 DEBUG: Start: Iteration 50
+2016-08-24 09:56:31,607 DEBUG: View 0 : 0.46357615894
+2016-08-24 09:56:31,614 DEBUG: View 1 : 0.496688741722
+2016-08-24 09:56:31,651 DEBUG: View 2 : 0.516556291391
+2016-08-24 09:56:31,658 DEBUG: View 3 : 0.496688741722
+2016-08-24 09:56:31,807 DEBUG: Best view : MiRNA__
+2016-08-24 09:56:34,616 DEBUG: Start: Iteration 51
+2016-08-24 09:56:34,632 DEBUG: View 0 : 0.529801324503
+2016-08-24 09:56:34,639 DEBUG: View 1 : 0.609271523179
+2016-08-24 09:56:34,674 DEBUG: View 2 : 0.569536423841
+2016-08-24 09:56:34,682 DEBUG: View 3 : 0.46357615894
+2016-08-24 09:56:34,832 DEBUG: Best view : MiRNA__
+2016-08-24 09:56:37,700 DEBUG: Start: Iteration 52
+2016-08-24 09:56:37,715 DEBUG: View 0 : 0.516556291391
+2016-08-24 09:56:37,723 DEBUG: View 1 : 0.337748344371
+2016-08-24 09:56:37,758 DEBUG: View 2 : 0.596026490066
+2016-08-24 09:56:37,765 DEBUG: View 3 : 0.58940397351
+2016-08-24 09:56:37,918 DEBUG: Best view : RANSeq_
+2016-08-24 09:56:40,853 DEBUG: Start: Iteration 53
+2016-08-24 09:56:40,869 DEBUG: View 0 : 0.503311258278
+2016-08-24 09:56:40,877 DEBUG: View 1 : 0.53642384106
+2016-08-24 09:56:40,913 DEBUG: View 2 : 0.490066225166
+2016-08-24 09:56:40,920 DEBUG: View 3 : 0.403973509934
+2016-08-24 09:56:41,076 DEBUG: Best view : MiRNA__
+2016-08-24 09:56:44,061 DEBUG: Start: Iteration 54
+2016-08-24 09:56:44,077 DEBUG: View 0 : 0.576158940397
+2016-08-24 09:56:44,085 DEBUG: View 1 : 0.509933774834
+2016-08-24 09:56:44,120 DEBUG: View 2 : 0.430463576159
+2016-08-24 09:56:44,128 DEBUG: View 3 : 0.456953642384
+2016-08-24 09:56:44,286 DEBUG: Best view : Methyl_
+2016-08-24 09:56:47,328 DEBUG: Start: Iteration 55
+2016-08-24 09:56:47,344 DEBUG: View 0 : 0.483443708609
+2016-08-24 09:56:47,351 DEBUG: View 1 : 0.317880794702
+2016-08-24 09:56:47,387 DEBUG: View 2 : 0.622516556291
+2016-08-24 09:56:47,394 DEBUG: View 3 : 0.483443708609
+2016-08-24 09:56:47,552 DEBUG: Best view : RANSeq_
+2016-08-24 09:56:50,670 DEBUG: Start: Iteration 56
+2016-08-24 09:56:50,686 DEBUG: View 0 : 0.53642384106
+2016-08-24 09:56:50,693 DEBUG: View 1 : 0.53642384106
+2016-08-24 09:56:50,728 DEBUG: View 2 : 0.622516556291
+2016-08-24 09:56:50,736 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:56:50,897 DEBUG: Best view : RANSeq_
+2016-08-24 09:56:54,078 DEBUG: Start: Iteration 57
+2016-08-24 09:56:54,094 DEBUG: View 0 : 0.53642384106
+2016-08-24 09:56:54,102 DEBUG: View 1 : 0.708609271523
+2016-08-24 09:56:54,137 DEBUG: View 2 : 0.450331125828
+2016-08-24 09:56:54,144 DEBUG: View 3 : 0.523178807947
+2016-08-24 09:56:54,307 DEBUG: Best view : MiRNA__
+2016-08-24 09:56:57,540 DEBUG: Start: Iteration 58
+2016-08-24 09:56:57,556 DEBUG: View 0 : 0.582781456954
+2016-08-24 09:56:57,563 DEBUG: View 1 : 0.602649006623
+2016-08-24 09:56:57,598 DEBUG: View 2 : 0.576158940397
+2016-08-24 09:56:57,605 DEBUG: View 3 : 0.496688741722
+2016-08-24 09:56:57,771 DEBUG: Best view : MiRNA__
+2016-08-24 09:57:01,055 DEBUG: Start: Iteration 59
+2016-08-24 09:57:01,071 DEBUG: View 0 : 0.615894039735
+2016-08-24 09:57:01,078 DEBUG: View 1 : 0.284768211921
+2016-08-24 09:57:01,114 DEBUG: View 2 : 0.569536423841
+2016-08-24 09:57:01,121 DEBUG: View 3 : 0.503311258278
+2016-08-24 09:57:01,288 DEBUG: Best view : Methyl_
+2016-08-24 09:57:04,633 DEBUG: Start: Iteration 60
+2016-08-24 09:57:04,648 DEBUG: View 0 : 0.543046357616
+2016-08-24 09:57:04,656 DEBUG: View 1 : 0.615894039735
+2016-08-24 09:57:04,691 DEBUG: View 2 : 0.582781456954
+2016-08-24 09:57:04,699 DEBUG: View 3 : 0.582781456954
+2016-08-24 09:57:04,868 DEBUG: Best view : MiRNA__
+2016-08-24 09:57:08,269 DEBUG: Start: Iteration 61
+2016-08-24 09:57:08,284 DEBUG: View 0 : 0.529801324503
+2016-08-24 09:57:08,291 DEBUG: View 1 : 0.715231788079
+2016-08-24 09:57:08,327 DEBUG: View 2 : 0.562913907285
+2016-08-24 09:57:08,334 DEBUG: View 3 : 0.423841059603
+2016-08-24 09:57:08,506 DEBUG: Best view : MiRNA__
+2016-08-24 09:57:11,957 DEBUG: Start: Iteration 62
+2016-08-24 09:57:11,973 DEBUG: View 0 : 0.523178807947
+2016-08-24 09:57:11,980 DEBUG: View 1 : 0.350993377483
+2016-08-24 09:57:12,023 DEBUG: View 2 : 0.456953642384
+2016-08-24 09:57:12,030 DEBUG: View 3 : 0.483443708609
+2016-08-24 09:57:12,204 DEBUG: Best view : Methyl_
+2016-08-24 09:57:15,727 DEBUG: Start: Iteration 63
+2016-08-24 09:57:15,743 DEBUG: View 0 : 0.331125827815
+2016-08-24 09:57:15,750 DEBUG: View 1 : 0.688741721854
+2016-08-24 09:57:15,785 DEBUG: View 2 : 0.64238410596
+2016-08-24 09:57:15,793 DEBUG: View 3 : 0.503311258278
+2016-08-24 09:57:15,968 DEBUG: Best view : MiRNA__
+2016-08-24 09:57:19,529 DEBUG: Start: Iteration 64
+2016-08-24 09:57:19,545 DEBUG: View 0 : 0.58940397351
+2016-08-24 09:57:19,552 DEBUG: View 1 : 0.596026490066
+2016-08-24 09:57:19,591 DEBUG: View 2 : 0.503311258278
+2016-08-24 09:57:19,598 DEBUG: View 3 : 0.596026490066
+2016-08-24 09:57:19,775 DEBUG: Best view : Methyl_
+2016-08-24 09:57:23,387 DEBUG: Start: Iteration 65
+2016-08-24 09:57:23,403 DEBUG: View 0 : 0.470198675497
+2016-08-24 09:57:23,411 DEBUG: View 1 : 0.41059602649
+2016-08-24 09:57:23,446 DEBUG: View 2 : 0.523178807947
+2016-08-24 09:57:23,454 DEBUG: View 3 : 0.417218543046
+2016-08-24 09:57:23,632 DEBUG: Best view : RANSeq_
+2016-08-24 09:57:27,319 DEBUG: Start: Iteration 66
+2016-08-24 09:57:27,335 DEBUG: View 0 : 0.728476821192
+2016-08-24 09:57:27,343 DEBUG: View 1 : 0.523178807947
+2016-08-24 09:57:27,379 DEBUG: View 2 : 0.562913907285
+2016-08-24 09:57:27,386 DEBUG: View 3 : 0.41059602649
+2016-08-24 09:57:27,568 DEBUG: Best view : Methyl_
+2016-08-24 09:57:31,325 DEBUG: Start: Iteration 67
+2016-08-24 09:57:31,341 DEBUG: View 0 : 0.450331125828
+2016-08-24 09:57:31,348 DEBUG: View 1 : 0.649006622517
+2016-08-24 09:57:31,383 DEBUG: View 2 : 0.569536423841
+2016-08-24 09:57:31,391 DEBUG: View 3 : 0.483443708609
+2016-08-24 09:57:31,574 DEBUG: Best view : MiRNA__
+2016-08-24 09:57:35,371 DEBUG: Start: Iteration 68
+2016-08-24 09:57:35,386 DEBUG: View 0 : 0.443708609272
+2016-08-24 09:57:35,394 DEBUG: View 1 : 0.509933774834
+2016-08-24 09:57:35,429 DEBUG: View 2 : 0.635761589404
+2016-08-24 09:57:35,436 DEBUG: View 3 : 0.46357615894
+2016-08-24 09:57:35,622 DEBUG: Best view : RANSeq_
+2016-08-24 09:57:39,510 DEBUG: Start: Iteration 69
+2016-08-24 09:57:39,526 DEBUG: View 0 : 0.344370860927
+2016-08-24 09:57:39,533 DEBUG: View 1 : 0.708609271523
+2016-08-24 09:57:39,568 DEBUG: View 2 : 0.423841059603
+2016-08-24 09:57:39,576 DEBUG: View 3 : 0.417218543046
+2016-08-24 09:57:39,765 DEBUG: Best view : MiRNA__
+2016-08-24 09:57:43,687 DEBUG: Start: Iteration 70
+2016-08-24 09:57:43,703 DEBUG: View 0 : 0.701986754967
+2016-08-24 09:57:43,711 DEBUG: View 1 : 0.483443708609
+2016-08-24 09:57:43,746 DEBUG: View 2 : 0.562913907285
+2016-08-24 09:57:43,753 DEBUG: View 3 : 0.470198675497
+2016-08-24 09:57:43,943 DEBUG: Best view : Methyl_
+2016-08-24 09:57:47,935 DEBUG: Start: Iteration 71
+2016-08-24 09:57:47,951 DEBUG: View 0 : 0.569536423841
+2016-08-24 09:57:47,958 DEBUG: View 1 : 0.609271523179
+2016-08-24 09:57:47,993 DEBUG: View 2 : 0.417218543046
+2016-08-24 09:57:48,001 DEBUG: View 3 : 0.41059602649
+2016-08-24 09:57:48,193 DEBUG: Best view : MiRNA__
+2016-08-24 09:57:52,229 DEBUG: Start: Iteration 72
+2016-08-24 09:57:52,245 DEBUG: View 0 : 0.377483443709
+2016-08-24 09:57:52,252 DEBUG: View 1 : 0.662251655629
+2016-08-24 09:57:52,290 DEBUG: View 2 : 0.569536423841
+2016-08-24 09:57:52,297 DEBUG: View 3 : 0.503311258278
+2016-08-24 09:57:52,491 DEBUG: Best view : MiRNA__
+2016-08-24 09:57:56,588 DEBUG: Start: Iteration 73
+2016-08-24 09:57:56,604 DEBUG: View 0 : 0.46357615894
+2016-08-24 09:57:56,612 DEBUG: View 1 : 0.615894039735
+2016-08-24 09:57:56,647 DEBUG: View 2 : 0.609271523179
+2016-08-24 09:57:56,654 DEBUG: View 3 : 0.403973509934
+2016-08-24 09:57:56,850 DEBUG: Best view : MiRNA__
+2016-08-24 09:58:00,989 DEBUG: Start: Iteration 74
+2016-08-24 09:58:01,005 DEBUG: View 0 : 0.602649006623
+2016-08-24 09:58:01,012 DEBUG: View 1 : 0.741721854305
+2016-08-24 09:58:01,054 DEBUG: View 2 : 0.58940397351
+2016-08-24 09:58:01,061 DEBUG: View 3 : 0.53642384106
+2016-08-24 09:58:01,259 DEBUG: Best view : MiRNA__
+2016-08-24 09:58:05,450 DEBUG: Start: Iteration 75
+2016-08-24 09:58:05,466 DEBUG: View 0 : 0.549668874172
+2016-08-24 09:58:05,473 DEBUG: View 1 : 0.662251655629
+2016-08-24 09:58:05,513 DEBUG: View 2 : 0.562913907285
+2016-08-24 09:58:05,520 DEBUG: View 3 : 0.450331125828
+2016-08-24 09:58:05,721 DEBUG: Best view : MiRNA__
+2016-08-24 09:58:09,965 DEBUG: Start: Iteration 76
+2016-08-24 09:58:09,981 DEBUG: View 0 : 0.529801324503
+2016-08-24 09:58:09,988 DEBUG: View 1 : 0.576158940397
+2016-08-24 09:58:10,029 DEBUG: View 2 : 0.569536423841
+2016-08-24 09:58:10,037 DEBUG: View 3 : 0.582781456954
+2016-08-24 09:58:10,238 DEBUG: Best view : MiRNA__
+2016-08-24 09:58:14,540 DEBUG: Start: Iteration 77
+2016-08-24 09:58:14,556 DEBUG: View 0 : 0.503311258278
+2016-08-24 09:58:14,564 DEBUG: View 1 : 0.701986754967
+2016-08-24 09:58:14,604 DEBUG: View 2 : 0.509933774834
+2016-08-24 09:58:14,612 DEBUG: View 3 : 0.53642384106
+2016-08-24 09:58:14,817 DEBUG: Best view : MiRNA__
+2016-08-24 09:58:19,168 DEBUG: Start: Iteration 78
+2016-08-24 09:58:19,183 DEBUG: View 0 : 0.735099337748
+2016-08-24 09:58:19,191 DEBUG: View 1 : 0.576158940397
+2016-08-24 09:58:19,233 DEBUG: View 2 : 0.609271523179
+2016-08-24 09:58:19,242 DEBUG: View 3 : 0.556291390728
+2016-08-24 09:58:19,448 DEBUG: Best view : Methyl_
+2016-08-24 09:58:23,864 DEBUG: Start: Iteration 79
+2016-08-24 09:58:23,880 DEBUG: View 0 : 0.629139072848
+2016-08-24 09:58:23,887 DEBUG: View 1 : 0.655629139073
+2016-08-24 09:58:23,929 DEBUG: View 2 : 0.423841059603
+2016-08-24 09:58:23,938 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:58:24,146 DEBUG: Best view : MiRNA__
+2016-08-24 09:58:28,610 DEBUG: Start: Iteration 80
+2016-08-24 09:58:28,626 DEBUG: View 0 : 0.562913907285
+2016-08-24 09:58:28,633 DEBUG: View 1 : 0.622516556291
+2016-08-24 09:58:28,676 DEBUG: View 2 : 0.490066225166
+2016-08-24 09:58:28,684 DEBUG: View 3 : 0.549668874172
+2016-08-24 09:58:28,895 DEBUG: Best view : MiRNA__
+2016-08-24 09:58:33,419 DEBUG: Start: Iteration 81
+2016-08-24 09:58:33,435 DEBUG: View 0 : 0.496688741722
+2016-08-24 09:58:33,442 DEBUG: View 1 : 0.41059602649
+2016-08-24 09:58:33,487 DEBUG: View 2 : 0.516556291391
+2016-08-24 09:58:33,495 DEBUG: View 3 : 0.576158940397
+2016-08-24 09:58:33,706 DEBUG: Best view : Clinic_
+2016-08-24 09:58:38,279 DEBUG: Start: Iteration 82
+2016-08-24 09:58:38,294 DEBUG: View 0 : 0.377483443709
+2016-08-24 09:58:38,302 DEBUG: View 1 : 0.516556291391
+2016-08-24 09:58:38,347 DEBUG: View 2 : 0.562913907285
+2016-08-24 09:58:38,355 DEBUG: View 3 : 0.46357615894
+2016-08-24 09:58:38,570 DEBUG: Best view : RANSeq_
+2016-08-24 09:58:43,207 DEBUG: Start: Iteration 83
+2016-08-24 09:58:43,222 DEBUG: View 0 : 0.456953642384
+2016-08-24 09:58:43,230 DEBUG: View 1 : 0.46357615894
+2016-08-24 09:58:43,275 DEBUG: View 2 : 0.523178807947
+2016-08-24 09:58:43,283 DEBUG: View 3 : 0.350993377483
+2016-08-24 09:58:43,501 DEBUG: Best view : RANSeq_
+2016-08-24 09:58:48,208 DEBUG: Start: Iteration 84
+2016-08-24 09:58:48,224 DEBUG: View 0 : 0.470198675497
+2016-08-24 09:58:48,231 DEBUG: View 1 : 0.602649006623
+2016-08-24 09:58:48,276 DEBUG: View 2 : 0.41059602649
+2016-08-24 09:58:48,285 DEBUG: View 3 : 0.602649006623
+2016-08-24 09:58:48,504 DEBUG: Best view : MiRNA__
+2016-08-24 09:58:53,266 DEBUG: Start: Iteration 85
+2016-08-24 09:58:53,282 DEBUG: View 0 : 0.503311258278
+2016-08-24 09:58:53,290 DEBUG: View 1 : 0.337748344371
+2016-08-24 09:58:53,335 DEBUG: View 2 : 0.483443708609
+2016-08-24 09:58:53,344 DEBUG: View 3 : 0.483443708609
+2016-08-24 09:58:53,567 DEBUG: Best view : Methyl_
+2016-08-24 09:58:58,390 DEBUG: Start: Iteration 86
+2016-08-24 09:58:58,406 DEBUG: View 0 : 0.46357615894
+2016-08-24 09:58:58,414 DEBUG: View 1 : 0.615894039735
+2016-08-24 09:58:58,460 DEBUG: View 2 : 0.562913907285
+2016-08-24 09:58:58,469 DEBUG: View 3 : 0.476821192053
+2016-08-24 09:58:58,700 DEBUG: Best view : MiRNA__
+2016-08-24 09:59:03,579 DEBUG: Start: Iteration 87
+2016-08-24 09:59:03,595 DEBUG: View 0 : 0.622516556291
+2016-08-24 09:59:03,602 DEBUG: View 1 : 0.860927152318
+2016-08-24 09:59:03,647 DEBUG: View 2 : 0.456953642384
+2016-08-24 09:59:03,656 DEBUG: View 3 : 0.556291390728
+2016-08-24 09:59:03,881 DEBUG: Best view : MiRNA__
+2016-08-24 09:59:08,810 DEBUG: Start: Iteration 88
+2016-08-24 09:59:08,826 DEBUG: View 0 : 0.635761589404
+2016-08-24 09:59:08,833 DEBUG: View 1 : 0.403973509934
+2016-08-24 09:59:08,879 DEBUG: View 2 : 0.470198675497
+2016-08-24 09:59:08,888 DEBUG: View 3 : 0.516556291391
+2016-08-24 09:59:09,115 DEBUG: Best view : Methyl_
+2016-08-24 09:59:14,094 DEBUG: Start: Iteration 89
+2016-08-24 09:59:14,111 DEBUG: View 0 : 0.562913907285
+2016-08-24 09:59:14,118 DEBUG: View 1 : 0.735099337748
+2016-08-24 09:59:14,164 DEBUG: View 2 : 0.456953642384
+2016-08-24 09:59:14,172 DEBUG: View 3 : 0.576158940397
+2016-08-24 09:59:14,401 DEBUG: Best view : MiRNA__
+2016-08-24 09:59:19,446 DEBUG: Start: Iteration 90
+2016-08-24 09:59:19,461 DEBUG: View 0 : 0.549668874172
+2016-08-24 09:59:19,469 DEBUG: View 1 : 0.490066225166
+2016-08-24 09:59:19,515 DEBUG: View 2 : 0.509933774834
+2016-08-24 09:59:19,524 DEBUG: View 3 : 0.622516556291
+2016-08-24 09:59:19,755 DEBUG: Best view : Clinic_
+2016-08-24 09:59:24,857 DEBUG: Start: Iteration 91
+2016-08-24 09:59:24,873 DEBUG: View 0 : 0.523178807947
+2016-08-24 09:59:24,881 DEBUG: View 1 : 0.635761589404
+2016-08-24 09:59:24,926 DEBUG: View 2 : 0.609271523179
+2016-08-24 09:59:24,935 DEBUG: View 3 : 0.450331125828
+2016-08-24 09:59:25,168 DEBUG: Best view : MiRNA__
+2016-08-24 09:59:30,327 DEBUG: Start: Iteration 92
+2016-08-24 09:59:30,343 DEBUG: View 0 : 0.430463576159
+2016-08-24 09:59:30,350 DEBUG: View 1 : 0.655629139073
+2016-08-24 09:59:30,396 DEBUG: View 2 : 0.450331125828
+2016-08-24 09:59:30,405 DEBUG: View 3 : 0.476821192053
+2016-08-24 09:59:30,639 DEBUG: Best view : MiRNA__
+2016-08-24 09:59:35,851 DEBUG: Start: Iteration 93
+2016-08-24 09:59:35,867 DEBUG: View 0 : 0.596026490066
+2016-08-24 09:59:35,874 DEBUG: View 1 : 0.635761589404
+2016-08-24 09:59:35,929 DEBUG: View 2 : 0.423841059603
+2016-08-24 09:59:35,937 DEBUG: View 3 : 0.58940397351
+2016-08-24 09:59:36,174 DEBUG: Best view : MiRNA__
+2016-08-24 09:59:41,498 DEBUG: Start: Iteration 94
+2016-08-24 09:59:41,514 DEBUG: View 0 : 0.582781456954
+2016-08-24 09:59:41,522 DEBUG: View 1 : 0.562913907285
+2016-08-24 09:59:41,557 DEBUG: View 2 : 0.622516556291
+2016-08-24 09:59:41,565 DEBUG: View 3 : 0.476821192053
+2016-08-24 09:59:41,807 DEBUG: Best view : Methyl_
+2016-08-24 09:59:47,218 DEBUG: Start: Iteration 95
+2016-08-24 09:59:47,234 DEBUG: View 0 : 0.390728476821
+2016-08-24 09:59:47,242 DEBUG: View 1 : 0.430463576159
+2016-08-24 09:59:47,277 DEBUG: View 2 : 0.46357615894
+2016-08-24 09:59:47,284 DEBUG: View 3 : 0.602649006623
+2016-08-24 09:59:47,529 DEBUG: Best view : Clinic_
+2016-08-24 09:59:53,061 DEBUG: Start: Iteration 96
+2016-08-24 09:59:53,077 DEBUG: View 0 : 0.688741721854
+2016-08-24 09:59:53,085 DEBUG: View 1 : 0.556291390728
+2016-08-24 09:59:53,120 DEBUG: View 2 : 0.629139072848
+2016-08-24 09:59:53,128 DEBUG: View 3 : 0.53642384106
+2016-08-24 09:59:53,372 DEBUG: Best view : Methyl_
+2016-08-24 09:59:58,805 DEBUG: Start: Iteration 97
+2016-08-24 09:59:58,821 DEBUG: View 0 : 0.668874172185
+2016-08-24 09:59:58,828 DEBUG: View 1 : 0.443708609272
+2016-08-24 09:59:58,864 DEBUG: View 2 : 0.516556291391
+2016-08-24 09:59:58,871 DEBUG: View 3 : 0.53642384106
+2016-08-24 09:59:59,116 DEBUG: Best view : Methyl_
+2016-08-24 10:00:04,596 DEBUG: Start: Iteration 98
+2016-08-24 10:00:04,612 DEBUG: View 0 : 0.509933774834
+2016-08-24 10:00:04,620 DEBUG: View 1 : 0.675496688742
+2016-08-24 10:00:04,655 DEBUG: View 2 : 0.46357615894
+2016-08-24 10:00:04,662 DEBUG: View 3 : 0.417218543046
+2016-08-24 10:00:04,910 DEBUG: Best view : MiRNA__
+2016-08-24 10:00:10,446 DEBUG: Start: Iteration 99
+2016-08-24 10:00:10,462 DEBUG: View 0 : 0.569536423841
+2016-08-24 10:00:10,469 DEBUG: View 1 : 0.509933774834
+2016-08-24 10:00:10,505 DEBUG: View 2 : 0.476821192053
+2016-08-24 10:00:10,512 DEBUG: View 3 : 0.503311258278
+2016-08-24 10:00:10,761 DEBUG: Best view : Methyl_
+2016-08-24 10:00:16,350 DEBUG: Start: Iteration 100
+2016-08-24 10:00:16,366 DEBUG: View 0 : 0.437086092715
+2016-08-24 10:00:16,374 DEBUG: View 1 : 0.503311258278
+2016-08-24 10:00:16,409 DEBUG: View 2 : 0.417218543046
+2016-08-24 10:00:16,416 DEBUG: View 3 : 0.602649006623
+2016-08-24 10:00:16,668 DEBUG: Best view : Clinic_
+2016-08-24 10:00:22,325 DEBUG: Start: Iteration 101
+2016-08-24 10:00:22,341 DEBUG: View 0 : 0.516556291391
+2016-08-24 10:00:22,348 DEBUG: View 1 : 0.701986754967
+2016-08-24 10:00:22,384 DEBUG: View 2 : 0.483443708609
+2016-08-24 10:00:22,391 DEBUG: View 3 : 0.483443708609
+2016-08-24 10:00:22,646 DEBUG: Best view : MiRNA__
+2016-08-24 10:00:28,562 DEBUG: Start: Iteration 102
+2016-08-24 10:00:28,580 DEBUG: View 0 : 0.516556291391
+2016-08-24 10:00:28,589 DEBUG: View 1 : 0.403973509934
+2016-08-24 10:00:28,626 DEBUG: View 2 : 0.443708609272
+2016-08-24 10:00:28,633 DEBUG: View 3 : 0.443708609272
+2016-08-24 10:00:28,896 DEBUG: Best view : Methyl_
+2016-08-24 10:00:34,676 INFO: Start: Classification
+2016-08-24 10:00:48,946 INFO: Done: Fold number 2
+2016-08-24 10:00:48,947 INFO: Done: Classification
+2016-08-24 10:00:48,947 INFO: Info: Time for Classification: 788[s]
+2016-08-24 10:00:48,947 INFO: Start: Result Analysis for Mumbo
+2016-08-24 10:01:20,963 INFO: Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 73.3311664568
+ -On Test : 76.2295081967
+ -On Validation : 80.0970873786
+
+Dataset info :
+ -Database name : ModifiedMultiOmic
+ -Labels : No, Yes
+ -Views : Methyl, MiRNA, RNASEQ, Clinical
+ -2 folds
+ - Validation set length : 103 for learning rate : 0.7
+
+Classification configuration :
+ -Algorithm used : Mumbo
+ -Iterations : 1000
+ -Weak Classifiers : DecisionTree with depth 1.0, sub-sampled at 0.006 on Methyl
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on MiRNA
+ -DecisionTree with depth 1.0, sub-sampled at 0.007 on RNASEQ
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on Clinical
+
+ Mean average accuracies and stats for each fold :
+ - Fold 0
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0516503067485
+ - Percentage of time chosen : 0.926
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0545460122699
+ - Percentage of time chosen : 0.048
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.0513374233129
+ - Percentage of time chosen : 0.007
+ - On Clinic_ :
+ - Mean average Accuracy : 0.0527116564417
+ - Percentage of time chosen : 0.019
+ - Fold 1
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0529801324503
+ - Percentage of time chosen : 0.922
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0560728476821
+ - Percentage of time chosen : 0.057
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.0528675496689
+ - Percentage of time chosen : 0.012
+ - On Clinic_ :
+ - Mean average Accuracy : 0.0512847682119
+ - Percentage of time chosen : 0.009
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 62.5766871166
+ Accuracy on test : 59.8360655738
+ Accuracy on validation : 52.427184466
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 61.4207938894
+ Accuracy on test : 66.393442623
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 68.0981595092
+ Accuracy on test : 68.0327868852
+ Accuracy on validation : 64.0776699029
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 73.5099337748
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 70.804046642
+ Accuracy on test : 70.9016393443
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 66.2576687117
+ Accuracy on test : 64.7540983607
+ Accuracy on validation : 62.1359223301
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 81.4569536424
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 74.7572815534
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 73.857311177
+ Accuracy on test : 72.5409836066
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 69.3251533742
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 67.9611650485
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 76.7155568196
+ Accuracy on test : 78.2786885246
+ - Iteration 6
+ Fold 1
+ Accuracy on train : 68.0981595092
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 68.932038835
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 80.5825242718
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 76.1020598871
+ Accuracy on test : 80.3278688525
+ - Iteration 7
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 74.5901639344
+ Accuracy on validation : 70.8737864078
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 83.4437086093
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.5316702556
+ Accuracy on test : 79.0983606557
+ - Iteration 8
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 74.5901639344
+ Accuracy on validation : 72.8155339806
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 83.4437086093
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.5316702556
+ Accuracy on test : 79.0983606557
+ - Iteration 9
+ Fold 1
+ Accuracy on train : 76.0736196319
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 85.2459016393
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.0897899484
+ Accuracy on test : 81.1475409836
+ - Iteration 10
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 70.8737864078
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 81.4569536424
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 80.5825242718
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 77.5382927721
+ Accuracy on test : 78.2786885246
+ - Iteration 11
+ Fold 1
+ Accuracy on train : 73.0061349693
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 72.8155339806
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 81.4569536424
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 77.2315443059
+ Accuracy on test : 80.3278688525
+ - Iteration 12
+ Fold 1
+ Accuracy on train : 70.5521472393
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 75.7281553398
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 76.821192053
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 82.5242718447
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 73.6866696461
+ Accuracy on test : 77.4590163934
+ - Iteration 13
+ Fold 1
+ Accuracy on train : 73.0061349693
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 82.5242718447
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.5560476171
+ Accuracy on test : 79.5081967213
+ - Iteration 14
+ Fold 1
+ Accuracy on train : 72.3926380368
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 82.119205298
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 86.4077669903
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 77.2559216674
+ Accuracy on test : 77.868852459
+ - Iteration 15
+ Fold 1
+ Accuracy on train : 71.7791411043
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 72.8155339806
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 77.9425506846
+ Accuracy on test : 78.6885245902
+ - Iteration 16
+ Fold 1
+ Accuracy on train : 71.1656441718
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 69.9029126214
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 89.3203883495
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 77.9669280462
+ Accuracy on test : 76.2295081967
+ - Iteration 17
+ Fold 1
+ Accuracy on train : 72.3926380368
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 70.8737864078
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.5804249787
+ Accuracy on test : 77.868852459
+ - Iteration 18
+ Fold 1
+ Accuracy on train : 72.3926380368
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 71.8446601942
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 85.4304635762
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 86.4077669903
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 78.9115508065
+ Accuracy on test : 79.0983606557
+ - Iteration 19
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 76.6990291262
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 85.4304635762
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 86.4077669903
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.1385446715
+ Accuracy on test : 77.868852459
+ - Iteration 20
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 75.7281553398
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 86.0927152318
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 87.3786407767
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 80.4696704993
+ Accuracy on test : 76.6393442623
+ - Iteration 21
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 85.4304635762
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 85.4368932039
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.525047739
+ Accuracy on test : 79.0983606557
+ - Iteration 22
+ Fold 1
+ Accuracy on train : 76.6871165644
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 83.4437086093
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 88.3495145631
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 80.0654125868
+ Accuracy on test : 77.0491803279
+ - Iteration 23
+ Fold 1
+ Accuracy on train : 75.4601226994
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 77.6699029126
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.11416731
+ Accuracy on test : 78.2786885246
+ - Iteration 24
+ Fold 1
+ Accuracy on train : 76.6871165644
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.3965384147
+ Accuracy on test : 77.0491803279
+ - Iteration 25
+ Fold 1
+ Accuracy on train : 77.9141104294
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 86.4077669903
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 81.341161175
+ Accuracy on test : 77.4590163934
+ - Iteration 26
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 77.6699029126
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 86.4077669903
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.8074188437
+ Accuracy on test : 77.0491803279
+ - Iteration 27
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 82.7814569536
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 83.4951456311
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.2005444277
+ Accuracy on test : 76.6393442623
+ - Iteration 28
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 83.4437086093
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 85.4368932039
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 79.1451671881
+ Accuracy on test : 77.4590163934
+ - Iteration 29
+ Fold 1
+ Accuracy on train : 73.0061349693
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 78.640776699
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 85.4368932039
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.5560476171
+ Accuracy on test : 77.868852459
+ - Iteration 30
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 80.5825242718
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 85.4368932039
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 79.8074188437
+ Accuracy on test : 77.0491803279
+ - Iteration 31
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 82.119205298
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 84.4660194175
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 78.4829155324
+ Accuracy on test : 77.4590163934
+ - Iteration 32
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 85.4368932039
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 79.8074188437
+ Accuracy on test : 75.8196721311
+ - Iteration 33
+ Fold 1
+ Accuracy on train : 74.2331288344
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 82.119205298
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.1761670662
+ Accuracy on test : 77.868852459
+ - Iteration 34
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 85.4368932039
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.8074188437
+ Accuracy on test : 77.868852459
+ - Iteration 35
+ Fold 1
+ Accuracy on train : 75.4601226994
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 85.4368932039
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.11416731
+ Accuracy on test : 79.5081967213
+ - Iteration 36
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 87.417218543
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 88.3495145631
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 81.131922155
+ Accuracy on test : 78.6885245902
+ - Iteration 37
+ Fold 1
+ Accuracy on train : 73.0061349693
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 83.4437086093
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.2249217893
+ Accuracy on test : 79.9180327869
+ - Iteration 38
+ Fold 1
+ Accuracy on train : 73.0061349693
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 86.7549668874
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 88.3495145631
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.8805509284
+ Accuracy on test : 79.5081967213
+ - Iteration 39
+ Fold 1
+ Accuracy on train : 74.2331288344
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 82.7814569536
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.507292894
+ Accuracy on test : 79.0983606557
+ - Iteration 40
+ Fold 1
+ Accuracy on train : 74.2331288344
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 82.7814569536
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 85.4368932039
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 78.507292894
+ Accuracy on test : 77.868852459
+ - Iteration 41
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 82.7814569536
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 85.4368932039
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.8140413603
+ Accuracy on test : 79.5081967213
+ - Iteration 42
+ Fold 1
+ Accuracy on train : 72.3926380368
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 76.6990291262
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 81.4569536424
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 86.4077669903
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 76.9247958396
+ Accuracy on test : 78.2786885246
+ - Iteration 43
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 78.640776699
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.1939219112
+ Accuracy on test : 79.9180327869
+ - Iteration 44
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.1939219112
+ Accuracy on test : 79.9180327869
+ - Iteration 45
+ Fold 1
+ Accuracy on train : 72.3926380368
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 76.6990291262
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 83.4437086093
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 87.3786407767
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 77.918173323
+ Accuracy on test : 77.868852459
+ - Iteration 46
+ Fold 1
+ Accuracy on train : 70.5521472393
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 76.6990291262
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 81.4569536424
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 76.0045504408
+ Accuracy on test : 77.0491803279
+ - Iteration 47
+ Fold 1
+ Accuracy on train : 71.1656441718
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 79.4701986755
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 86.4077669903
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 75.3179214236
+ Accuracy on test : 76.6393442623
+ - Iteration 48
+ Fold 1
+ Accuracy on train : 71.1656441718
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 79.4701986755
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 87.3786407767
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 75.3179214236
+ Accuracy on test : 77.868852459
+ - Iteration 49
+ Fold 1
+ Accuracy on train : 70.5521472393
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 77.6699029126
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+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 996
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 997
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 998
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 999
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 1000
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:07:17 0:00:14
+ Fold 2 0:12:53 0:00:14
+ Total 0:20:11 0:00:28
+ So a total classification time of 0:13:08.
+
+
+2016-08-24 10:01:21,935 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-100121Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-100121Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png
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diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-100121Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-100121Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt
new file mode 100644
index 00000000..d4524b7c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-100121Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic.txt
@@ -0,0 +1,14060 @@
+ Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 73.3311664568
+ -On Test : 76.2295081967
+ -On Validation : 80.0970873786
+
+Dataset info :
+ -Database name : ModifiedMultiOmic
+ -Labels : No, Yes
+ -Views : Methyl, MiRNA, RNASEQ, Clinical
+ -2 folds
+ - Validation set length : 103 for learning rate : 0.7
+
+Classification configuration :
+ -Algorithm used : Mumbo
+ -Iterations : 1000
+ -Weak Classifiers : DecisionTree with depth 1.0, sub-sampled at 0.006 on Methyl
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on MiRNA
+ -DecisionTree with depth 1.0, sub-sampled at 0.007 on RNASEQ
+ -DecisionTree with depth 1.0, sub-sampled at 0.006 on Clinical
+
+ Mean average accuracies and stats for each fold :
+ - Fold 0
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0516503067485
+ - Percentage of time chosen : 0.926
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0545460122699
+ - Percentage of time chosen : 0.048
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.0513374233129
+ - Percentage of time chosen : 0.007
+ - On Clinic_ :
+ - Mean average Accuracy : 0.0527116564417
+ - Percentage of time chosen : 0.019
+ - Fold 1
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0529801324503
+ - Percentage of time chosen : 0.922
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0560728476821
+ - Percentage of time chosen : 0.057
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.0528675496689
+ - Percentage of time chosen : 0.012
+ - On Clinic_ :
+ - Mean average Accuracy : 0.0512847682119
+ - Percentage of time chosen : 0.009
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 62.5766871166
+ Accuracy on test : 59.8360655738
+ Accuracy on validation : 52.427184466
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 61.4207938894
+ Accuracy on test : 66.393442623
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 68.0981595092
+ Accuracy on test : 68.0327868852
+ Accuracy on validation : 64.0776699029
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 73.5099337748
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 70.804046642
+ Accuracy on test : 70.9016393443
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 66.2576687117
+ Accuracy on test : 64.7540983607
+ Accuracy on validation : 62.1359223301
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 81.4569536424
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 74.7572815534
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 73.857311177
+ Accuracy on test : 72.5409836066
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 69.3251533742
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 67.9611650485
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 76.7155568196
+ Accuracy on test : 78.2786885246
+ - Iteration 6
+ Fold 1
+ Accuracy on train : 68.0981595092
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 68.932038835
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 80.5825242718
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 76.1020598871
+ Accuracy on test : 80.3278688525
+ - Iteration 7
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 74.5901639344
+ Accuracy on validation : 70.8737864078
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 83.4437086093
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.5316702556
+ Accuracy on test : 79.0983606557
+ - Iteration 8
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 74.5901639344
+ Accuracy on validation : 72.8155339806
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 83.4437086093
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.5316702556
+ Accuracy on test : 79.0983606557
+ - Iteration 9
+ Fold 1
+ Accuracy on train : 76.0736196319
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 85.2459016393
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.0897899484
+ Accuracy on test : 81.1475409836
+ - Iteration 10
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 70.8737864078
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 81.4569536424
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 80.5825242718
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 77.5382927721
+ Accuracy on test : 78.2786885246
+ - Iteration 11
+ Fold 1
+ Accuracy on train : 73.0061349693
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 72.8155339806
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 81.4569536424
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 77.2315443059
+ Accuracy on test : 80.3278688525
+ - Iteration 12
+ Fold 1
+ Accuracy on train : 70.5521472393
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 75.7281553398
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 76.821192053
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 82.5242718447
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 73.6866696461
+ Accuracy on test : 77.4590163934
+ - Iteration 13
+ Fold 1
+ Accuracy on train : 73.0061349693
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 82.5242718447
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.5560476171
+ Accuracy on test : 79.5081967213
+ - Iteration 14
+ Fold 1
+ Accuracy on train : 72.3926380368
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 82.119205298
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 86.4077669903
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 77.2559216674
+ Accuracy on test : 77.868852459
+ - Iteration 15
+ Fold 1
+ Accuracy on train : 71.7791411043
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 72.8155339806
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 77.9425506846
+ Accuracy on test : 78.6885245902
+ - Iteration 16
+ Fold 1
+ Accuracy on train : 71.1656441718
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 69.9029126214
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 89.3203883495
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 77.9669280462
+ Accuracy on test : 76.2295081967
+ - Iteration 17
+ Fold 1
+ Accuracy on train : 72.3926380368
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 70.8737864078
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.5804249787
+ Accuracy on test : 77.868852459
+ - Iteration 18
+ Fold 1
+ Accuracy on train : 72.3926380368
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 71.8446601942
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 85.4304635762
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 86.4077669903
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 78.9115508065
+ Accuracy on test : 79.0983606557
+ - Iteration 19
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 76.6990291262
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 85.4304635762
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 86.4077669903
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.1385446715
+ Accuracy on test : 77.868852459
+ - Iteration 20
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 75.7281553398
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 86.0927152318
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 87.3786407767
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 80.4696704993
+ Accuracy on test : 76.6393442623
+ - Iteration 21
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 85.4304635762
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 85.4368932039
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.525047739
+ Accuracy on test : 79.0983606557
+ - Iteration 22
+ Fold 1
+ Accuracy on train : 76.6871165644
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 83.4437086093
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 88.3495145631
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 80.0654125868
+ Accuracy on test : 77.0491803279
+ - Iteration 23
+ Fold 1
+ Accuracy on train : 75.4601226994
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 77.6699029126
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.11416731
+ Accuracy on test : 78.2786885246
+ - Iteration 24
+ Fold 1
+ Accuracy on train : 76.6871165644
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.3965384147
+ Accuracy on test : 77.0491803279
+ - Iteration 25
+ Fold 1
+ Accuracy on train : 77.9141104294
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 86.4077669903
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 81.341161175
+ Accuracy on test : 77.4590163934
+ - Iteration 26
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 77.6699029126
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 86.4077669903
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.8074188437
+ Accuracy on test : 77.0491803279
+ - Iteration 27
+ Fold 1
+ Accuracy on train : 73.6196319018
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 82.7814569536
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 83.4951456311
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.2005444277
+ Accuracy on test : 76.6393442623
+ - Iteration 28
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 83.4437086093
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 85.4368932039
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 79.1451671881
+ Accuracy on test : 77.4590163934
+ - Iteration 29
+ Fold 1
+ Accuracy on train : 73.0061349693
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 78.640776699
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 84.1059602649
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 85.4368932039
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.5560476171
+ Accuracy on test : 77.868852459
+ - Iteration 30
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 80.5825242718
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 85.4368932039
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 79.8074188437
+ Accuracy on test : 77.0491803279
+ - Iteration 31
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 82.119205298
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 84.4660194175
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 78.4829155324
+ Accuracy on test : 77.4590163934
+ - Iteration 32
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 76.2295081967
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 75.4098360656
+ Accuracy on validation : 85.4368932039
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 79.8074188437
+ Accuracy on test : 75.8196721311
+ - Iteration 33
+ Fold 1
+ Accuracy on train : 74.2331288344
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 82.119205298
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.1761670662
+ Accuracy on test : 77.868852459
+ - Iteration 34
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 85.4368932039
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.8074188437
+ Accuracy on test : 77.868852459
+ - Iteration 35
+ Fold 1
+ Accuracy on train : 75.4601226994
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 84.7682119205
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 85.4368932039
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.11416731
+ Accuracy on test : 79.5081967213
+ - Iteration 36
+ Fold 1
+ Accuracy on train : 74.8466257669
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 87.417218543
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 88.3495145631
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 81.131922155
+ Accuracy on test : 78.6885245902
+ - Iteration 37
+ Fold 1
+ Accuracy on train : 73.0061349693
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 83.4437086093
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.2249217893
+ Accuracy on test : 79.9180327869
+ - Iteration 38
+ Fold 1
+ Accuracy on train : 73.0061349693
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 86.7549668874
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 88.3495145631
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.8805509284
+ Accuracy on test : 79.5081967213
+ - Iteration 39
+ Fold 1
+ Accuracy on train : 74.2331288344
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 82.7814569536
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.507292894
+ Accuracy on test : 79.0983606557
+ - Iteration 40
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+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 987
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 988
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 989
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 990
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 991
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 992
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 993
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 994
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 995
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 996
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 997
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 998
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 999
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+ - Iteration 1000
+ Fold 1
+ Accuracy on train : 63.1901840491
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 60.2649006623
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 61.7275423557
+ Accuracy on test : 72.9508196721
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:07:17 0:00:14
+ Fold 2 0:12:53 0:00:14
+ Total 0:20:11 0:00:28
+ So a total classification time of 0:13:08.
+
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-110726-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-110726-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..31fcc125
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-110726-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,390 @@
+2016-08-24 11:07:26,238 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 11:07:26,238 INFO: Info: Labels used: No, Yes
+2016-08-24 11:07:26,239 INFO: Info: Length of dataset:347
+2016-08-24 11:07:26,240 INFO: ### Main Programm for Multiview Classification
+2016-08-24 11:07:26,240 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 11:07:26,241 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 11:07:26,241 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 11:07:26,242 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 11:07:26,242 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 11:07:26,242 INFO: Done: Read Database Files
+2016-08-24 11:07:26,242 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 11:07:26,246 INFO: Done: Determine validation split
+2016-08-24 11:07:26,246 INFO: Start: Determine 2 folds
+2016-08-24 11:07:26,254 INFO: Info: Length of Learning Sets: 122
+2016-08-24 11:07:26,254 INFO: Info: Length of Testing Sets: 122
+2016-08-24 11:07:26,254 INFO: Info: Length of Validation Set: 103
+2016-08-24 11:07:26,254 INFO: Done: Determine folds
+2016-08-24 11:07:26,254 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 11:07:26,254 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 11:07:26,254 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 11:07:33,791 DEBUG: 0.558270893372Poulet
+2016-08-24 11:07:33,791 DEBUG: 0.521268011527Poulet
+2016-08-24 11:07:33,791 DEBUG: 0.521556195965Poulet
+2016-08-24 11:07:33,792 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:07:33,792 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 11:07:35,798 DEBUG: 0.530893371758Poulet
+2016-08-24 11:07:35,798 DEBUG: 0.530720461095Poulet
+2016-08-24 11:07:35,798 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:07:35,799 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 11:07:52,803 DEBUG: 0.583227665706Poulet
+2016-08-24 11:07:52,803 DEBUG: 0.569798270893Poulet
+2016-08-24 11:07:52,803 DEBUG: 0.543746397695Poulet
+2016-08-24 11:07:52,803 DEBUG: 0.520749279539Poulet
+2016-08-24 11:07:52,803 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:07:52,804 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 11:07:54,548 DEBUG: 0.559827089337Poulet
+2016-08-24 11:07:54,549 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:07:54,549 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 11:08:34,507 DEBUG: 0.561383285303Poulet
+2016-08-24 11:08:34,507 DEBUG: 0.549337175793Poulet
+2016-08-24 11:08:34,508 DEBUG: 0.511930835735Poulet
+2016-08-24 11:08:34,508 DEBUG: 0.514524495677Poulet
+2016-08-24 11:08:34,508 DEBUG: 0.514755043228Poulet
+2016-08-24 11:08:34,512 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:08:34,512 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 11:08:34,512 INFO: Start: Fold number 1
+2016-08-24 11:08:36,691 DEBUG: Start: Iteration 1
+2016-08-24 11:08:36,707 DEBUG: View 0 : 0.620253164557
+2016-08-24 11:08:36,715 DEBUG: View 1 : 0.26582278481
+2016-08-24 11:08:36,744 DEBUG: View 2 : 0.620253164557
+2016-08-24 11:08:36,751 DEBUG: View 3 : 0.569620253165
+2016-08-24 11:08:36,793 DEBUG: Best view : Clinic_
+2016-08-24 11:08:36,864 DEBUG: Start: Iteration 2
+2016-08-24 11:08:36,881 DEBUG: View 0 : 0.436708860759
+2016-08-24 11:08:36,888 DEBUG: View 1 : 0.481012658228
+2016-08-24 11:08:36,925 DEBUG: View 2 : 0.405063291139
+2016-08-24 11:08:36,932 DEBUG: View 3 : 0.626582278481
+2016-08-24 11:08:36,977 DEBUG: Best view : Clinic_
+2016-08-24 11:08:37,106 DEBUG: Start: Iteration 3
+2016-08-24 11:08:37,122 DEBUG: View 0 : 0.53164556962
+2016-08-24 11:08:37,130 DEBUG: View 1 : 0.626582278481
+2016-08-24 11:08:37,168 DEBUG: View 2 : 0.518987341772
+2016-08-24 11:08:37,176 DEBUG: View 3 : 0.575949367089
+2016-08-24 11:08:37,230 DEBUG: Best view : MiRNA__
+2016-08-24 11:08:37,420 DEBUG: Start: Iteration 4
+2016-08-24 11:08:37,437 DEBUG: View 0 : 0.677215189873
+2016-08-24 11:08:37,445 DEBUG: View 1 : 0.588607594937
+2016-08-24 11:08:37,482 DEBUG: View 2 : 0.436708860759
+2016-08-24 11:08:37,489 DEBUG: View 3 : 0.411392405063
+2016-08-24 11:08:37,546 DEBUG: Best view : Methyl_
+2016-08-24 11:08:37,798 DEBUG: Start: Iteration 5
+2016-08-24 11:08:37,815 DEBUG: View 0 : 0.506329113924
+2016-08-24 11:08:37,823 DEBUG: View 1 : 0.474683544304
+2016-08-24 11:08:37,861 DEBUG: View 2 : 0.392405063291
+2016-08-24 11:08:37,868 DEBUG: View 3 : 0.436708860759
+2016-08-24 11:08:37,927 DEBUG: Best view : MiRNA__
+2016-08-24 11:08:38,240 DEBUG: Start: Iteration 6
+2016-08-24 11:08:38,256 DEBUG: View 0 : 0.449367088608
+2016-08-24 11:08:38,264 DEBUG: View 1 : 0.626582278481
+2016-08-24 11:08:38,302 DEBUG: View 2 : 0.417721518987
+2016-08-24 11:08:38,309 DEBUG: View 3 : 0.53164556962
+2016-08-24 11:08:38,371 DEBUG: Best view : Clinic_
+2016-08-24 11:08:38,740 DEBUG: Start: Iteration 7
+2016-08-24 11:08:38,756 DEBUG: View 0 : 0.443037974684
+2016-08-24 11:08:38,764 DEBUG: View 1 : 0.487341772152
+2016-08-24 11:08:38,800 DEBUG: View 2 : 0.582278481013
+2016-08-24 11:08:38,808 DEBUG: View 3 : 0.512658227848
+2016-08-24 11:08:38,871 DEBUG: Best view : RANSeq_
+2016-08-24 11:08:39,315 DEBUG: Start: Iteration 8
+2016-08-24 11:08:39,332 DEBUG: View 0 : 0.417721518987
+2016-08-24 11:08:39,339 DEBUG: View 1 : 0.462025316456
+2016-08-24 11:08:39,377 DEBUG: View 2 : 0.474683544304
+2016-08-24 11:08:39,384 DEBUG: View 3 : 0.556962025316
+2016-08-24 11:08:39,450 DEBUG: Best view : Clinic_
+2016-08-24 11:08:39,972 DEBUG: Start: Iteration 9
+2016-08-24 11:08:39,990 DEBUG: View 0 : 0.5
+2016-08-24 11:08:39,998 DEBUG: View 1 : 0.651898734177
+2016-08-24 11:08:40,037 DEBUG: View 2 : 0.537974683544
+2016-08-24 11:08:40,045 DEBUG: View 3 : 0.392405063291
+2016-08-24 11:08:40,115 DEBUG: Best view : MiRNA__
+2016-08-24 11:08:40,698 DEBUG: Start: Iteration 10
+2016-08-24 11:08:40,715 DEBUG: View 0 : 0.594936708861
+2016-08-24 11:08:40,723 DEBUG: View 1 : 0.354430379747
+2016-08-24 11:08:40,760 DEBUG: View 2 : 0.613924050633
+2016-08-24 11:08:40,768 DEBUG: View 3 : 0.544303797468
+2016-08-24 11:08:40,837 DEBUG: Best view : RANSeq_
+2016-08-24 11:08:41,469 DEBUG: Start: Iteration 11
+2016-08-24 11:08:41,485 DEBUG: View 0 : 0.367088607595
+2016-08-24 11:08:41,493 DEBUG: View 1 : 0.658227848101
+2016-08-24 11:08:41,530 DEBUG: View 2 : 0.537974683544
+2016-08-24 11:08:41,537 DEBUG: View 3 : 0.582278481013
+2016-08-24 11:08:41,610 DEBUG: Best view : MiRNA__
+2016-08-24 11:08:42,303 DEBUG: Start: Iteration 12
+2016-08-24 11:08:42,319 DEBUG: View 0 : 0.424050632911
+2016-08-24 11:08:42,327 DEBUG: View 1 : 0.373417721519
+2016-08-24 11:08:42,364 DEBUG: View 2 : 0.462025316456
+2016-08-24 11:08:42,371 DEBUG: View 3 : 0.588607594937
+2016-08-24 11:08:42,445 DEBUG: Best view : Clinic_
+2016-08-24 11:08:43,193 DEBUG: Start: Iteration 13
+2016-08-24 11:08:43,209 DEBUG: View 0 : 0.474683544304
+2016-08-24 11:08:43,217 DEBUG: View 1 : 0.575949367089
+2016-08-24 11:08:43,253 DEBUG: View 2 : 0.411392405063
+2016-08-24 11:08:43,261 DEBUG: View 3 : 0.386075949367
+2016-08-24 11:08:43,339 DEBUG: Best view : MiRNA__
+2016-08-24 11:08:44,142 DEBUG: Start: Iteration 14
+2016-08-24 11:08:44,159 DEBUG: View 0 : 0.620253164557
+2016-08-24 11:08:44,166 DEBUG: View 1 : 0.493670886076
+2016-08-24 11:08:44,203 DEBUG: View 2 : 0.575949367089
+2016-08-24 11:08:44,211 DEBUG: View 3 : 0.658227848101
+2016-08-24 11:08:44,291 DEBUG: Best view : Clinic_
+2016-08-24 11:08:45,149 DEBUG: Start: Iteration 15
+2016-08-24 11:08:45,165 DEBUG: View 0 : 0.518987341772
+2016-08-24 11:08:45,173 DEBUG: View 1 : 0.46835443038
+2016-08-24 11:08:45,210 DEBUG: View 2 : 0.392405063291
+2016-08-24 11:08:45,218 DEBUG: View 3 : 0.443037974684
+2016-08-24 11:08:45,299 DEBUG: Best view : MiRNA__
+2016-08-24 11:08:46,226 DEBUG: Start: Iteration 16
+2016-08-24 11:08:46,243 DEBUG: View 0 : 0.556962025316
+2016-08-24 11:08:46,251 DEBUG: View 1 : 0.398734177215
+2016-08-24 11:08:46,287 DEBUG: View 2 : 0.550632911392
+2016-08-24 11:08:46,294 DEBUG: View 3 : 0.664556962025
+2016-08-24 11:08:46,380 DEBUG: Best view : Clinic_
+2016-08-24 11:08:47,364 DEBUG: Start: Iteration 17
+2016-08-24 11:08:47,380 DEBUG: View 0 : 0.626582278481
+2016-08-24 11:08:47,389 DEBUG: View 1 : 0.506329113924
+2016-08-24 11:08:47,428 DEBUG: View 2 : 0.474683544304
+2016-08-24 11:08:47,436 DEBUG: View 3 : 0.455696202532
+2016-08-24 11:08:47,522 DEBUG: Best view : Methyl_
+2016-08-24 11:08:48,613 DEBUG: Start: Iteration 18
+2016-08-24 11:08:48,629 DEBUG: View 0 : 0.525316455696
+2016-08-24 11:08:48,636 DEBUG: View 1 : 0.715189873418
+2016-08-24 11:08:48,673 DEBUG: View 2 : 0.386075949367
+2016-08-24 11:08:48,681 DEBUG: View 3 : 0.455696202532
+2016-08-24 11:08:48,770 DEBUG: Best view : MiRNA__
+2016-08-24 11:08:49,927 DEBUG: Start: Iteration 19
+2016-08-24 11:08:49,947 DEBUG: View 0 : 0.550632911392
+2016-08-24 11:08:49,955 DEBUG: View 1 : 0.569620253165
+2016-08-24 11:08:49,992 DEBUG: View 2 : 0.405063291139
+2016-08-24 11:08:50,000 DEBUG: View 3 : 0.575949367089
+2016-08-24 11:08:50,090 DEBUG: Best view : Clinic_
+2016-08-24 11:08:51,285 DEBUG: Start: Iteration 20
+2016-08-24 11:08:51,304 DEBUG: View 0 : 0.424050632911
+2016-08-24 11:08:51,313 DEBUG: View 1 : 0.354430379747
+2016-08-24 11:08:51,355 DEBUG: View 2 : 0.474683544304
+2016-08-24 11:08:51,363 DEBUG: View 3 : 0.436708860759
+2016-08-24 11:08:51,363 WARNING: WARNING: All bad for iteration 19
+2016-08-24 11:08:51,460 DEBUG: Best view : RANSeq_
+2016-08-24 11:08:52,684 DEBUG: Start: Iteration 21
+2016-08-24 11:08:52,701 DEBUG: View 0 : 0.563291139241
+2016-08-24 11:08:52,708 DEBUG: View 1 : 0.29746835443
+2016-08-24 11:08:52,745 DEBUG: View 2 : 0.487341772152
+2016-08-24 11:08:52,753 DEBUG: View 3 : 0.462025316456
+2016-08-24 11:08:52,849 DEBUG: Best view : Methyl_
+2016-08-24 11:08:54,166 DEBUG: Start: Iteration 22
+2016-08-24 11:08:54,182 DEBUG: View 0 : 0.563291139241
+2016-08-24 11:08:54,190 DEBUG: View 1 : 0.392405063291
+2016-08-24 11:08:54,232 DEBUG: View 2 : 0.518987341772
+2016-08-24 11:08:54,239 DEBUG: View 3 : 0.658227848101
+2016-08-24 11:08:54,339 DEBUG: Best view : Clinic_
+2016-08-24 11:08:55,764 DEBUG: Start: Iteration 23
+2016-08-24 11:08:55,784 DEBUG: View 0 : 0.405063291139
+2016-08-24 11:08:55,796 DEBUG: View 1 : 0.696202531646
+2016-08-24 11:08:55,833 DEBUG: View 2 : 0.512658227848
+2016-08-24 11:08:55,840 DEBUG: View 3 : 0.481012658228
+2016-08-24 11:08:55,941 DEBUG: Best view : MiRNA__
+2016-08-24 11:08:57,391 DEBUG: Start: Iteration 24
+2016-08-24 11:08:57,408 DEBUG: View 0 : 0.563291139241
+2016-08-24 11:08:57,416 DEBUG: View 1 : 0.512658227848
+2016-08-24 11:08:57,455 DEBUG: View 2 : 0.626582278481
+2016-08-24 11:08:57,465 DEBUG: View 3 : 0.556962025316
+2016-08-24 11:08:57,578 DEBUG: Best view : RANSeq_
+2016-08-24 11:08:59,113 DEBUG: Start: Iteration 25
+2016-08-24 11:08:59,129 DEBUG: View 0 : 0.462025316456
+2016-08-24 11:08:59,137 DEBUG: View 1 : 0.626582278481
+2016-08-24 11:08:59,174 DEBUG: View 2 : 0.462025316456
+2016-08-24 11:08:59,181 DEBUG: View 3 : 0.474683544304
+2016-08-24 11:08:59,294 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:00,911 DEBUG: Start: Iteration 26
+2016-08-24 11:09:00,929 DEBUG: View 0 : 0.316455696203
+2016-08-24 11:09:00,937 DEBUG: View 1 : 0.29746835443
+2016-08-24 11:09:00,974 DEBUG: View 2 : 0.632911392405
+2016-08-24 11:09:00,981 DEBUG: View 3 : 0.544303797468
+2016-08-24 11:09:01,092 DEBUG: Best view : RANSeq_
+2016-08-24 11:09:02,722 DEBUG: Start: Iteration 27
+2016-08-24 11:09:02,739 DEBUG: View 0 : 0.53164556962
+2016-08-24 11:09:02,747 DEBUG: View 1 : 0.620253164557
+2016-08-24 11:09:02,783 DEBUG: View 2 : 0.594936708861
+2016-08-24 11:09:02,791 DEBUG: View 3 : 0.53164556962
+2016-08-24 11:09:02,898 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:04,562 DEBUG: Start: Iteration 28
+2016-08-24 11:09:04,579 DEBUG: View 0 : 0.398734177215
+2016-08-24 11:09:04,587 DEBUG: View 1 : 0.487341772152
+2016-08-24 11:09:04,623 DEBUG: View 2 : 0.449367088608
+2016-08-24 11:09:04,631 DEBUG: View 3 : 0.537974683544
+2016-08-24 11:09:04,742 DEBUG: Best view : Clinic_
+2016-08-24 11:09:06,650 DEBUG: Start: Iteration 29
+2016-08-24 11:09:06,666 DEBUG: View 0 : 0.544303797468
+2016-08-24 11:09:06,674 DEBUG: View 1 : 0.708860759494
+2016-08-24 11:09:06,711 DEBUG: View 2 : 0.462025316456
+2016-08-24 11:09:06,719 DEBUG: View 3 : 0.575949367089
+2016-08-24 11:09:06,836 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:08,697 DEBUG: Start: Iteration 30
+2016-08-24 11:09:08,714 DEBUG: View 0 : 0.46835443038
+2016-08-24 11:09:08,721 DEBUG: View 1 : 0.512658227848
+2016-08-24 11:09:08,759 DEBUG: View 2 : 0.424050632911
+2016-08-24 11:09:08,766 DEBUG: View 3 : 0.462025316456
+2016-08-24 11:09:08,882 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:10,855 DEBUG: Start: Iteration 31
+2016-08-24 11:09:10,874 DEBUG: View 0 : 0.563291139241
+2016-08-24 11:09:10,882 DEBUG: View 1 : 0.335443037975
+2016-08-24 11:09:10,922 DEBUG: View 2 : 0.512658227848
+2016-08-24 11:09:10,930 DEBUG: View 3 : 0.487341772152
+2016-08-24 11:09:11,065 DEBUG: Best view : Methyl_
+2016-08-24 11:09:13,022 DEBUG: Start: Iteration 32
+2016-08-24 11:09:13,039 DEBUG: View 0 : 0.424050632911
+2016-08-24 11:09:13,047 DEBUG: View 1 : 0.632911392405
+2016-08-24 11:09:13,089 DEBUG: View 2 : 0.474683544304
+2016-08-24 11:09:13,097 DEBUG: View 3 : 0.582278481013
+2016-08-24 11:09:13,225 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:15,276 DEBUG: Start: Iteration 33
+2016-08-24 11:09:15,292 DEBUG: View 0 : 0.493670886076
+2016-08-24 11:09:15,300 DEBUG: View 1 : 0.651898734177
+2016-08-24 11:09:15,340 DEBUG: View 2 : 0.462025316456
+2016-08-24 11:09:15,349 DEBUG: View 3 : 0.367088607595
+2016-08-24 11:09:15,480 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:17,640 DEBUG: Start: Iteration 34
+2016-08-24 11:09:17,656 DEBUG: View 0 : 0.455696202532
+2016-08-24 11:09:17,664 DEBUG: View 1 : 0.79746835443
+2016-08-24 11:09:17,701 DEBUG: View 2 : 0.594936708861
+2016-08-24 11:09:17,708 DEBUG: View 3 : 0.594936708861
+2016-08-24 11:09:17,832 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:19,911 DEBUG: Start: Iteration 35
+2016-08-24 11:09:19,928 DEBUG: View 0 : 0.575949367089
+2016-08-24 11:09:19,935 DEBUG: View 1 : 0.601265822785
+2016-08-24 11:09:19,971 DEBUG: View 2 : 0.613924050633
+2016-08-24 11:09:19,979 DEBUG: View 3 : 0.443037974684
+2016-08-24 11:09:20,105 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:22,296 DEBUG: Start: Iteration 36
+2016-08-24 11:09:22,312 DEBUG: View 0 : 0.639240506329
+2016-08-24 11:09:22,320 DEBUG: View 1 : 0.550632911392
+2016-08-24 11:09:22,357 DEBUG: View 2 : 0.424050632911
+2016-08-24 11:09:22,365 DEBUG: View 3 : 0.474683544304
+2016-08-24 11:09:22,498 DEBUG: Best view : Methyl_
+2016-08-24 11:09:24,768 DEBUG: Start: Iteration 37
+2016-08-24 11:09:24,785 DEBUG: View 0 : 0.455696202532
+2016-08-24 11:09:24,792 DEBUG: View 1 : 0.677215189873
+2016-08-24 11:09:24,831 DEBUG: View 2 : 0.46835443038
+2016-08-24 11:09:24,838 DEBUG: View 3 : 0.518987341772
+2016-08-24 11:09:24,970 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:27,288 DEBUG: Start: Iteration 38
+2016-08-24 11:09:27,305 DEBUG: View 0 : 0.411392405063
+2016-08-24 11:09:27,313 DEBUG: View 1 : 0.46835443038
+2016-08-24 11:09:27,350 DEBUG: View 2 : 0.436708860759
+2016-08-24 11:09:27,357 DEBUG: View 3 : 0.474683544304
+2016-08-24 11:09:27,358 WARNING: WARNING: All bad for iteration 37
+2016-08-24 11:09:27,495 DEBUG: Best view : Clinic_
+2016-08-24 11:09:29,938 DEBUG: Start: Iteration 39
+2016-08-24 11:09:29,954 DEBUG: View 0 : 0.563291139241
+2016-08-24 11:09:29,962 DEBUG: View 1 : 0.379746835443
+2016-08-24 11:09:29,998 DEBUG: View 2 : 0.430379746835
+2016-08-24 11:09:30,006 DEBUG: View 3 : 0.443037974684
+2016-08-24 11:09:30,143 DEBUG: Best view : Methyl_
+2016-08-24 11:09:32,730 DEBUG: Start: Iteration 40
+2016-08-24 11:09:32,748 DEBUG: View 0 : 0.462025316456
+2016-08-24 11:09:32,755 DEBUG: View 1 : 0.626582278481
+2016-08-24 11:09:32,797 DEBUG: View 2 : 0.411392405063
+2016-08-24 11:09:32,804 DEBUG: View 3 : 0.53164556962
+2016-08-24 11:09:32,941 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:35,450 DEBUG: Start: Iteration 41
+2016-08-24 11:09:35,467 DEBUG: View 0 : 0.556962025316
+2016-08-24 11:09:35,474 DEBUG: View 1 : 0.620253164557
+2016-08-24 11:09:35,511 DEBUG: View 2 : 0.443037974684
+2016-08-24 11:09:35,518 DEBUG: View 3 : 0.594936708861
+2016-08-24 11:09:35,658 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:38,266 DEBUG: Start: Iteration 42
+2016-08-24 11:09:38,283 DEBUG: View 0 : 0.632911392405
+2016-08-24 11:09:38,290 DEBUG: View 1 : 0.582278481013
+2016-08-24 11:09:38,328 DEBUG: View 2 : 0.550632911392
+2016-08-24 11:09:38,335 DEBUG: View 3 : 0.398734177215
+2016-08-24 11:09:38,481 DEBUG: Best view : Methyl_
+2016-08-24 11:09:41,096 DEBUG: Start: Iteration 43
+2016-08-24 11:09:41,113 DEBUG: View 0 : 0.430379746835
+2016-08-24 11:09:41,121 DEBUG: View 1 : 0.613924050633
+2016-08-24 11:09:41,158 DEBUG: View 2 : 0.474683544304
+2016-08-24 11:09:41,165 DEBUG: View 3 : 0.512658227848
+2016-08-24 11:09:41,315 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:43,945 DEBUG: Start: Iteration 44
+2016-08-24 11:09:43,961 DEBUG: View 0 : 0.462025316456
+2016-08-24 11:09:43,969 DEBUG: View 1 : 0.537974683544
+2016-08-24 11:09:44,024 DEBUG: View 2 : 0.594936708861
+2016-08-24 11:09:44,038 DEBUG: View 3 : 0.411392405063
+2016-08-24 11:09:44,211 DEBUG: Best view : RANSeq_
+2016-08-24 11:09:46,944 DEBUG: Start: Iteration 45
+2016-08-24 11:09:46,961 DEBUG: View 0 : 0.53164556962
+2016-08-24 11:09:46,968 DEBUG: View 1 : 0.626582278481
+2016-08-24 11:09:47,006 DEBUG: View 2 : 0.506329113924
+2016-08-24 11:09:47,014 DEBUG: View 3 : 0.462025316456
+2016-08-24 11:09:47,164 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:49,893 DEBUG: Start: Iteration 46
+2016-08-24 11:09:49,910 DEBUG: View 0 : 0.348101265823
+2016-08-24 11:09:49,917 DEBUG: View 1 : 0.607594936709
+2016-08-24 11:09:49,954 DEBUG: View 2 : 0.632911392405
+2016-08-24 11:09:49,962 DEBUG: View 3 : 0.556962025316
+2016-08-24 11:09:50,111 DEBUG: Best view : RANSeq_
+2016-08-24 11:09:52,926 DEBUG: Start: Iteration 47
+2016-08-24 11:09:52,943 DEBUG: View 0 : 0.518987341772
+2016-08-24 11:09:52,951 DEBUG: View 1 : 0.664556962025
+2016-08-24 11:09:52,987 DEBUG: View 2 : 0.417721518987
+2016-08-24 11:09:52,995 DEBUG: View 3 : 0.582278481013
+2016-08-24 11:09:53,147 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:56,001 DEBUG: Start: Iteration 48
+2016-08-24 11:09:56,017 DEBUG: View 0 : 0.487341772152
+2016-08-24 11:09:56,025 DEBUG: View 1 : 0.689873417722
+2016-08-24 11:09:56,063 DEBUG: View 2 : 0.411392405063
+2016-08-24 11:09:56,071 DEBUG: View 3 : 0.449367088608
+2016-08-24 11:09:56,226 DEBUG: Best view : MiRNA__
+2016-08-24 11:09:59,142 DEBUG: Start: Iteration 49
+2016-08-24 11:09:59,158 DEBUG: View 0 : 0.360759493671
+2016-08-24 11:09:59,166 DEBUG: View 1 : 0.392405063291
+2016-08-24 11:09:59,202 DEBUG: View 2 : 0.398734177215
+2016-08-24 11:09:59,210 DEBUG: View 3 : 0.607594936709
+2016-08-24 11:09:59,368 DEBUG: Best view : Clinic_
+2016-08-24 11:10:02,340 DEBUG: Start: Iteration 50
+2016-08-24 11:10:02,356 DEBUG: View 0 : 0.518987341772
+2016-08-24 11:10:02,364 DEBUG: View 1 : 0.5
+2016-08-24 11:10:02,400 DEBUG: View 2 : 0.430379746835
+2016-08-24 11:10:02,408 DEBUG: View 3 : 0.556962025316
+2016-08-24 11:10:02,569 DEBUG: Best view : MiRNA__
+2016-08-24 11:10:05,711 DEBUG: Start: Iteration 51
+2016-08-24 11:10:05,734 DEBUG: View 0 : 0.518987341772
+2016-08-24 11:10:05,747 DEBUG: View 1 : 0.373417721519
+2016-08-24 11:10:05,793 DEBUG: View 2 : 0.518987341772
+2016-08-24 11:10:05,801 DEBUG: View 3 : 0.556962025316
+2016-08-24 11:10:05,984 DEBUG: Best view : Clinic_
+2016-08-24 11:10:09,117 DEBUG: Start: Iteration 52
+2016-08-24 11:10:09,133 DEBUG: View 0 : 0.430379746835
+2016-08-24 11:10:09,141 DEBUG: View 1 : 0.645569620253
+2016-08-24 11:10:09,178 DEBUG: View 2 : 0.556962025316
+2016-08-24 11:10:09,185 DEBUG: View 3 : 0.658227848101
+2016-08-24 11:10:09,351 DEBUG: Best view : Clinic_
+2016-08-24 11:10:12,580 DEBUG: Start: Iteration 53
+2016-08-24 11:10:12,600 DEBUG: View 0 : 0.550632911392
+2016-08-24 11:10:12,609 DEBUG: View 1 : 0.639240506329
+2016-08-24 11:10:12,649 DEBUG: View 2 : 0.525316455696
+2016-08-24 11:10:12,657 DEBUG: View 3 : 0.487341772152
+2016-08-24 11:10:12,836 DEBUG: Best view : MiRNA__
+2016-08-24 11:10:16,117 DEBUG: Start: Iteration 54
+2016-08-24 11:10:16,135 DEBUG: View 0 : 0.481012658228
+2016-08-24 11:10:16,143 DEBUG: View 1 : 0.430379746835
+2016-08-24 11:10:16,180 DEBUG: View 2 : 0.607594936709
+2016-08-24 11:10:16,187 DEBUG: View 3 : 0.582278481013
+2016-08-24 11:10:16,358 DEBUG: Best view : RANSeq_
+2016-08-24 11:10:19,696 DEBUG: Start: Iteration 55
+2016-08-24 11:10:19,712 DEBUG: View 0 : 0.594936708861
+2016-08-24 11:10:19,720 DEBUG: View 1 : 0.607594936709
+2016-08-24 11:10:19,756 DEBUG: View 2 : 0.512658227848
+2016-08-24 11:10:19,764 DEBUG: View 3 : 0.5
+2016-08-24 11:10:19,935 DEBUG: Best view : Methyl_
+2016-08-24 11:10:23,272 DEBUG: Start: Iteration 56
+2016-08-24 11:10:23,289 DEBUG: View 0 : 0.563291139241
+2016-08-24 11:10:23,297 DEBUG: View 1 : 0.405063291139
+2016-08-24 11:10:23,334 DEBUG: View 2 : 0.575949367089
+2016-08-24 11:10:23,341 DEBUG: View 3 : 0.594936708861
+2016-08-24 11:10:23,515 DEBUG: Best view : Clinic_
+2016-08-24 11:10:26,942 DEBUG: Start: Iteration 57
+2016-08-24 11:10:26,959 DEBUG: View 0 : 0.481012658228
+2016-08-24 11:10:26,966 DEBUG: View 1 : 0.5
+2016-08-24 11:10:27,003 DEBUG: View 2 : 0.512658227848
+2016-08-24 11:10:27,011 DEBUG: View 3 : 0.563291139241
+2016-08-24 11:10:27,189 DEBUG: Best view : Clinic_
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-111030-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-111030-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..eef88fa9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-111030-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,32 @@
+2016-08-24 11:10:30,661 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 11:10:30,661 INFO: Info: Labels used: No, Yes
+2016-08-24 11:10:30,662 INFO: Info: Length of dataset:347
+2016-08-24 11:10:30,663 INFO: ### Main Programm for Multiview Classification
+2016-08-24 11:10:30,663 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 11:10:30,664 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 11:10:30,664 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 11:10:30,664 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 11:10:30,665 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 11:10:30,665 INFO: Done: Read Database Files
+2016-08-24 11:10:30,665 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 11:10:30,668 INFO: Done: Determine validation split
+2016-08-24 11:10:30,669 INFO: Start: Determine 2 folds
+2016-08-24 11:10:30,679 INFO: Info: Length of Learning Sets: 122
+2016-08-24 11:10:30,679 INFO: Info: Length of Testing Sets: 122
+2016-08-24 11:10:30,679 INFO: Info: Length of Validation Set: 103
+2016-08-24 11:10:30,679 INFO: Done: Determine folds
+2016-08-24 11:10:30,679 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 11:10:30,679 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 11:10:30,679 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 11:10:38,124 DEBUG: Info: Best Reslut : 0.515158501441
+2016-08-24 11:10:38,124 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:10:38,125 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 11:10:40,053 DEBUG: Info: Best Reslut : 0.546397694524
+2016-08-24 11:10:40,053 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:10:40,053 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 11:10:57,326 DEBUG: Info: Best Reslut : 0.501268011527
+2016-08-24 11:10:57,327 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:10:57,327 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 11:10:59,071 DEBUG: Info: Best Reslut : 0.510086455331
+2016-08-24 11:10:59,072 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:10:59,072 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-111136-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-111136-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..30b21857
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-111136-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,391 @@
+2016-08-24 11:11:36,038 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 11:11:36,039 INFO: Info: Labels used: No, Yes
+2016-08-24 11:11:36,039 INFO: Info: Length of dataset:347
+2016-08-24 11:11:36,040 INFO: ### Main Programm for Multiview Classification
+2016-08-24 11:11:36,041 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 11:11:36,041 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 11:11:36,041 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 11:11:36,042 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 11:11:36,042 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 11:11:36,042 INFO: Done: Read Database Files
+2016-08-24 11:11:36,043 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 11:11:36,046 INFO: Done: Determine validation split
+2016-08-24 11:11:36,046 INFO: Start: Determine 2 folds
+2016-08-24 11:11:36,054 INFO: Info: Length of Learning Sets: 122
+2016-08-24 11:11:36,054 INFO: Info: Length of Testing Sets: 122
+2016-08-24 11:11:36,054 INFO: Info: Length of Validation Set: 103
+2016-08-24 11:11:36,054 INFO: Done: Determine folds
+2016-08-24 11:11:36,054 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 11:11:36,054 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-24 11:11:36,055 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-24 11:11:43,470 DEBUG: Info: Best Reslut : 0.506570605187
+2016-08-24 11:11:43,470 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:11:43,470 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-24 11:11:45,393 DEBUG: Info: Best Reslut : 0.596080691643
+2016-08-24 11:11:45,393 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:11:45,394 DEBUG: Start: Gridsearch for DecisionTree on RANSeq_
+2016-08-24 11:12:02,118 DEBUG: Info: Best Reslut : 0.520922190202
+2016-08-24 11:12:02,119 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:12:02,119 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-24 11:12:03,876 DEBUG: Info: Best Reslut : 0.504553314121
+2016-08-24 11:12:03,877 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:12:03,877 DEBUG: Start: Gridsearch for DecisionTree on MRNASeq
+2016-08-24 11:12:41,748 DEBUG: Info: Best Reslut : 0.515216138329
+2016-08-24 11:12:41,749 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-24 11:12:41,749 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-24 11:12:41,749 INFO: Start: Fold number 1
+2016-08-24 11:12:43,406 DEBUG: Start: Iteration 1
+2016-08-24 11:12:43,425 DEBUG: View 0 : 0.639240506329
+2016-08-24 11:12:43,433 DEBUG: View 1 : 0.620253164557
+2016-08-24 11:12:43,471 DEBUG: View 2 : 0.474683544304
+2016-08-24 11:12:43,479 DEBUG: View 3 : 0.379746835443
+2016-08-24 11:12:43,520 DEBUG: Best view : Methyl_
+2016-08-24 11:12:43,596 DEBUG: Start: Iteration 2
+2016-08-24 11:12:43,614 DEBUG: View 0 : 0.620253164557
+2016-08-24 11:12:43,621 DEBUG: View 1 : 0.398734177215
+2016-08-24 11:12:43,658 DEBUG: View 2 : 0.398734177215
+2016-08-24 11:12:43,666 DEBUG: View 3 : 0.518987341772
+2016-08-24 11:12:43,711 DEBUG: Best view : Methyl_
+2016-08-24 11:12:43,849 DEBUG: Start: Iteration 3
+2016-08-24 11:12:43,866 DEBUG: View 0 : 0.569620253165
+2016-08-24 11:12:43,874 DEBUG: View 1 : 0.436708860759
+2016-08-24 11:12:43,911 DEBUG: View 2 : 0.411392405063
+2016-08-24 11:12:43,918 DEBUG: View 3 : 0.430379746835
+2016-08-24 11:12:43,972 DEBUG: Best view : Methyl_
+2016-08-24 11:12:44,173 DEBUG: Start: Iteration 4
+2016-08-24 11:12:44,189 DEBUG: View 0 : 0.575949367089
+2016-08-24 11:12:44,197 DEBUG: View 1 : 0.512658227848
+2016-08-24 11:12:44,234 DEBUG: View 2 : 0.474683544304
+2016-08-24 11:12:44,242 DEBUG: View 3 : 0.411392405063
+2016-08-24 11:12:44,298 DEBUG: Best view : Methyl_
+2016-08-24 11:12:44,560 DEBUG: Start: Iteration 5
+2016-08-24 11:12:44,576 DEBUG: View 0 : 0.759493670886
+2016-08-24 11:12:44,584 DEBUG: View 1 : 0.569620253165
+2016-08-24 11:12:44,621 DEBUG: View 2 : 0.398734177215
+2016-08-24 11:12:44,628 DEBUG: View 3 : 0.518987341772
+2016-08-24 11:12:44,687 DEBUG: Best view : Methyl_
+2016-08-24 11:12:45,011 DEBUG: Start: Iteration 6
+2016-08-24 11:12:45,027 DEBUG: View 0 : 0.474683544304
+2016-08-24 11:12:45,035 DEBUG: View 1 : 0.46835443038
+2016-08-24 11:12:45,072 DEBUG: View 2 : 0.601265822785
+2016-08-24 11:12:45,080 DEBUG: View 3 : 0.53164556962
+2016-08-24 11:12:45,140 DEBUG: Best view : RANSeq_
+2016-08-24 11:12:45,549 DEBUG: Start: Iteration 7
+2016-08-24 11:12:45,565 DEBUG: View 0 : 0.620253164557
+2016-08-24 11:12:45,574 DEBUG: View 1 : 0.392405063291
+2016-08-24 11:12:45,610 DEBUG: View 2 : 0.53164556962
+2016-08-24 11:12:45,618 DEBUG: View 3 : 0.5
+2016-08-24 11:12:45,681 DEBUG: Best view : Methyl_
+2016-08-24 11:12:46,144 DEBUG: Start: Iteration 8
+2016-08-24 11:12:46,161 DEBUG: View 0 : 0.449367088608
+2016-08-24 11:12:46,169 DEBUG: View 1 : 0.436708860759
+2016-08-24 11:12:46,205 DEBUG: View 2 : 0.525316455696
+2016-08-24 11:12:46,213 DEBUG: View 3 : 0.506329113924
+2016-08-24 11:12:46,277 DEBUG: Best view : RANSeq_
+2016-08-24 11:12:46,809 DEBUG: Start: Iteration 9
+2016-08-24 11:12:46,825 DEBUG: View 0 : 0.487341772152
+2016-08-24 11:12:46,833 DEBUG: View 1 : 0.658227848101
+2016-08-24 11:12:46,870 DEBUG: View 2 : 0.462025316456
+2016-08-24 11:12:46,877 DEBUG: View 3 : 0.481012658228
+2016-08-24 11:12:46,944 DEBUG: Best view : MiRNA__
+2016-08-24 11:12:47,535 DEBUG: Start: Iteration 10
+2016-08-24 11:12:47,552 DEBUG: View 0 : 0.474683544304
+2016-08-24 11:12:47,560 DEBUG: View 1 : 0.588607594937
+2016-08-24 11:12:47,596 DEBUG: View 2 : 0.411392405063
+2016-08-24 11:12:47,604 DEBUG: View 3 : 0.525316455696
+2016-08-24 11:12:47,673 DEBUG: Best view : MiRNA__
+2016-08-24 11:12:48,319 DEBUG: Start: Iteration 11
+2016-08-24 11:12:48,336 DEBUG: View 0 : 0.46835443038
+2016-08-24 11:12:48,344 DEBUG: View 1 : 0.639240506329
+2016-08-24 11:12:48,380 DEBUG: View 2 : 0.537974683544
+2016-08-24 11:12:48,388 DEBUG: View 3 : 0.53164556962
+2016-08-24 11:12:48,460 DEBUG: Best view : MiRNA__
+2016-08-24 11:12:49,176 DEBUG: Start: Iteration 12
+2016-08-24 11:12:49,193 DEBUG: View 0 : 0.411392405063
+2016-08-24 11:12:49,201 DEBUG: View 1 : 0.626582278481
+2016-08-24 11:12:49,239 DEBUG: View 2 : 0.379746835443
+2016-08-24 11:12:49,247 DEBUG: View 3 : 0.607594936709
+2016-08-24 11:12:49,323 DEBUG: Best view : MiRNA__
+2016-08-24 11:12:50,095 DEBUG: Start: Iteration 13
+2016-08-24 11:12:50,112 DEBUG: View 0 : 0.46835443038
+2016-08-24 11:12:50,120 DEBUG: View 1 : 0.405063291139
+2016-08-24 11:12:50,157 DEBUG: View 2 : 0.474683544304
+2016-08-24 11:12:50,165 DEBUG: View 3 : 0.582278481013
+2016-08-24 11:12:50,241 DEBUG: Best view : Clinic_
+2016-08-24 11:12:51,101 DEBUG: Start: Iteration 14
+2016-08-24 11:12:51,117 DEBUG: View 0 : 0.569620253165
+2016-08-24 11:12:51,125 DEBUG: View 1 : 0.594936708861
+2016-08-24 11:12:51,162 DEBUG: View 2 : 0.424050632911
+2016-08-24 11:12:51,170 DEBUG: View 3 : 0.392405063291
+2016-08-24 11:12:51,248 DEBUG: Best view : MiRNA__
+2016-08-24 11:12:52,146 DEBUG: Start: Iteration 15
+2016-08-24 11:12:52,163 DEBUG: View 0 : 0.594936708861
+2016-08-24 11:12:52,171 DEBUG: View 1 : 0.651898734177
+2016-08-24 11:12:52,208 DEBUG: View 2 : 0.411392405063
+2016-08-24 11:12:52,215 DEBUG: View 3 : 0.5
+2016-08-24 11:12:52,297 DEBUG: Best view : MiRNA__
+2016-08-24 11:12:53,236 DEBUG: Start: Iteration 16
+2016-08-24 11:12:53,252 DEBUG: View 0 : 0.386075949367
+2016-08-24 11:12:53,260 DEBUG: View 1 : 0.518987341772
+2016-08-24 11:12:53,297 DEBUG: View 2 : 0.588607594937
+2016-08-24 11:12:53,305 DEBUG: View 3 : 0.53164556962
+2016-08-24 11:12:53,388 DEBUG: Best view : RANSeq_
+2016-08-24 11:12:54,407 DEBUG: Start: Iteration 17
+2016-08-24 11:12:54,424 DEBUG: View 0 : 0.626582278481
+2016-08-24 11:12:54,432 DEBUG: View 1 : 0.550632911392
+2016-08-24 11:12:54,469 DEBUG: View 2 : 0.5
+2016-08-24 11:12:54,476 DEBUG: View 3 : 0.373417721519
+2016-08-24 11:12:54,562 DEBUG: Best view : Methyl_
+2016-08-24 11:12:55,640 DEBUG: Start: Iteration 18
+2016-08-24 11:12:55,657 DEBUG: View 0 : 0.424050632911
+2016-08-24 11:12:55,665 DEBUG: View 1 : 0.398734177215
+2016-08-24 11:12:55,701 DEBUG: View 2 : 0.658227848101
+2016-08-24 11:12:55,709 DEBUG: View 3 : 0.525316455696
+2016-08-24 11:12:55,796 DEBUG: Best view : RANSeq_
+2016-08-24 11:12:56,943 DEBUG: Start: Iteration 19
+2016-08-24 11:12:56,960 DEBUG: View 0 : 0.556962025316
+2016-08-24 11:12:56,968 DEBUG: View 1 : 0.392405063291
+2016-08-24 11:12:57,005 DEBUG: View 2 : 0.613924050633
+2016-08-24 11:12:57,013 DEBUG: View 3 : 0.677215189873
+2016-08-24 11:12:57,105 DEBUG: Best view : Clinic_
+2016-08-24 11:12:58,305 DEBUG: Start: Iteration 20
+2016-08-24 11:12:58,322 DEBUG: View 0 : 0.417721518987
+2016-08-24 11:12:58,330 DEBUG: View 1 : 0.436708860759
+2016-08-24 11:12:58,367 DEBUG: View 2 : 0.411392405063
+2016-08-24 11:12:58,374 DEBUG: View 3 : 0.443037974684
+2016-08-24 11:12:58,374 WARNING: WARNING: All bad for iteration 19
+2016-08-24 11:12:58,467 DEBUG: Best view : Clinic_
+2016-08-24 11:12:59,738 DEBUG: Start: Iteration 21
+2016-08-24 11:12:59,755 DEBUG: View 0 : 0.430379746835
+2016-08-24 11:12:59,763 DEBUG: View 1 : 0.506329113924
+2016-08-24 11:12:59,800 DEBUG: View 2 : 0.493670886076
+2016-08-24 11:12:59,807 DEBUG: View 3 : 0.632911392405
+2016-08-24 11:12:59,904 DEBUG: Best view : Clinic_
+2016-08-24 11:13:01,234 DEBUG: Start: Iteration 22
+2016-08-24 11:13:01,251 DEBUG: View 0 : 0.462025316456
+2016-08-24 11:13:01,259 DEBUG: View 1 : 0.506329113924
+2016-08-24 11:13:01,296 DEBUG: View 2 : 0.620253164557
+2016-08-24 11:13:01,304 DEBUG: View 3 : 0.575949367089
+2016-08-24 11:13:01,401 DEBUG: Best view : RANSeq_
+2016-08-24 11:13:02,803 DEBUG: Start: Iteration 23
+2016-08-24 11:13:02,820 DEBUG: View 0 : 0.607594936709
+2016-08-24 11:13:02,828 DEBUG: View 1 : 0.518987341772
+2016-08-24 11:13:02,866 DEBUG: View 2 : 0.436708860759
+2016-08-24 11:13:02,874 DEBUG: View 3 : 0.436708860759
+2016-08-24 11:13:02,973 DEBUG: Best view : Methyl_
+2016-08-24 11:13:04,490 DEBUG: Start: Iteration 24
+2016-08-24 11:13:04,508 DEBUG: View 0 : 0.316455696203
+2016-08-24 11:13:04,515 DEBUG: View 1 : 0.373417721519
+2016-08-24 11:13:04,552 DEBUG: View 2 : 0.588607594937
+2016-08-24 11:13:04,560 DEBUG: View 3 : 0.487341772152
+2016-08-24 11:13:04,661 DEBUG: Best view : RANSeq_
+2016-08-24 11:13:06,180 DEBUG: Start: Iteration 25
+2016-08-24 11:13:06,198 DEBUG: View 0 : 0.569620253165
+2016-08-24 11:13:06,206 DEBUG: View 1 : 0.436708860759
+2016-08-24 11:13:06,243 DEBUG: View 2 : 0.556962025316
+2016-08-24 11:13:06,250 DEBUG: View 3 : 0.392405063291
+2016-08-24 11:13:06,355 DEBUG: Best view : Methyl_
+2016-08-24 11:13:07,932 DEBUG: Start: Iteration 26
+2016-08-24 11:13:07,948 DEBUG: View 0 : 0.512658227848
+2016-08-24 11:13:07,956 DEBUG: View 1 : 0.689873417722
+2016-08-24 11:13:07,993 DEBUG: View 2 : 0.537974683544
+2016-08-24 11:13:08,001 DEBUG: View 3 : 0.417721518987
+2016-08-24 11:13:08,107 DEBUG: Best view : MiRNA__
+2016-08-24 11:13:09,754 DEBUG: Start: Iteration 27
+2016-08-24 11:13:09,770 DEBUG: View 0 : 0.462025316456
+2016-08-24 11:13:09,778 DEBUG: View 1 : 0.588607594937
+2016-08-24 11:13:09,815 DEBUG: View 2 : 0.607594936709
+2016-08-24 11:13:09,823 DEBUG: View 3 : 0.46835443038
+2016-08-24 11:13:09,931 DEBUG: Best view : MiRNA__
+2016-08-24 11:13:11,652 DEBUG: Start: Iteration 28
+2016-08-24 11:13:11,668 DEBUG: View 0 : 0.588607594937
+2016-08-24 11:13:11,676 DEBUG: View 1 : 0.569620253165
+2016-08-24 11:13:11,714 DEBUG: View 2 : 0.582278481013
+2016-08-24 11:13:11,722 DEBUG: View 3 : 0.537974683544
+2016-08-24 11:13:11,832 DEBUG: Best view : MiRNA__
+2016-08-24 11:13:13,601 DEBUG: Start: Iteration 29
+2016-08-24 11:13:13,617 DEBUG: View 0 : 0.645569620253
+2016-08-24 11:13:13,625 DEBUG: View 1 : 0.5
+2016-08-24 11:13:13,663 DEBUG: View 2 : 0.556962025316
+2016-08-24 11:13:13,670 DEBUG: View 3 : 0.645569620253
+2016-08-24 11:13:13,786 DEBUG: Best view : Methyl_
+2016-08-24 11:13:15,623 DEBUG: Start: Iteration 30
+2016-08-24 11:13:15,640 DEBUG: View 0 : 0.518987341772
+2016-08-24 11:13:15,648 DEBUG: View 1 : 0.46835443038
+2016-08-24 11:13:15,685 DEBUG: View 2 : 0.506329113924
+2016-08-24 11:13:15,693 DEBUG: View 3 : 0.481012658228
+2016-08-24 11:13:15,810 DEBUG: Best view : Methyl_
+2016-08-24 11:13:17,695 DEBUG: Start: Iteration 31
+2016-08-24 11:13:17,711 DEBUG: View 0 : 0.645569620253
+2016-08-24 11:13:17,719 DEBUG: View 1 : 0.544303797468
+2016-08-24 11:13:17,755 DEBUG: View 2 : 0.493670886076
+2016-08-24 11:13:17,763 DEBUG: View 3 : 0.386075949367
+2016-08-24 11:13:17,880 DEBUG: Best view : Methyl_
+2016-08-24 11:13:19,816 DEBUG: Start: Iteration 32
+2016-08-24 11:13:19,832 DEBUG: View 0 : 0.436708860759
+2016-08-24 11:13:19,840 DEBUG: View 1 : 0.430379746835
+2016-08-24 11:13:19,877 DEBUG: View 2 : 0.569620253165
+2016-08-24 11:13:19,885 DEBUG: View 3 : 0.563291139241
+2016-08-24 11:13:20,004 DEBUG: Best view : Clinic_
+2016-08-24 11:13:21,998 DEBUG: Start: Iteration 33
+2016-08-24 11:13:22,015 DEBUG: View 0 : 0.487341772152
+2016-08-24 11:13:22,023 DEBUG: View 1 : 0.417721518987
+2016-08-24 11:13:22,060 DEBUG: View 2 : 0.506329113924
+2016-08-24 11:13:22,067 DEBUG: View 3 : 0.556962025316
+2016-08-24 11:13:22,189 DEBUG: Best view : Clinic_
+2016-08-24 11:13:24,238 DEBUG: Start: Iteration 34
+2016-08-24 11:13:24,255 DEBUG: View 0 : 0.582278481013
+2016-08-24 11:13:24,263 DEBUG: View 1 : 0.348101265823
+2016-08-24 11:13:24,299 DEBUG: View 2 : 0.462025316456
+2016-08-24 11:13:24,307 DEBUG: View 3 : 0.525316455696
+2016-08-24 11:13:24,431 DEBUG: Best view : Methyl_
+2016-08-24 11:13:26,552 DEBUG: Start: Iteration 35
+2016-08-24 11:13:26,568 DEBUG: View 0 : 0.367088607595
+2016-08-24 11:13:26,576 DEBUG: View 1 : 0.588607594937
+2016-08-24 11:13:26,613 DEBUG: View 2 : 0.398734177215
+2016-08-24 11:13:26,621 DEBUG: View 3 : 0.563291139241
+2016-08-24 11:13:26,747 DEBUG: Best view : MiRNA__
+2016-08-24 11:13:28,935 DEBUG: Start: Iteration 36
+2016-08-24 11:13:28,952 DEBUG: View 0 : 0.575949367089
+2016-08-24 11:13:28,960 DEBUG: View 1 : 0.518987341772
+2016-08-24 11:13:28,997 DEBUG: View 2 : 0.537974683544
+2016-08-24 11:13:29,004 DEBUG: View 3 : 0.46835443038
+2016-08-24 11:13:29,132 DEBUG: Best view : Methyl_
+2016-08-24 11:13:31,405 DEBUG: Start: Iteration 37
+2016-08-24 11:13:31,423 DEBUG: View 0 : 0.569620253165
+2016-08-24 11:13:31,431 DEBUG: View 1 : 0.556962025316
+2016-08-24 11:13:31,468 DEBUG: View 2 : 0.424050632911
+2016-08-24 11:13:31,476 DEBUG: View 3 : 0.563291139241
+2016-08-24 11:13:31,606 DEBUG: Best view : Methyl_
+2016-08-24 11:13:33,902 DEBUG: Start: Iteration 38
+2016-08-24 11:13:33,919 DEBUG: View 0 : 0.601265822785
+2016-08-24 11:13:33,927 DEBUG: View 1 : 0.367088607595
+2016-08-24 11:13:33,963 DEBUG: View 2 : 0.392405063291
+2016-08-24 11:13:33,971 DEBUG: View 3 : 0.639240506329
+2016-08-24 11:13:34,104 DEBUG: Best view : Clinic_
+2016-08-24 11:13:36,607 DEBUG: Start: Iteration 39
+2016-08-24 11:13:36,624 DEBUG: View 0 : 0.360759493671
+2016-08-24 11:13:36,631 DEBUG: View 1 : 0.405063291139
+2016-08-24 11:13:36,668 DEBUG: View 2 : 0.411392405063
+2016-08-24 11:13:36,676 DEBUG: View 3 : 0.575949367089
+2016-08-24 11:13:36,811 DEBUG: Best view : Clinic_
+2016-08-24 11:13:39,566 DEBUG: Start: Iteration 40
+2016-08-24 11:13:39,594 DEBUG: View 0 : 0.550632911392
+2016-08-24 11:13:39,608 DEBUG: View 1 : 0.449367088608
+2016-08-24 11:13:39,662 DEBUG: View 2 : 0.474683544304
+2016-08-24 11:13:39,675 DEBUG: View 3 : 0.493670886076
+2016-08-24 11:13:39,892 DEBUG: Best view : Methyl_
+2016-08-24 11:13:42,508 DEBUG: Start: Iteration 41
+2016-08-24 11:13:42,525 DEBUG: View 0 : 0.46835443038
+2016-08-24 11:13:42,533 DEBUG: View 1 : 0.506329113924
+2016-08-24 11:13:42,570 DEBUG: View 2 : 0.506329113924
+2016-08-24 11:13:42,578 DEBUG: View 3 : 0.53164556962
+2016-08-24 11:13:42,720 DEBUG: Best view : MiRNA__
+2016-08-24 11:13:45,271 DEBUG: Start: Iteration 42
+2016-08-24 11:13:45,289 DEBUG: View 0 : 0.53164556962
+2016-08-24 11:13:45,297 DEBUG: View 1 : 0.670886075949
+2016-08-24 11:13:45,334 DEBUG: View 2 : 0.537974683544
+2016-08-24 11:13:45,342 DEBUG: View 3 : 0.436708860759
+2016-08-24 11:13:45,484 DEBUG: Best view : MiRNA__
+2016-08-24 11:13:48,085 DEBUG: Start: Iteration 43
+2016-08-24 11:13:48,102 DEBUG: View 0 : 0.443037974684
+2016-08-24 11:13:48,110 DEBUG: View 1 : 0.607594936709
+2016-08-24 11:13:48,147 DEBUG: View 2 : 0.487341772152
+2016-08-24 11:13:48,155 DEBUG: View 3 : 0.424050632911
+2016-08-24 11:13:48,299 DEBUG: Best view : MiRNA__
+2016-08-24 11:13:50,954 DEBUG: Start: Iteration 44
+2016-08-24 11:13:50,970 DEBUG: View 0 : 0.443037974684
+2016-08-24 11:13:50,978 DEBUG: View 1 : 0.537974683544
+2016-08-24 11:13:51,016 DEBUG: View 2 : 0.481012658228
+2016-08-24 11:13:51,024 DEBUG: View 3 : 0.537974683544
+2016-08-24 11:13:51,170 DEBUG: Best view : MiRNA__
+2016-08-24 11:13:53,901 DEBUG: Start: Iteration 45
+2016-08-24 11:13:53,918 DEBUG: View 0 : 0.474683544304
+2016-08-24 11:13:53,926 DEBUG: View 1 : 0.664556962025
+2016-08-24 11:13:53,963 DEBUG: View 2 : 0.443037974684
+2016-08-24 11:13:53,971 DEBUG: View 3 : 0.632911392405
+2016-08-24 11:13:54,120 DEBUG: Best view : MiRNA__
+2016-08-24 11:13:57,012 DEBUG: Start: Iteration 46
+2016-08-24 11:13:57,030 DEBUG: View 0 : 0.411392405063
+2016-08-24 11:13:57,038 DEBUG: View 1 : 0.727848101266
+2016-08-24 11:13:57,077 DEBUG: View 2 : 0.436708860759
+2016-08-24 11:13:57,085 DEBUG: View 3 : 0.474683544304
+2016-08-24 11:13:57,239 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:00,114 DEBUG: Start: Iteration 47
+2016-08-24 11:14:00,131 DEBUG: View 0 : 0.487341772152
+2016-08-24 11:14:00,138 DEBUG: View 1 : 0.607594936709
+2016-08-24 11:14:00,175 DEBUG: View 2 : 0.449367088608
+2016-08-24 11:14:00,183 DEBUG: View 3 : 0.525316455696
+2016-08-24 11:14:00,338 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:03,280 DEBUG: Start: Iteration 48
+2016-08-24 11:14:03,297 DEBUG: View 0 : 0.544303797468
+2016-08-24 11:14:03,304 DEBUG: View 1 : 0.613924050633
+2016-08-24 11:14:03,341 DEBUG: View 2 : 0.518987341772
+2016-08-24 11:14:03,349 DEBUG: View 3 : 0.436708860759
+2016-08-24 11:14:03,509 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:06,498 DEBUG: Start: Iteration 49
+2016-08-24 11:14:06,514 DEBUG: View 0 : 0.392405063291
+2016-08-24 11:14:06,522 DEBUG: View 1 : 0.721518987342
+2016-08-24 11:14:06,559 DEBUG: View 2 : 0.405063291139
+2016-08-24 11:14:06,567 DEBUG: View 3 : 0.518987341772
+2016-08-24 11:14:06,728 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:09,769 DEBUG: Start: Iteration 50
+2016-08-24 11:14:09,789 DEBUG: View 0 : 0.462025316456
+2016-08-24 11:14:09,797 DEBUG: View 1 : 0.53164556962
+2016-08-24 11:14:09,839 DEBUG: View 2 : 0.53164556962
+2016-08-24 11:14:09,848 DEBUG: View 3 : 0.525316455696
+2016-08-24 11:14:10,022 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:13,136 DEBUG: Start: Iteration 51
+2016-08-24 11:14:13,152 DEBUG: View 0 : 0.582278481013
+2016-08-24 11:14:13,160 DEBUG: View 1 : 0.46835443038
+2016-08-24 11:14:13,197 DEBUG: View 2 : 0.563291139241
+2016-08-24 11:14:13,205 DEBUG: View 3 : 0.518987341772
+2016-08-24 11:14:13,366 DEBUG: Best view : Methyl_
+2016-08-24 11:14:16,488 DEBUG: Start: Iteration 52
+2016-08-24 11:14:16,505 DEBUG: View 0 : 0.373417721519
+2016-08-24 11:14:16,513 DEBUG: View 1 : 0.518987341772
+2016-08-24 11:14:16,550 DEBUG: View 2 : 0.386075949367
+2016-08-24 11:14:16,558 DEBUG: View 3 : 0.487341772152
+2016-08-24 11:14:16,722 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:19,893 DEBUG: Start: Iteration 53
+2016-08-24 11:14:19,910 DEBUG: View 0 : 0.563291139241
+2016-08-24 11:14:19,917 DEBUG: View 1 : 0.53164556962
+2016-08-24 11:14:19,954 DEBUG: View 2 : 0.537974683544
+2016-08-24 11:14:19,961 DEBUG: View 3 : 0.550632911392
+2016-08-24 11:14:20,128 DEBUG: Best view : Methyl_
+2016-08-24 11:14:23,363 DEBUG: Start: Iteration 54
+2016-08-24 11:14:23,380 DEBUG: View 0 : 0.613924050633
+2016-08-24 11:14:23,388 DEBUG: View 1 : 0.455696202532
+2016-08-24 11:14:23,425 DEBUG: View 2 : 0.601265822785
+2016-08-24 11:14:23,433 DEBUG: View 3 : 0.436708860759
+2016-08-24 11:14:23,601 DEBUG: Best view : Methyl_
+2016-08-24 11:14:26,896 DEBUG: Start: Iteration 55
+2016-08-24 11:14:26,913 DEBUG: View 0 : 0.493670886076
+2016-08-24 11:14:26,921 DEBUG: View 1 : 0.626582278481
+2016-08-24 11:14:26,958 DEBUG: View 2 : 0.455696202532
+2016-08-24 11:14:26,965 DEBUG: View 3 : 0.398734177215
+2016-08-24 11:14:27,137 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:30,496 DEBUG: Start: Iteration 56
+2016-08-24 11:14:30,513 DEBUG: View 0 : 0.550632911392
+2016-08-24 11:14:30,521 DEBUG: View 1 : 0.664556962025
+2016-08-24 11:14:30,558 DEBUG: View 2 : 0.588607594937
+2016-08-24 11:14:30,565 DEBUG: View 3 : 0.645569620253
+2016-08-24 11:14:30,739 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:34,390 DEBUG: Start: Iteration 57
+2016-08-24 11:14:34,414 DEBUG: View 0 : 0.506329113924
+2016-08-24 11:14:34,422 DEBUG: View 1 : 0.512658227848
+2016-08-24 11:14:34,460 DEBUG: View 2 : 0.46835443038
+2016-08-24 11:14:34,468 DEBUG: View 3 : 0.569620253165
+2016-08-24 11:14:34,648 DEBUG: Best view : Clinic_
+2016-08-24 11:14:38,374 DEBUG: Start: Iteration 58
+2016-08-24 11:14:38,391 DEBUG: View 0 : 0.639240506329
+2016-08-24 11:14:38,399 DEBUG: View 1 : 0.651898734177
+2016-08-24 11:14:38,436 DEBUG: View 2 : 0.398734177215
+2016-08-24 11:14:38,444 DEBUG: View 3 : 0.556962025316
+2016-08-24 11:14:38,627 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:42,400 DEBUG: Start: Iteration 59
+2016-08-24 11:14:42,419 DEBUG: View 0 : 0.620253164557
+2016-08-24 11:14:42,428 DEBUG: View 1 : 0.677215189873
+2016-08-24 11:14:42,471 DEBUG: View 2 : 0.411392405063
+2016-08-24 11:14:42,480 DEBUG: View 3 : 0.512658227848
+2016-08-24 11:14:42,689 DEBUG: Best view : MiRNA__
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-111445-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-111445-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..52ce30c5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-111445-CMultiV-Mumbo-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,15314 @@
+2016-08-24 11:14:45,495 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
+2016-08-24 11:14:45,495 INFO: Info: Labels used: No, Yes
+2016-08-24 11:14:45,496 INFO: Info: Length of dataset:347
+2016-08-24 11:14:45,497 INFO: ### Main Programm for Multiview Classification
+2016-08-24 11:14:45,498 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Mumbo ; Cores : 1
+2016-08-24 11:14:45,498 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-24 11:14:45,499 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-24 11:14:45,499 INFO: Info: Shape of RANSeq_ :(347, 73599)
+2016-08-24 11:14:45,500 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-24 11:14:45,500 INFO: Done: Read Database Files
+2016-08-24 11:14:45,500 INFO: Start: Determine validation split for ratio 0.7
+2016-08-24 11:14:45,506 INFO: Done: Determine validation split
+2016-08-24 11:14:45,506 INFO: Start: Determine 2 folds
+2016-08-24 11:14:45,517 INFO: Info: Length of Learning Sets: 122
+2016-08-24 11:14:45,517 INFO: Info: Length of Testing Sets: 122
+2016-08-24 11:14:45,517 INFO: Info: Length of Validation Set: 103
+2016-08-24 11:14:45,517 INFO: Done: Determine folds
+2016-08-24 11:14:45,518 INFO: Start: Learning with Mumbo and 2 folds
+2016-08-24 11:14:45,518 INFO: Start: Fold number 1
+2016-08-24 11:14:47,122 DEBUG: Start: Iteration 1
+2016-08-24 11:14:47,138 DEBUG: View 0 : 0.372670807453
+2016-08-24 11:14:47,146 DEBUG: View 1 : 0.627329192547
+2016-08-24 11:14:47,177 DEBUG: View 2 : 0.372670807453
+2016-08-24 11:14:47,185 DEBUG: View 3 : 0.627329192547
+2016-08-24 11:14:47,227 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:47,301 DEBUG: Start: Iteration 2
+2016-08-24 11:14:47,320 DEBUG: View 0 : 0.608695652174
+2016-08-24 11:14:47,328 DEBUG: View 1 : 0.726708074534
+2016-08-24 11:14:47,423 DEBUG: View 2 : 0.627329192547
+2016-08-24 11:14:47,432 DEBUG: View 3 : 0.683229813665
+2016-08-24 11:14:47,478 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:47,611 DEBUG: Start: Iteration 3
+2016-08-24 11:14:47,629 DEBUG: View 0 : 0.552795031056
+2016-08-24 11:14:47,637 DEBUG: View 1 : 0.645962732919
+2016-08-24 11:14:47,759 DEBUG: View 2 : 0.534161490683
+2016-08-24 11:14:47,771 DEBUG: View 3 : 0.633540372671
+2016-08-24 11:14:47,849 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:48,046 DEBUG: Start: Iteration 4
+2016-08-24 11:14:48,064 DEBUG: View 0 : 0.577639751553
+2016-08-24 11:14:48,072 DEBUG: View 1 : 0.391304347826
+2016-08-24 11:14:48,164 DEBUG: View 2 : 0.527950310559
+2016-08-24 11:14:48,172 DEBUG: View 3 : 0.55900621118
+2016-08-24 11:14:48,228 DEBUG: Best view : RANSeq_
+2016-08-24 11:14:48,495 DEBUG: Start: Iteration 5
+2016-08-24 11:14:48,513 DEBUG: View 0 : 0.453416149068
+2016-08-24 11:14:48,521 DEBUG: View 1 : 0.689440993789
+2016-08-24 11:14:48,604 DEBUG: View 2 : 0.484472049689
+2016-08-24 11:14:48,612 DEBUG: View 3 : 0.577639751553
+2016-08-24 11:14:48,670 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:48,996 DEBUG: Start: Iteration 6
+2016-08-24 11:14:49,014 DEBUG: View 0 : 0.596273291925
+2016-08-24 11:14:49,021 DEBUG: View 1 : 0.645962732919
+2016-08-24 11:14:49,113 DEBUG: View 2 : 0.577639751553
+2016-08-24 11:14:49,121 DEBUG: View 3 : 0.608695652174
+2016-08-24 11:14:49,182 DEBUG: Best view : Clinic_
+2016-08-24 11:14:49,566 DEBUG: Start: Iteration 7
+2016-08-24 11:14:49,584 DEBUG: View 0 : 0.509316770186
+2016-08-24 11:14:49,592 DEBUG: View 1 : 0.72049689441
+2016-08-24 11:14:49,679 DEBUG: View 2 : 0.577639751553
+2016-08-24 11:14:49,687 DEBUG: View 3 : 0.546583850932
+2016-08-24 11:14:49,750 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:50,217 DEBUG: Start: Iteration 8
+2016-08-24 11:14:50,235 DEBUG: View 0 : 0.540372670807
+2016-08-24 11:14:50,243 DEBUG: View 1 : 0.639751552795
+2016-08-24 11:14:50,331 DEBUG: View 2 : 0.577639751553
+2016-08-24 11:14:50,339 DEBUG: View 3 : 0.621118012422
+2016-08-24 11:14:50,404 DEBUG: Best view : Clinic_
+2016-08-24 11:14:50,908 DEBUG: Start: Iteration 9
+2016-08-24 11:14:50,926 DEBUG: View 0 : 0.614906832298
+2016-08-24 11:14:50,934 DEBUG: View 1 : 0.571428571429
+2016-08-24 11:14:51,028 DEBUG: View 2 : 0.55900621118
+2016-08-24 11:14:51,036 DEBUG: View 3 : 0.565217391304
+2016-08-24 11:14:51,104 DEBUG: Best view : Clinic_
+2016-08-24 11:14:51,669 DEBUG: Start: Iteration 10
+2016-08-24 11:14:51,687 DEBUG: View 0 : 0.608695652174
+2016-08-24 11:14:51,695 DEBUG: View 1 : 0.689440993789
+2016-08-24 11:14:51,787 DEBUG: View 2 : 0.540372670807
+2016-08-24 11:14:51,795 DEBUG: View 3 : 0.639751552795
+2016-08-24 11:14:51,865 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:52,493 DEBUG: Start: Iteration 11
+2016-08-24 11:14:52,511 DEBUG: View 0 : 0.596273291925
+2016-08-24 11:14:52,519 DEBUG: View 1 : 0.465838509317
+2016-08-24 11:14:52,606 DEBUG: View 2 : 0.565217391304
+2016-08-24 11:14:52,614 DEBUG: View 3 : 0.490683229814
+2016-08-24 11:14:52,687 DEBUG: Best view : RANSeq_
+2016-08-24 11:14:53,382 DEBUG: Start: Iteration 12
+2016-08-24 11:14:53,401 DEBUG: View 0 : 0.378881987578
+2016-08-24 11:14:53,409 DEBUG: View 1 : 0.614906832298
+2016-08-24 11:14:53,498 DEBUG: View 2 : 0.55900621118
+2016-08-24 11:14:53,506 DEBUG: View 3 : 0.633540372671
+2016-08-24 11:14:53,580 DEBUG: Best view : Clinic_
+2016-08-24 11:14:54,336 DEBUG: Start: Iteration 13
+2016-08-24 11:14:54,354 DEBUG: View 0 : 0.689440993789
+2016-08-24 11:14:54,362 DEBUG: View 1 : 0.577639751553
+2016-08-24 11:14:54,450 DEBUG: View 2 : 0.596273291925
+2016-08-24 11:14:54,458 DEBUG: View 3 : 0.683229813665
+2016-08-24 11:14:54,535 DEBUG: Best view : Clinic_
+2016-08-24 11:14:55,380 DEBUG: Start: Iteration 14
+2016-08-24 11:14:55,399 DEBUG: View 0 : 0.459627329193
+2016-08-24 11:14:55,408 DEBUG: View 1 : 0.546583850932
+2016-08-24 11:14:55,511 DEBUG: View 2 : 0.608695652174
+2016-08-24 11:14:55,520 DEBUG: View 3 : 0.621118012422
+2016-08-24 11:14:55,597 DEBUG: Best view : Clinic_
+2016-08-24 11:14:56,463 DEBUG: Start: Iteration 15
+2016-08-24 11:14:56,480 DEBUG: View 0 : 0.515527950311
+2016-08-24 11:14:56,488 DEBUG: View 1 : 0.633540372671
+2016-08-24 11:14:56,580 DEBUG: View 2 : 0.527950310559
+2016-08-24 11:14:56,588 DEBUG: View 3 : 0.484472049689
+2016-08-24 11:14:56,668 DEBUG: Best view : MiRNA__
+2016-08-24 11:14:57,608 DEBUG: Start: Iteration 16
+2016-08-24 11:14:57,628 DEBUG: View 0 : 0.484472049689
+2016-08-24 11:14:57,637 DEBUG: View 1 : 0.596273291925
+2016-08-24 11:14:57,734 DEBUG: View 2 : 0.608695652174
+2016-08-24 11:14:57,742 DEBUG: View 3 : 0.627329192547
+2016-08-24 11:14:57,824 DEBUG: Best view : Clinic_
+2016-08-24 11:14:58,815 DEBUG: Start: Iteration 17
+2016-08-24 11:14:58,833 DEBUG: View 0 : 0.459627329193
+2016-08-24 11:14:58,841 DEBUG: View 1 : 0.67701863354
+2016-08-24 11:14:58,934 DEBUG: View 2 : 0.621118012422
+2016-08-24 11:14:58,942 DEBUG: View 3 : 0.590062111801
+2016-08-24 11:14:59,028 DEBUG: Best view : MiRNA__
+2016-08-24 11:15:00,093 DEBUG: Start: Iteration 18
+2016-08-24 11:15:00,112 DEBUG: View 0 : 0.67701863354
+2016-08-24 11:15:00,120 DEBUG: View 1 : 0.608695652174
+2016-08-24 11:15:00,210 DEBUG: View 2 : 0.515527950311
+2016-08-24 11:15:00,217 DEBUG: View 3 : 0.708074534161
+2016-08-24 11:15:00,304 DEBUG: Best view : Clinic_
+2016-08-24 11:15:01,416 DEBUG: Start: Iteration 19
+2016-08-24 11:15:01,434 DEBUG: View 0 : 0.546583850932
+2016-08-24 11:15:01,442 DEBUG: View 1 : 0.701863354037
+2016-08-24 11:15:01,534 DEBUG: View 2 : 0.590062111801
+2016-08-24 11:15:01,542 DEBUG: View 3 : 0.639751552795
+2016-08-24 11:15:01,631 DEBUG: Best view : MiRNA__
+2016-08-24 11:15:02,804 DEBUG: Start: Iteration 20
+2016-08-24 11:15:02,826 DEBUG: View 0 : 0.484472049689
+2016-08-24 11:15:02,834 DEBUG: View 1 : 0.490683229814
+2016-08-24 11:15:02,941 DEBUG: View 2 : 0.577639751553
+2016-08-24 11:15:02,949 DEBUG: View 3 : 0.608695652174
+2016-08-24 11:15:03,045 DEBUG: Best view : Clinic_
+2016-08-24 11:15:04,267 DEBUG: Start: Iteration 21
+2016-08-24 11:15:04,286 DEBUG: View 0 : 0.621118012422
+2016-08-24 11:15:04,294 DEBUG: View 1 : 0.521739130435
+2016-08-24 11:15:04,382 DEBUG: View 2 : 0.60248447205
+2016-08-24 11:15:04,389 DEBUG: View 3 : 0.633540372671
+2016-08-24 11:15:04,483 DEBUG: Best view : Clinic_
+2016-08-24 11:15:05,775 DEBUG: Start: Iteration 22
+2016-08-24 11:15:05,793 DEBUG: View 0 : 0.540372670807
+2016-08-24 11:15:05,801 DEBUG: View 1 : 0.658385093168
+2016-08-24 11:15:05,889 DEBUG: View 2 : 0.608695652174
+2016-08-24 11:15:05,898 DEBUG: View 3 : 0.527950310559
+2016-08-24 11:15:05,993 DEBUG: Best view : MiRNA__
+2016-08-24 11:15:07,339 DEBUG: Start: Iteration 23
+2016-08-24 11:15:07,357 DEBUG: View 0 : 0.571428571429
+2016-08-24 11:15:07,364 DEBUG: View 1 : 0.534161490683
+2016-08-24 11:15:07,454 DEBUG: View 2 : 0.627329192547
+2016-08-24 11:15:07,462 DEBUG: View 3 : 0.72049689441
+2016-08-24 11:15:07,560 DEBUG: Best view : Clinic_
+2016-08-24 11:15:08,965 DEBUG: Start: Iteration 24
+2016-08-24 11:15:08,983 DEBUG: View 0 : 0.478260869565
+2016-08-24 11:15:08,990 DEBUG: View 1 : 0.39751552795
+2016-08-24 11:15:09,083 DEBUG: View 2 : 0.577639751553
+2016-08-24 11:15:09,091 DEBUG: View 3 : 0.614906832298
+2016-08-24 11:15:09,192 DEBUG: Best view : Clinic_
+2016-08-24 11:15:10,667 DEBUG: Start: Iteration 25
+2016-08-24 11:15:10,685 DEBUG: View 0 : 0.621118012422
+2016-08-24 11:15:10,692 DEBUG: View 1 : 0.385093167702
+2016-08-24 11:15:10,784 DEBUG: View 2 : 0.565217391304
+2016-08-24 11:15:10,791 DEBUG: View 3 : 0.552795031056
+2016-08-24 11:15:10,893 DEBUG: Best view : Methyl_
+2016-08-24 11:15:12,414 DEBUG: Start: Iteration 26
+2016-08-24 11:15:12,433 DEBUG: View 0 : 0.633540372671
+2016-08-24 11:15:12,440 DEBUG: View 1 : 0.577639751553
+2016-08-24 11:15:12,533 DEBUG: View 2 : 0.527950310559
+2016-08-24 11:15:12,541 DEBUG: View 3 : 0.67701863354
+2016-08-24 11:15:12,652 DEBUG: Best view : Clinic_
+2016-08-24 11:15:14,242 DEBUG: Start: Iteration 27
+2016-08-24 11:15:14,260 DEBUG: View 0 : 0.55900621118
+2016-08-24 11:15:14,268 DEBUG: View 1 : 0.614906832298
+2016-08-24 11:15:14,363 DEBUG: View 2 : 0.546583850932
+2016-08-24 11:15:14,371 DEBUG: View 3 : 0.546583850932
+2016-08-24 11:15:14,477 DEBUG: Best view : MiRNA__
+2016-08-24 11:15:16,122 DEBUG: Start: Iteration 28
+2016-08-24 11:15:16,140 DEBUG: View 0 : 0.583850931677
+2016-08-24 11:15:16,148 DEBUG: View 1 : 0.645962732919
+2016-08-24 11:15:16,243 DEBUG: View 2 : 0.565217391304
+2016-08-24 11:15:16,251 DEBUG: View 3 : 0.571428571429
+2016-08-24 11:15:16,360 DEBUG: Best view : MiRNA__
+2016-08-24 11:15:18,074 DEBUG: Start: Iteration 29
+2016-08-24 11:15:18,092 DEBUG: View 0 : 0.608695652174
+2016-08-24 11:15:18,100 DEBUG: View 1 : 0.627329192547
+2016-08-24 11:15:18,193 DEBUG: View 2 : 0.521739130435
+2016-08-24 11:15:18,201 DEBUG: View 3 : 0.664596273292
+2016-08-24 11:15:18,311 DEBUG: Best view : Clinic_
+2016-08-24 11:15:20,068 DEBUG: Start: Iteration 30
+2016-08-24 11:15:20,087 DEBUG: View 0 : 0.540372670807
+2016-08-24 11:15:20,095 DEBUG: View 1 : 0.639751552795
+2016-08-24 11:15:20,193 DEBUG: View 2 : 0.552795031056
+2016-08-24 11:15:20,201 DEBUG: View 3 : 0.627329192547
+2016-08-24 11:15:20,319 DEBUG: Best view : Clinic_
+2016-08-24 11:15:22,144 DEBUG: Start: Iteration 31
+2016-08-24 11:15:22,161 DEBUG: View 0 : 0.490683229814
+2016-08-24 11:15:22,169 DEBUG: View 1 : 0.360248447205
+2016-08-24 11:15:22,259 DEBUG: View 2 : 0.55900621118
+2016-08-24 11:15:22,267 DEBUG: View 3 : 0.534161490683
+2016-08-24 11:15:22,383 DEBUG: Best view : RANSeq_
+2016-08-24 11:15:24,278 DEBUG: Start: Iteration 32
+2016-08-24 11:15:24,297 DEBUG: View 0 : 0.534161490683
+2016-08-24 11:15:24,305 DEBUG: View 1 : 0.627329192547
+2016-08-24 11:15:24,397 DEBUG: View 2 : 0.639751552795
+2016-08-24 11:15:24,405 DEBUG: View 3 : 0.60248447205
+2016-08-24 11:15:24,524 DEBUG: Best view : RANSeq_
+2016-08-24 11:15:26,504 DEBUG: Start: Iteration 33
+2016-08-24 11:15:26,522 DEBUG: View 0 : 0.472049689441
+2016-08-24 11:15:26,529 DEBUG: View 1 : 0.639751552795
+2016-08-24 11:15:26,621 DEBUG: View 2 : 0.503105590062
+2016-08-24 11:15:26,629 DEBUG: View 3 : 0.664596273292
+2016-08-24 11:15:26,748 DEBUG: Best view : Clinic_
+2016-08-24 11:15:28,792 DEBUG: Start: Iteration 34
+2016-08-24 11:15:28,809 DEBUG: View 0 : 0.583850931677
+2016-08-24 11:15:28,817 DEBUG: View 1 : 0.55900621118
+2016-08-24 11:15:28,909 DEBUG: View 2 : 0.577639751553
+2016-08-24 11:15:28,917 DEBUG: View 3 : 0.621118012422
+2016-08-24 11:15:29,040 DEBUG: Best view : Clinic_
+2016-08-24 11:15:31,128 DEBUG: Start: Iteration 35
+2016-08-24 11:15:31,146 DEBUG: View 0 : 0.490683229814
+2016-08-24 11:15:31,153 DEBUG: View 1 : 0.571428571429
+2016-08-24 11:15:31,241 DEBUG: View 2 : 0.652173913043
+2016-08-24 11:15:31,248 DEBUG: View 3 : 0.596273291925
+2016-08-24 11:15:31,372 DEBUG: Best view : RANSeq_
+2016-08-24 11:15:33,543 DEBUG: Start: Iteration 36
+2016-08-24 11:15:33,561 DEBUG: View 0 : 0.614906832298
+2016-08-24 11:15:33,568 DEBUG: View 1 : 0.614906832298
+2016-08-24 11:15:33,651 DEBUG: View 2 : 0.515527950311
+2016-08-24 11:15:33,659 DEBUG: View 3 : 0.565217391304
+2016-08-24 11:15:33,785 DEBUG: Best view : MiRNA__
+2016-08-24 11:15:36,019 DEBUG: Start: Iteration 37
+2016-08-24 11:15:36,037 DEBUG: View 0 : 0.757763975155
+2016-08-24 11:15:36,045 DEBUG: View 1 : 0.60248447205
+2016-08-24 11:15:36,132 DEBUG: View 2 : 0.577639751553
+2016-08-24 11:15:36,140 DEBUG: View 3 : 0.490683229814
+2016-08-24 11:15:36,269 DEBUG: Best view : Methyl_
+2016-08-24 11:15:38,554 DEBUG: Start: Iteration 38
+2016-08-24 11:15:38,572 DEBUG: View 0 : 0.552795031056
+2016-08-24 11:15:38,579 DEBUG: View 1 : 0.683229813665
+2016-08-24 11:15:38,669 DEBUG: View 2 : 0.534161490683
+2016-08-24 11:15:38,677 DEBUG: View 3 : 0.571428571429
+2016-08-24 11:15:38,808 DEBUG: Best view : MiRNA__
+2016-08-24 11:15:41,153 DEBUG: Start: Iteration 39
+2016-08-24 11:15:41,170 DEBUG: View 0 : 0.652173913043
+2016-08-24 11:15:41,178 DEBUG: View 1 : 0.658385093168
+2016-08-24 11:15:41,265 DEBUG: View 2 : 0.658385093168
+2016-08-24 11:15:41,273 DEBUG: View 3 : 0.596273291925
+2016-08-24 11:15:41,405 DEBUG: Best view : RANSeq_
+2016-08-24 11:15:43,837 DEBUG: Start: Iteration 40
+2016-08-24 11:15:43,854 DEBUG: View 0 : 0.633540372671
+2016-08-24 11:15:43,862 DEBUG: View 1 : 0.39751552795
+2016-08-24 11:15:43,951 DEBUG: View 2 : 0.55900621118
+2016-08-24 11:15:43,959 DEBUG: View 3 : 0.590062111801
+2016-08-24 11:15:44,094 DEBUG: Best view : Methyl_
+2016-08-24 11:15:46,579 DEBUG: Start: Iteration 41
+2016-08-24 11:15:46,597 DEBUG: View 0 : 0.552795031056
+2016-08-24 11:15:46,605 DEBUG: View 1 : 0.639751552795
+2016-08-24 11:15:46,694 DEBUG: View 2 : 0.60248447205
+2016-08-24 11:15:46,702 DEBUG: View 3 : 0.478260869565
+2016-08-24 11:15:46,839 DEBUG: Best view : MiRNA__
+2016-08-24 11:15:49,378 DEBUG: Start: Iteration 42
+2016-08-24 11:15:49,396 DEBUG: View 0 : 0.565217391304
+2016-08-24 11:15:49,404 DEBUG: View 1 : 0.596273291925
+2016-08-24 11:15:49,491 DEBUG: View 2 : 0.608695652174
+2016-08-24 11:15:49,499 DEBUG: View 3 : 0.608695652174
+2016-08-24 11:15:49,638 DEBUG: Best view : Clinic_
+2016-08-24 11:15:52,257 DEBUG: Start: Iteration 43
+2016-08-24 11:15:52,275 DEBUG: View 0 : 0.701863354037
+2016-08-24 11:15:52,283 DEBUG: View 1 : 0.645962732919
+2016-08-24 11:15:52,377 DEBUG: View 2 : 0.627329192547
+2016-08-24 11:15:52,385 DEBUG: View 3 : 0.608695652174
+2016-08-24 11:15:52,526 DEBUG: Best view : Methyl_
+2016-08-24 11:15:55,194 DEBUG: Start: Iteration 44
+2016-08-24 11:15:55,213 DEBUG: View 0 : 0.472049689441
+2016-08-24 11:15:55,221 DEBUG: View 1 : 0.496894409938
+2016-08-24 11:15:55,316 DEBUG: View 2 : 0.509316770186
+2016-08-24 11:15:55,324 DEBUG: View 3 : 0.552795031056
+2016-08-24 11:15:55,466 DEBUG: Best view : Clinic_
+2016-08-24 11:15:58,192 DEBUG: Start: Iteration 45
+2016-08-24 11:15:58,210 DEBUG: View 0 : 0.484472049689
+2016-08-24 11:15:58,217 DEBUG: View 1 : 0.621118012422
+2016-08-24 11:15:58,311 DEBUG: View 2 : 0.583850931677
+2016-08-24 11:15:58,318 DEBUG: View 3 : 0.583850931677
+2016-08-24 11:15:58,464 DEBUG: Best view : MiRNA__
+2016-08-24 11:16:01,240 DEBUG: Start: Iteration 46
+2016-08-24 11:16:01,258 DEBUG: View 0 : 0.60248447205
+2016-08-24 11:16:01,265 DEBUG: View 1 : 0.639751552795
+2016-08-24 11:16:01,360 DEBUG: View 2 : 0.565217391304
+2016-08-24 11:16:01,368 DEBUG: View 3 : 0.521739130435
+2016-08-24 11:16:01,516 DEBUG: Best view : MiRNA__
+2016-08-24 11:16:04,354 DEBUG: Start: Iteration 47
+2016-08-24 11:16:04,372 DEBUG: View 0 : 0.639751552795
+2016-08-24 11:16:04,380 DEBUG: View 1 : 0.633540372671
+2016-08-24 11:16:04,472 DEBUG: View 2 : 0.521739130435
+2016-08-24 11:16:04,480 DEBUG: View 3 : 0.490683229814
+2016-08-24 11:16:04,629 DEBUG: Best view : Methyl_
+2016-08-24 11:16:07,543 DEBUG: Start: Iteration 48
+2016-08-24 11:16:07,561 DEBUG: View 0 : 0.577639751553
+2016-08-24 11:16:07,569 DEBUG: View 1 : 0.633540372671
+2016-08-24 11:16:07,661 DEBUG: View 2 : 0.60248447205
+2016-08-24 11:16:07,669 DEBUG: View 3 : 0.596273291925
+2016-08-24 11:16:07,822 DEBUG: Best view : Clinic_
+2016-08-24 11:16:10,789 DEBUG: Start: Iteration 49
+2016-08-24 11:16:10,807 DEBUG: View 0 : 0.453416149068
+2016-08-24 11:16:10,814 DEBUG: View 1 : 0.571428571429
+2016-08-24 11:16:10,907 DEBUG: View 2 : 0.639751552795
+2016-08-24 11:16:10,915 DEBUG: View 3 : 0.633540372671
+2016-08-24 11:16:11,069 DEBUG: Best view : Clinic_
+2016-08-24 11:16:14,095 DEBUG: Start: Iteration 50
+2016-08-24 11:16:14,112 DEBUG: View 0 : 0.670807453416
+2016-08-24 11:16:14,120 DEBUG: View 1 : 0.664596273292
+2016-08-24 11:16:14,200 DEBUG: View 2 : 0.596273291925
+2016-08-24 11:16:14,208 DEBUG: View 3 : 0.60248447205
+2016-08-24 11:16:14,365 DEBUG: Best view : Methyl_
+2016-08-24 11:16:17,472 DEBUG: Start: Iteration 51
+2016-08-24 11:16:17,490 DEBUG: View 0 : 0.652173913043
+2016-08-24 11:16:17,498 DEBUG: View 1 : 0.416149068323
+2016-08-24 11:16:17,602 DEBUG: View 2 : 0.484472049689
+2016-08-24 11:16:17,610 DEBUG: View 3 : 0.621118012422
+2016-08-24 11:16:17,772 DEBUG: Best view : Methyl_
+2016-08-24 11:16:20,914 DEBUG: Start: Iteration 52
+2016-08-24 11:16:20,932 DEBUG: View 0 : 0.689440993789
+2016-08-24 11:16:20,940 DEBUG: View 1 : 0.627329192547
+2016-08-24 11:16:21,034 DEBUG: View 2 : 0.552795031056
+2016-08-24 11:16:21,041 DEBUG: View 3 : 0.490683229814
+2016-08-24 11:16:21,205 DEBUG: Best view : Methyl_
+2016-08-24 11:16:24,426 DEBUG: Start: Iteration 53
+2016-08-24 11:16:24,443 DEBUG: View 0 : 0.565217391304
+2016-08-24 11:16:24,451 DEBUG: View 1 : 0.658385093168
+2016-08-24 11:16:24,538 DEBUG: View 2 : 0.590062111801
+2016-08-24 11:16:24,546 DEBUG: View 3 : 0.534161490683
+2016-08-24 11:16:24,710 DEBUG: Best view : MiRNA__
+2016-08-24 11:16:27,999 DEBUG: Start: Iteration 54
+2016-08-24 11:16:28,017 DEBUG: View 0 : 0.577639751553
+2016-08-24 11:16:28,025 DEBUG: View 1 : 0.434782608696
+2016-08-24 11:16:28,116 DEBUG: View 2 : 0.552795031056
+2016-08-24 11:16:28,124 DEBUG: View 3 : 0.534161490683
+2016-08-24 11:16:28,291 DEBUG: Best view : Methyl_
+2016-08-24 11:16:31,633 DEBUG: Start: Iteration 55
+2016-08-24 11:16:31,650 DEBUG: View 0 : 0.434782608696
+2016-08-24 11:16:31,658 DEBUG: View 1 : 0.645962732919
+2016-08-24 11:16:31,746 DEBUG: View 2 : 0.577639751553
+2016-08-24 11:16:31,754 DEBUG: View 3 : 0.689440993789
+2016-08-24 11:16:31,923 DEBUG: Best view : Clinic_
+2016-08-24 11:16:35,333 DEBUG: Start: Iteration 56
+2016-08-24 11:16:35,351 DEBUG: View 0 : 0.614906832298
+2016-08-24 11:16:35,359 DEBUG: View 1 : 0.857142857143
+2016-08-24 11:16:35,449 DEBUG: View 2 : 0.546583850932
+2016-08-24 11:16:35,456 DEBUG: View 3 : 0.627329192547
+2016-08-24 11:16:35,627 DEBUG: Best view : MiRNA__
+2016-08-24 11:16:39,082 DEBUG: Start: Iteration 57
+2016-08-24 11:16:39,100 DEBUG: View 0 : 0.527950310559
+2016-08-24 11:16:39,108 DEBUG: View 1 : 0.434782608696
+2016-08-24 11:16:39,201 DEBUG: View 2 : 0.621118012422
+2016-08-24 11:16:39,209 DEBUG: View 3 : 0.590062111801
+2016-08-24 11:16:39,381 DEBUG: Best view : RANSeq_
+2016-08-24 11:16:42,922 DEBUG: Start: Iteration 58
+2016-08-24 11:16:42,940 DEBUG: View 0 : 0.540372670807
+2016-08-24 11:16:42,948 DEBUG: View 1 : 0.534161490683
+2016-08-24 11:16:43,034 DEBUG: View 2 : 0.565217391304
+2016-08-24 11:16:43,041 DEBUG: View 3 : 0.490683229814
+2016-08-24 11:16:43,217 DEBUG: Best view : RANSeq_
+2016-08-24 11:16:46,827 DEBUG: Start: Iteration 59
+2016-08-24 11:16:46,845 DEBUG: View 0 : 0.55900621118
+2016-08-24 11:16:46,853 DEBUG: View 1 : 0.639751552795
+2016-08-24 11:16:46,947 DEBUG: View 2 : 0.627329192547
+2016-08-24 11:16:46,955 DEBUG: View 3 : 0.590062111801
+2016-08-24 11:16:47,141 DEBUG: Best view : RANSeq_
+2016-08-24 11:16:50,821 DEBUG: Start: Iteration 60
+2016-08-24 11:16:50,841 DEBUG: View 0 : 0.608695652174
+2016-08-24 11:16:50,848 DEBUG: View 1 : 0.478260869565
+2016-08-24 11:16:50,938 DEBUG: View 2 : 0.596273291925
+2016-08-24 11:16:50,947 DEBUG: View 3 : 0.55900621118
+2016-08-24 11:16:51,127 DEBUG: Best view : RANSeq_
+2016-08-24 11:16:54,882 DEBUG: Start: Iteration 61
+2016-08-24 11:16:54,900 DEBUG: View 0 : 0.403726708075
+2016-08-24 11:16:54,908 DEBUG: View 1 : 0.652173913043
+2016-08-24 11:16:54,998 DEBUG: View 2 : 0.60248447205
+2016-08-24 11:16:55,006 DEBUG: View 3 : 0.658385093168
+2016-08-24 11:16:55,186 DEBUG: Best view : Clinic_
+2016-08-24 11:16:58,982 DEBUG: Start: Iteration 62
+2016-08-24 11:16:59,000 DEBUG: View 0 : 0.509316770186
+2016-08-24 11:16:59,008 DEBUG: View 1 : 0.72049689441
+2016-08-24 11:16:59,102 DEBUG: View 2 : 0.633540372671
+2016-08-24 11:16:59,110 DEBUG: View 3 : 0.67701863354
+2016-08-24 11:16:59,295 DEBUG: Best view : MiRNA__
+2016-08-24 11:17:03,161 DEBUG: Start: Iteration 63
+2016-08-24 11:17:03,179 DEBUG: View 0 : 0.527950310559
+2016-08-24 11:17:03,187 DEBUG: View 1 : 0.484472049689
+2016-08-24 11:17:03,278 DEBUG: View 2 : 0.596273291925
+2016-08-24 11:17:03,286 DEBUG: View 3 : 0.639751552795
+2016-08-24 11:17:03,471 DEBUG: Best view : Clinic_
+2016-08-24 11:17:07,398 DEBUG: Start: Iteration 64
+2016-08-24 11:17:07,416 DEBUG: View 0 : 0.546583850932
+2016-08-24 11:17:07,425 DEBUG: View 1 : 0.633540372671
+2016-08-24 11:17:07,518 DEBUG: View 2 : 0.633540372671
+2016-08-24 11:17:07,526 DEBUG: View 3 : 0.509316770186
+2016-08-24 11:17:07,715 DEBUG: Best view : RANSeq_
+2016-08-24 11:17:11,730 DEBUG: Start: Iteration 65
+2016-08-24 11:17:11,749 DEBUG: View 0 : 0.447204968944
+2016-08-24 11:17:11,757 DEBUG: View 1 : 0.496894409938
+2016-08-24 11:17:11,849 DEBUG: View 2 : 0.614906832298
+2016-08-24 11:17:11,857 DEBUG: View 3 : 0.503105590062
+2016-08-24 11:17:12,055 DEBUG: Best view : RANSeq_
+2016-08-24 11:17:16,117 DEBUG: Start: Iteration 66
+2016-08-24 11:17:16,135 DEBUG: View 0 : 0.534161490683
+2016-08-24 11:17:16,143 DEBUG: View 1 : 0.614906832298
+2016-08-24 11:17:16,223 DEBUG: View 2 : 0.621118012422
+2016-08-24 11:17:16,231 DEBUG: View 3 : 0.683229813665
+2016-08-24 11:17:16,425 DEBUG: Best view : Clinic_
+2016-08-24 11:17:20,587 DEBUG: Start: Iteration 67
+2016-08-24 11:17:20,604 DEBUG: View 0 : 0.428571428571
+2016-08-24 11:17:20,612 DEBUG: View 1 : 0.496894409938
+2016-08-24 11:17:20,705 DEBUG: View 2 : 0.645962732919
+2016-08-24 11:17:20,713 DEBUG: View 3 : 0.60248447205
+2016-08-24 11:17:20,908 DEBUG: Best view : RANSeq_
+2016-08-24 11:17:25,155 DEBUG: Start: Iteration 68
+2016-08-24 11:17:25,173 DEBUG: View 0 : 0.534161490683
+2016-08-24 11:17:25,181 DEBUG: View 1 : 0.664596273292
+2016-08-24 11:17:25,267 DEBUG: View 2 : 0.627329192547
+2016-08-24 11:17:25,275 DEBUG: View 3 : 0.670807453416
+2016-08-24 11:17:25,469 DEBUG: Best view : Clinic_
+2016-08-24 11:17:29,738 DEBUG: Start: Iteration 69
+2016-08-24 11:17:29,756 DEBUG: View 0 : 0.465838509317
+2016-08-24 11:17:29,764 DEBUG: View 1 : 0.633540372671
+2016-08-24 11:17:29,858 DEBUG: View 2 : 0.652173913043
+2016-08-24 11:17:29,866 DEBUG: View 3 : 0.608695652174
+2016-08-24 11:17:30,064 DEBUG: Best view : RANSeq_
+2016-08-24 11:17:34,406 DEBUG: Start: Iteration 70
+2016-08-24 11:17:34,424 DEBUG: View 0 : 0.552795031056
+2016-08-24 11:17:34,432 DEBUG: View 1 : 0.596273291925
+2016-08-24 11:17:34,520 DEBUG: View 2 : 0.627329192547
+2016-08-24 11:17:34,528 DEBUG: View 3 : 0.608695652174
+2016-08-24 11:17:34,727 DEBUG: Best view : Clinic_
+2016-08-24 11:17:39,117 DEBUG: Start: Iteration 71
+2016-08-24 11:17:39,135 DEBUG: View 0 : 0.565217391304
+2016-08-24 11:17:39,143 DEBUG: View 1 : 0.515527950311
+2016-08-24 11:17:39,228 DEBUG: View 2 : 0.546583850932
+2016-08-24 11:17:39,236 DEBUG: View 3 : 0.621118012422
+2016-08-24 11:17:39,489 DEBUG: Best view : Clinic_
+2016-08-24 11:17:43,962 DEBUG: Start: Iteration 72
+2016-08-24 11:17:43,980 DEBUG: View 0 : 0.621118012422
+2016-08-24 11:17:43,987 DEBUG: View 1 : 0.565217391304
+2016-08-24 11:17:44,079 DEBUG: View 2 : 0.55900621118
+2016-08-24 11:17:44,088 DEBUG: View 3 : 0.577639751553
+2016-08-24 11:17:44,293 DEBUG: Best view : Methyl_
+2016-08-24 11:17:48,830 DEBUG: Start: Iteration 73
+2016-08-24 11:17:48,850 DEBUG: View 0 : 0.67701863354
+2016-08-24 11:17:48,860 DEBUG: View 1 : 0.540372670807
+2016-08-24 11:17:48,961 DEBUG: View 2 : 0.590062111801
+2016-08-24 11:17:48,968 DEBUG: View 3 : 0.633540372671
+2016-08-24 11:17:49,176 DEBUG: Best view : Clinic_
+2016-08-24 11:17:53,775 DEBUG: Start: Iteration 74
+2016-08-24 11:17:53,793 DEBUG: View 0 : 0.453416149068
+2016-08-24 11:17:53,800 DEBUG: View 1 : 0.652173913043
+2016-08-24 11:17:53,889 DEBUG: View 2 : 0.60248447205
+2016-08-24 11:17:53,897 DEBUG: View 3 : 0.503105590062
+2016-08-24 11:17:54,119 DEBUG: Best view : MiRNA__
+2016-08-24 11:17:58,762 DEBUG: Start: Iteration 75
+2016-08-24 11:17:58,780 DEBUG: View 0 : 0.503105590062
+2016-08-24 11:17:58,788 DEBUG: View 1 : 0.627329192547
+2016-08-24 11:17:58,880 DEBUG: View 2 : 0.652173913043
+2016-08-24 11:17:58,888 DEBUG: View 3 : 0.670807453416
+2016-08-24 11:17:59,101 DEBUG: Best view : Clinic_
+2016-08-24 11:18:03,812 DEBUG: Start: Iteration 76
+2016-08-24 11:18:03,829 DEBUG: View 0 : 0.478260869565
+2016-08-24 11:18:03,837 DEBUG: View 1 : 0.627329192547
+2016-08-24 11:18:03,929 DEBUG: View 2 : 0.596273291925
+2016-08-24 11:18:03,937 DEBUG: View 3 : 0.521739130435
+2016-08-24 11:18:04,151 DEBUG: Best view : RANSeq_
+2016-08-24 11:18:08,927 DEBUG: Start: Iteration 77
+2016-08-24 11:18:08,945 DEBUG: View 0 : 0.509316770186
+2016-08-24 11:18:08,953 DEBUG: View 1 : 0.465838509317
+2016-08-24 11:18:09,049 DEBUG: View 2 : 0.515527950311
+2016-08-24 11:18:09,057 DEBUG: View 3 : 0.552795031056
+2016-08-24 11:18:09,272 DEBUG: Best view : Clinic_
+2016-08-24 11:18:14,096 DEBUG: Start: Iteration 78
+2016-08-24 11:18:14,114 DEBUG: View 0 : 0.496894409938
+2016-08-24 11:18:14,122 DEBUG: View 1 : 0.614906832298
+2016-08-24 11:18:14,216 DEBUG: View 2 : 0.540372670807
+2016-08-24 11:18:14,223 DEBUG: View 3 : 0.583850931677
+2016-08-24 11:18:14,442 DEBUG: Best view : MiRNA__
+2016-08-24 11:18:19,347 DEBUG: Start: Iteration 79
+2016-08-24 11:18:19,365 DEBUG: View 0 : 0.608695652174
+2016-08-24 11:18:19,373 DEBUG: View 1 : 0.726708074534
+2016-08-24 11:18:19,461 DEBUG: View 2 : 0.621118012422
+2016-08-24 11:18:19,469 DEBUG: View 3 : 0.664596273292
+2016-08-24 11:18:19,693 DEBUG: Best view : MiRNA__
+2016-08-24 11:18:24,651 DEBUG: Start: Iteration 80
+2016-08-24 11:18:24,669 DEBUG: View 0 : 0.546583850932
+2016-08-24 11:18:24,676 DEBUG: View 1 : 0.633540372671
+2016-08-24 11:18:24,765 DEBUG: View 2 : 0.503105590062
+2016-08-24 11:18:24,773 DEBUG: View 3 : 0.670807453416
+2016-08-24 11:18:24,993 DEBUG: Best view : Clinic_
+2016-08-24 11:18:30,011 DEBUG: Start: Iteration 81
+2016-08-24 11:18:30,030 DEBUG: View 0 : 0.490683229814
+2016-08-24 11:18:30,038 DEBUG: View 1 : 0.577639751553
+2016-08-24 11:18:30,128 DEBUG: View 2 : 0.55900621118
+2016-08-24 11:18:30,137 DEBUG: View 3 : 0.596273291925
+2016-08-24 11:18:30,364 DEBUG: Best view : Clinic_
+2016-08-24 11:18:35,443 DEBUG: Start: Iteration 82
+2016-08-24 11:18:35,462 DEBUG: View 0 : 0.527950310559
+2016-08-24 11:18:35,470 DEBUG: View 1 : 0.608695652174
+2016-08-24 11:18:35,558 DEBUG: View 2 : 0.552795031056
+2016-08-24 11:18:35,567 DEBUG: View 3 : 0.652173913043
+2016-08-24 11:18:35,795 DEBUG: Best view : Clinic_
+2016-08-24 11:18:40,943 DEBUG: Start: Iteration 83
+2016-08-24 11:18:40,961 DEBUG: View 0 : 0.689440993789
+2016-08-24 11:18:40,969 DEBUG: View 1 : 0.658385093168
+2016-08-24 11:18:41,058 DEBUG: View 2 : 0.521739130435
+2016-08-24 11:18:41,066 DEBUG: View 3 : 0.645962732919
+2016-08-24 11:18:41,297 DEBUG: Best view : Methyl_
+2016-08-24 11:18:46,494 DEBUG: Start: Iteration 84
+2016-08-24 11:18:46,512 DEBUG: View 0 : 0.55900621118
+2016-08-24 11:18:46,520 DEBUG: View 1 : 0.652173913043
+2016-08-24 11:18:46,614 DEBUG: View 2 : 0.608695652174
+2016-08-24 11:18:46,622 DEBUG: View 3 : 0.546583850932
+2016-08-24 11:18:46,855 DEBUG: Best view : MiRNA__
+2016-08-24 11:18:52,119 DEBUG: Start: Iteration 85
+2016-08-24 11:18:52,137 DEBUG: View 0 : 0.515527950311
+2016-08-24 11:18:52,144 DEBUG: View 1 : 0.453416149068
+2016-08-24 11:18:52,235 DEBUG: View 2 : 0.633540372671
+2016-08-24 11:18:52,243 DEBUG: View 3 : 0.577639751553
+2016-08-24 11:18:52,479 DEBUG: Best view : RANSeq_
+2016-08-24 11:18:57,816 DEBUG: Start: Iteration 86
+2016-08-24 11:18:57,834 DEBUG: View 0 : 0.614906832298
+2016-08-24 11:18:57,842 DEBUG: View 1 : 0.608695652174
+2016-08-24 11:18:57,936 DEBUG: View 2 : 0.670807453416
+2016-08-24 11:18:57,944 DEBUG: View 3 : 0.608695652174
+2016-08-24 11:18:58,180 DEBUG: Best view : RANSeq_
+2016-08-24 11:19:03,593 DEBUG: Start: Iteration 87
+2016-08-24 11:19:03,611 DEBUG: View 0 : 0.60248447205
+2016-08-24 11:19:03,619 DEBUG: View 1 : 0.608695652174
+2016-08-24 11:19:03,713 DEBUG: View 2 : 0.627329192547
+2016-08-24 11:19:03,721 DEBUG: View 3 : 0.521739130435
+2016-08-24 11:19:03,959 DEBUG: Best view : RANSeq_
+2016-08-24 11:19:09,435 DEBUG: Start: Iteration 88
+2016-08-24 11:19:09,453 DEBUG: View 0 : 0.664596273292
+2016-08-24 11:19:09,461 DEBUG: View 1 : 0.534161490683
+2016-08-24 11:19:09,553 DEBUG: View 2 : 0.55900621118
+2016-08-24 11:19:09,560 DEBUG: View 3 : 0.596273291925
+2016-08-24 11:19:09,799 DEBUG: Best view : Methyl_
+2016-08-24 11:19:15,336 DEBUG: Start: Iteration 89
+2016-08-24 11:19:15,355 DEBUG: View 0 : 0.527950310559
+2016-08-24 11:19:15,363 DEBUG: View 1 : 0.515527950311
+2016-08-24 11:19:15,465 DEBUG: View 2 : 0.633540372671
+2016-08-24 11:19:15,473 DEBUG: View 3 : 0.652173913043
+2016-08-24 11:19:15,721 DEBUG: Best view : Clinic_
+2016-08-24 11:19:21,322 DEBUG: Start: Iteration 90
+2016-08-24 11:19:21,340 DEBUG: View 0 : 0.478260869565
+2016-08-24 11:19:21,348 DEBUG: View 1 : 0.670807453416
+2016-08-24 11:19:21,443 DEBUG: View 2 : 0.60248447205
+2016-08-24 11:19:21,450 DEBUG: View 3 : 0.577639751553
+2016-08-24 11:19:21,697 DEBUG: Best view : MiRNA__
+2016-08-24 11:19:27,356 DEBUG: Start: Iteration 91
+2016-08-24 11:19:27,374 DEBUG: View 0 : 0.521739130435
+2016-08-24 11:19:27,382 DEBUG: View 1 : 0.496894409938
+2016-08-24 11:19:27,473 DEBUG: View 2 : 0.627329192547
+2016-08-24 11:19:27,481 DEBUG: View 3 : 0.527950310559
+2016-08-24 11:19:27,732 DEBUG: Best view : RANSeq_
+2016-08-24 11:19:33,475 DEBUG: Start: Iteration 92
+2016-08-24 11:19:33,493 DEBUG: View 0 : 0.546583850932
+2016-08-24 11:19:33,501 DEBUG: View 1 : 0.55900621118
+2016-08-24 11:19:33,591 DEBUG: View 2 : 0.627329192547
+2016-08-24 11:19:33,600 DEBUG: View 3 : 0.583850931677
+2016-08-24 11:19:33,851 DEBUG: Best view : RANSeq_
+2016-08-24 11:19:39,661 DEBUG: Start: Iteration 93
+2016-08-24 11:19:39,679 DEBUG: View 0 : 0.590062111801
+2016-08-24 11:19:39,687 DEBUG: View 1 : 0.708074534161
+2016-08-24 11:19:39,775 DEBUG: View 2 : 0.521739130435
+2016-08-24 11:19:39,783 DEBUG: View 3 : 0.546583850932
+2016-08-24 11:19:40,039 DEBUG: Best view : MiRNA__
+2016-08-24 11:19:45,933 DEBUG: Start: Iteration 94
+2016-08-24 11:19:45,951 DEBUG: View 0 : 0.645962732919
+2016-08-24 11:19:45,959 DEBUG: View 1 : 0.409937888199
+2016-08-24 11:19:46,043 DEBUG: View 2 : 0.55900621118
+2016-08-24 11:19:46,051 DEBUG: View 3 : 0.639751552795
+2016-08-24 11:19:46,302 DEBUG: Best view : Clinic_
+2016-08-24 11:19:52,252 DEBUG: Start: Iteration 95
+2016-08-24 11:19:52,270 DEBUG: View 0 : 0.583850931677
+2016-08-24 11:19:52,278 DEBUG: View 1 : 0.552795031056
+2016-08-24 11:19:52,367 DEBUG: View 2 : 0.552795031056
+2016-08-24 11:19:52,375 DEBUG: View 3 : 0.596273291925
+2016-08-24 11:19:52,642 DEBUG: Best view : Clinic_
+2016-08-24 11:19:58,625 DEBUG: Start: Iteration 96
+2016-08-24 11:19:58,643 DEBUG: View 0 : 0.540372670807
+2016-08-24 11:19:58,651 DEBUG: View 1 : 0.496894409938
+2016-08-24 11:19:58,742 DEBUG: View 2 : 0.60248447205
+2016-08-24 11:19:58,749 DEBUG: View 3 : 0.465838509317
+2016-08-24 11:19:59,007 DEBUG: Best view : RANSeq_
+2016-08-24 11:20:05,084 DEBUG: Start: Iteration 97
+2016-08-24 11:20:05,103 DEBUG: View 0 : 0.577639751553
+2016-08-24 11:20:05,111 DEBUG: View 1 : 0.683229813665
+2016-08-24 11:20:05,209 DEBUG: View 2 : 0.509316770186
+2016-08-24 11:20:05,217 DEBUG: View 3 : 0.590062111801
+2016-08-24 11:20:05,489 DEBUG: Best view : MiRNA__
+2016-08-24 11:20:11,716 DEBUG: Start: Iteration 98
+2016-08-24 11:20:11,734 DEBUG: View 0 : 0.571428571429
+2016-08-24 11:20:11,743 DEBUG: View 1 : 0.621118012422
+2016-08-24 11:20:11,834 DEBUG: View 2 : 0.621118012422
+2016-08-24 11:20:11,842 DEBUG: View 3 : 0.478260869565
+2016-08-24 11:20:12,104 DEBUG: Best view : MiRNA__
+2016-08-24 11:20:18,337 DEBUG: Start: Iteration 99
+2016-08-24 11:20:18,355 DEBUG: View 0 : 0.596273291925
+2016-08-24 11:20:18,363 DEBUG: View 1 : 0.621118012422
+2016-08-24 11:20:18,460 DEBUG: View 2 : 0.571428571429
+2016-08-24 11:20:18,468 DEBUG: View 3 : 0.55900621118
+2016-08-24 11:20:18,734 DEBUG: Best view : MiRNA__
+2016-08-24 11:20:25,017 DEBUG: Start: Iteration 100
+2016-08-24 11:20:25,036 DEBUG: View 0 : 0.403726708075
+2016-08-24 11:20:25,044 DEBUG: View 1 : 0.590062111801
+2016-08-24 11:20:25,138 DEBUG: View 2 : 0.60248447205
+2016-08-24 11:20:25,146 DEBUG: View 3 : 0.540372670807
+2016-08-24 11:20:25,413 DEBUG: Best view : MiRNA__
+2016-08-24 11:20:31,776 DEBUG: Start: Iteration 101
+2016-08-24 11:20:31,794 DEBUG: View 0 : 0.496894409938
+2016-08-24 11:20:31,802 DEBUG: View 1 : 0.714285714286
+2016-08-24 11:20:31,895 DEBUG: View 2 : 0.503105590062
+2016-08-24 11:20:31,902 DEBUG: View 3 : 0.496894409938
+2016-08-24 11:20:32,172 DEBUG: Best view : MiRNA__
+2016-08-24 11:20:38,807 DEBUG: Start: Iteration 102
+2016-08-24 11:20:38,831 DEBUG: View 0 : 0.695652173913
+2016-08-24 11:20:38,843 DEBUG: View 1 : 0.403726708075
+2016-08-24 11:20:38,958 DEBUG: View 2 : 0.496894409938
+2016-08-24 11:20:38,968 DEBUG: View 3 : 0.683229813665
+2016-08-24 11:20:39,249 DEBUG: Best view : Clinic_
+2016-08-24 11:20:45,713 INFO: Start: Classification
+2016-08-24 11:21:01,314 INFO: Done: Fold number 1
+2016-08-24 11:21:01,314 INFO: Start: Fold number 2
+2016-08-24 11:21:02,941 DEBUG: Start: Iteration 1
+2016-08-24 11:21:02,957 DEBUG: View 0 : 0.615384615385
+2016-08-24 11:21:02,965 DEBUG: View 1 : 0.615384615385
+2016-08-24 11:21:03,052 DEBUG: View 2 : 0.615384615385
+2016-08-24 11:21:03,060 DEBUG: View 3 : 0.615384615385
+2016-08-24 11:21:03,101 DEBUG: Best view : Methyl_
+2016-08-24 11:21:03,178 DEBUG: Start: Iteration 2
+2016-08-24 11:21:03,195 DEBUG: View 0 : 0.557692307692
+2016-08-24 11:21:03,203 DEBUG: View 1 : 0.455128205128
+2016-08-24 11:21:03,294 DEBUG: View 2 : 0.576923076923
+2016-08-24 11:21:03,301 DEBUG: View 3 : 0.608974358974
+2016-08-24 11:21:03,347 DEBUG: Best view : Clinic_
+2016-08-24 11:21:03,483 DEBUG: Start: Iteration 3
+2016-08-24 11:21:03,501 DEBUG: View 0 : 0.487179487179
+2016-08-24 11:21:03,509 DEBUG: View 1 : 0.570512820513
+2016-08-24 11:21:03,594 DEBUG: View 2 : 0.442307692308
+2016-08-24 11:21:03,602 DEBUG: View 3 : 0.474358974359
+2016-08-24 11:21:03,656 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:03,847 DEBUG: Start: Iteration 4
+2016-08-24 11:21:03,863 DEBUG: View 0 : 0.487179487179
+2016-08-24 11:21:03,871 DEBUG: View 1 : 0.628205128205
+2016-08-24 11:21:03,953 DEBUG: View 2 : 0.564102564103
+2016-08-24 11:21:03,961 DEBUG: View 3 : 0.673076923077
+2016-08-24 11:21:04,015 DEBUG: Best view : Clinic_
+2016-08-24 11:21:04,261 DEBUG: Start: Iteration 5
+2016-08-24 11:21:04,278 DEBUG: View 0 : 0.467948717949
+2016-08-24 11:21:04,285 DEBUG: View 1 : 0.384615384615
+2016-08-24 11:21:04,372 DEBUG: View 2 : 0.589743589744
+2016-08-24 11:21:04,380 DEBUG: View 3 : 0.583333333333
+2016-08-24 11:21:04,437 DEBUG: Best view : RANSeq_
+2016-08-24 11:21:04,758 DEBUG: Start: Iteration 6
+2016-08-24 11:21:04,775 DEBUG: View 0 : 0.74358974359
+2016-08-24 11:21:04,783 DEBUG: View 1 : 0.538461538462
+2016-08-24 11:21:04,865 DEBUG: View 2 : 0.608974358974
+2016-08-24 11:21:04,873 DEBUG: View 3 : 0.544871794872
+2016-08-24 11:21:04,933 DEBUG: Best view : Methyl_
+2016-08-24 11:21:05,314 DEBUG: Start: Iteration 7
+2016-08-24 11:21:05,331 DEBUG: View 0 : 0.50641025641
+2016-08-24 11:21:05,338 DEBUG: View 1 : 0.628205128205
+2016-08-24 11:21:05,422 DEBUG: View 2 : 0.5
+2016-08-24 11:21:05,430 DEBUG: View 3 : 0.641025641026
+2016-08-24 11:21:05,491 DEBUG: Best view : Clinic_
+2016-08-24 11:21:05,935 DEBUG: Start: Iteration 8
+2016-08-24 11:21:05,952 DEBUG: View 0 : 0.487179487179
+2016-08-24 11:21:05,959 DEBUG: View 1 : 0.596153846154
+2016-08-24 11:21:06,054 DEBUG: View 2 : 0.564102564103
+2016-08-24 11:21:06,063 DEBUG: View 3 : 0.557692307692
+2016-08-24 11:21:06,129 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:06,623 DEBUG: Start: Iteration 9
+2016-08-24 11:21:06,639 DEBUG: View 0 : 0.487179487179
+2016-08-24 11:21:06,647 DEBUG: View 1 : 0.596153846154
+2016-08-24 11:21:06,724 DEBUG: View 2 : 0.564102564103
+2016-08-24 11:21:06,731 DEBUG: View 3 : 0.596153846154
+2016-08-24 11:21:06,797 DEBUG: Best view : Clinic_
+2016-08-24 11:21:07,345 DEBUG: Start: Iteration 10
+2016-08-24 11:21:07,367 DEBUG: View 0 : 0.653846153846
+2016-08-24 11:21:07,375 DEBUG: View 1 : 0.608974358974
+2016-08-24 11:21:07,458 DEBUG: View 2 : 0.634615384615
+2016-08-24 11:21:07,466 DEBUG: View 3 : 0.673076923077
+2016-08-24 11:21:07,533 DEBUG: Best view : Clinic_
+2016-08-24 11:21:08,137 DEBUG: Start: Iteration 11
+2016-08-24 11:21:08,153 DEBUG: View 0 : 0.49358974359
+2016-08-24 11:21:08,162 DEBUG: View 1 : 0.24358974359
+2016-08-24 11:21:08,271 DEBUG: View 2 : 0.519230769231
+2016-08-24 11:21:08,280 DEBUG: View 3 : 0.634615384615
+2016-08-24 11:21:08,355 DEBUG: Best view : Clinic_
+2016-08-24 11:21:09,028 DEBUG: Start: Iteration 12
+2016-08-24 11:21:09,044 DEBUG: View 0 : 0.602564102564
+2016-08-24 11:21:09,051 DEBUG: View 1 : 0.564102564103
+2016-08-24 11:21:09,139 DEBUG: View 2 : 0.557692307692
+2016-08-24 11:21:09,146 DEBUG: View 3 : 0.583333333333
+2016-08-24 11:21:09,228 DEBUG: Best view : Clinic_
+2016-08-24 11:21:09,951 DEBUG: Start: Iteration 13
+2016-08-24 11:21:09,968 DEBUG: View 0 : 0.679487179487
+2016-08-24 11:21:09,976 DEBUG: View 1 : 0.615384615385
+2016-08-24 11:21:10,058 DEBUG: View 2 : 0.596153846154
+2016-08-24 11:21:10,066 DEBUG: View 3 : 0.570512820513
+2016-08-24 11:21:10,140 DEBUG: Best view : Methyl_
+2016-08-24 11:21:10,927 DEBUG: Start: Iteration 14
+2016-08-24 11:21:10,943 DEBUG: View 0 : 0.564102564103
+2016-08-24 11:21:10,952 DEBUG: View 1 : 0.621794871795
+2016-08-24 11:21:11,038 DEBUG: View 2 : 0.557692307692
+2016-08-24 11:21:11,045 DEBUG: View 3 : 0.602564102564
+2016-08-24 11:21:11,122 DEBUG: Best view : Clinic_
+2016-08-24 11:21:11,968 DEBUG: Start: Iteration 15
+2016-08-24 11:21:11,985 DEBUG: View 0 : 0.551282051282
+2016-08-24 11:21:11,992 DEBUG: View 1 : 0.634615384615
+2016-08-24 11:21:12,074 DEBUG: View 2 : 0.467948717949
+2016-08-24 11:21:12,082 DEBUG: View 3 : 0.596153846154
+2016-08-24 11:21:12,161 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:13,061 DEBUG: Start: Iteration 16
+2016-08-24 11:21:13,078 DEBUG: View 0 : 0.679487179487
+2016-08-24 11:21:13,086 DEBUG: View 1 : 0.403846153846
+2016-08-24 11:21:13,172 DEBUG: View 2 : 0.628205128205
+2016-08-24 11:21:13,180 DEBUG: View 3 : 0.544871794872
+2016-08-24 11:21:13,262 DEBUG: Best view : Methyl_
+2016-08-24 11:21:14,233 DEBUG: Start: Iteration 17
+2016-08-24 11:21:14,250 DEBUG: View 0 : 0.621794871795
+2016-08-24 11:21:14,258 DEBUG: View 1 : 0.673076923077
+2016-08-24 11:21:14,340 DEBUG: View 2 : 0.602564102564
+2016-08-24 11:21:14,348 DEBUG: View 3 : 0.608974358974
+2016-08-24 11:21:14,431 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:15,447 DEBUG: Start: Iteration 18
+2016-08-24 11:21:15,464 DEBUG: View 0 : 0.557692307692
+2016-08-24 11:21:15,471 DEBUG: View 1 : 0.641025641026
+2016-08-24 11:21:15,573 DEBUG: View 2 : 0.551282051282
+2016-08-24 11:21:15,582 DEBUG: View 3 : 0.608974358974
+2016-08-24 11:21:15,675 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:16,754 DEBUG: Start: Iteration 19
+2016-08-24 11:21:16,770 DEBUG: View 0 : 0.461538461538
+2016-08-24 11:21:16,777 DEBUG: View 1 : 0.647435897436
+2016-08-24 11:21:16,859 DEBUG: View 2 : 0.538461538462
+2016-08-24 11:21:16,866 DEBUG: View 3 : 0.551282051282
+2016-08-24 11:21:16,954 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:18,089 DEBUG: Start: Iteration 20
+2016-08-24 11:21:18,105 DEBUG: View 0 : 0.551282051282
+2016-08-24 11:21:18,113 DEBUG: View 1 : 0.371794871795
+2016-08-24 11:21:18,193 DEBUG: View 2 : 0.666666666667
+2016-08-24 11:21:18,201 DEBUG: View 3 : 0.596153846154
+2016-08-24 11:21:18,291 DEBUG: Best view : RANSeq_
+2016-08-24 11:21:19,495 DEBUG: Start: Iteration 21
+2016-08-24 11:21:19,512 DEBUG: View 0 : 0.365384615385
+2016-08-24 11:21:19,520 DEBUG: View 1 : 0.576923076923
+2016-08-24 11:21:19,609 DEBUG: View 2 : 0.557692307692
+2016-08-24 11:21:19,616 DEBUG: View 3 : 0.608974358974
+2016-08-24 11:21:19,709 DEBUG: Best view : Clinic_
+2016-08-24 11:21:20,977 DEBUG: Start: Iteration 22
+2016-08-24 11:21:20,994 DEBUG: View 0 : 0.660256410256
+2016-08-24 11:21:21,002 DEBUG: View 1 : 0.782051282051
+2016-08-24 11:21:21,090 DEBUG: View 2 : 0.602564102564
+2016-08-24 11:21:21,097 DEBUG: View 3 : 0.538461538462
+2016-08-24 11:21:21,191 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:22,497 DEBUG: Start: Iteration 23
+2016-08-24 11:21:22,514 DEBUG: View 0 : 0.474358974359
+2016-08-24 11:21:22,522 DEBUG: View 1 : 0.314102564103
+2016-08-24 11:21:22,598 DEBUG: View 2 : 0.50641025641
+2016-08-24 11:21:22,605 DEBUG: View 3 : 0.602564102564
+2016-08-24 11:21:22,701 DEBUG: Best view : Clinic_
+2016-08-24 11:21:24,095 DEBUG: Start: Iteration 24
+2016-08-24 11:21:24,112 DEBUG: View 0 : 0.551282051282
+2016-08-24 11:21:24,120 DEBUG: View 1 : 0.429487179487
+2016-08-24 11:21:24,205 DEBUG: View 2 : 0.621794871795
+2016-08-24 11:21:24,214 DEBUG: View 3 : 0.653846153846
+2016-08-24 11:21:24,312 DEBUG: Best view : Clinic_
+2016-08-24 11:21:25,747 DEBUG: Start: Iteration 25
+2016-08-24 11:21:25,764 DEBUG: View 0 : 0.570512820513
+2016-08-24 11:21:25,772 DEBUG: View 1 : 0.519230769231
+2016-08-24 11:21:25,855 DEBUG: View 2 : 0.621794871795
+2016-08-24 11:21:25,863 DEBUG: View 3 : 0.596153846154
+2016-08-24 11:21:25,963 DEBUG: Best view : Clinic_
+2016-08-24 11:21:27,457 DEBUG: Start: Iteration 26
+2016-08-24 11:21:27,473 DEBUG: View 0 : 0.557692307692
+2016-08-24 11:21:27,481 DEBUG: View 1 : 0.378205128205
+2016-08-24 11:21:27,564 DEBUG: View 2 : 0.576923076923
+2016-08-24 11:21:27,572 DEBUG: View 3 : 0.692307692308
+2016-08-24 11:21:27,675 DEBUG: Best view : Clinic_
+2016-08-24 11:21:29,220 DEBUG: Start: Iteration 27
+2016-08-24 11:21:29,236 DEBUG: View 0 : 0.474358974359
+2016-08-24 11:21:29,244 DEBUG: View 1 : 0.564102564103
+2016-08-24 11:21:29,327 DEBUG: View 2 : 0.570512820513
+2016-08-24 11:21:29,335 DEBUG: View 3 : 0.564102564103
+2016-08-24 11:21:29,440 DEBUG: Best view : Clinic_
+2016-08-24 11:21:31,047 DEBUG: Start: Iteration 28
+2016-08-24 11:21:31,063 DEBUG: View 0 : 0.429487179487
+2016-08-24 11:21:31,071 DEBUG: View 1 : 0.75
+2016-08-24 11:21:31,155 DEBUG: View 2 : 0.564102564103
+2016-08-24 11:21:31,162 DEBUG: View 3 : 0.602564102564
+2016-08-24 11:21:31,270 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:32,931 DEBUG: Start: Iteration 29
+2016-08-24 11:21:32,947 DEBUG: View 0 : 0.544871794872
+2016-08-24 11:21:32,955 DEBUG: View 1 : 0.576923076923
+2016-08-24 11:21:33,041 DEBUG: View 2 : 0.49358974359
+2016-08-24 11:21:33,049 DEBUG: View 3 : 0.5
+2016-08-24 11:21:33,161 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:34,897 DEBUG: Start: Iteration 30
+2016-08-24 11:21:34,914 DEBUG: View 0 : 0.615384615385
+2016-08-24 11:21:34,921 DEBUG: View 1 : 0.621794871795
+2016-08-24 11:21:35,009 DEBUG: View 2 : 0.448717948718
+2016-08-24 11:21:35,016 DEBUG: View 3 : 0.538461538462
+2016-08-24 11:21:35,130 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:36,907 DEBUG: Start: Iteration 31
+2016-08-24 11:21:36,923 DEBUG: View 0 : 0.480769230769
+2016-08-24 11:21:36,931 DEBUG: View 1 : 0.570512820513
+2016-08-24 11:21:37,013 DEBUG: View 2 : 0.589743589744
+2016-08-24 11:21:37,020 DEBUG: View 3 : 0.608974358974
+2016-08-24 11:21:37,135 DEBUG: Best view : Clinic_
+2016-08-24 11:21:38,986 DEBUG: Start: Iteration 32
+2016-08-24 11:21:39,003 DEBUG: View 0 : 0.410256410256
+2016-08-24 11:21:39,011 DEBUG: View 1 : 0.666666666667
+2016-08-24 11:21:39,094 DEBUG: View 2 : 0.570512820513
+2016-08-24 11:21:39,103 DEBUG: View 3 : 0.519230769231
+2016-08-24 11:21:39,220 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:41,118 DEBUG: Start: Iteration 33
+2016-08-24 11:21:41,134 DEBUG: View 0 : 0.339743589744
+2016-08-24 11:21:41,142 DEBUG: View 1 : 0.525641025641
+2016-08-24 11:21:41,217 DEBUG: View 2 : 0.551282051282
+2016-08-24 11:21:41,225 DEBUG: View 3 : 0.442307692308
+2016-08-24 11:21:41,344 DEBUG: Best view : RANSeq_
+2016-08-24 11:21:43,320 DEBUG: Start: Iteration 34
+2016-08-24 11:21:43,336 DEBUG: View 0 : 0.519230769231
+2016-08-24 11:21:43,344 DEBUG: View 1 : 0.448717948718
+2016-08-24 11:21:43,426 DEBUG: View 2 : 0.628205128205
+2016-08-24 11:21:43,434 DEBUG: View 3 : 0.487179487179
+2016-08-24 11:21:43,554 DEBUG: Best view : RANSeq_
+2016-08-24 11:21:45,593 DEBUG: Start: Iteration 35
+2016-08-24 11:21:45,609 DEBUG: View 0 : 0.487179487179
+2016-08-24 11:21:45,617 DEBUG: View 1 : 0.673076923077
+2016-08-24 11:21:45,699 DEBUG: View 2 : 0.583333333333
+2016-08-24 11:21:45,707 DEBUG: View 3 : 0.576923076923
+2016-08-24 11:21:45,831 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:47,920 DEBUG: Start: Iteration 36
+2016-08-24 11:21:47,936 DEBUG: View 0 : 0.416666666667
+2016-08-24 11:21:47,944 DEBUG: View 1 : 0.435897435897
+2016-08-24 11:21:48,029 DEBUG: View 2 : 0.570512820513
+2016-08-24 11:21:48,037 DEBUG: View 3 : 0.679487179487
+2016-08-24 11:21:48,164 DEBUG: Best view : Clinic_
+2016-08-24 11:21:50,319 DEBUG: Start: Iteration 37
+2016-08-24 11:21:50,335 DEBUG: View 0 : 0.525641025641
+2016-08-24 11:21:50,343 DEBUG: View 1 : 0.602564102564
+2016-08-24 11:21:50,429 DEBUG: View 2 : 0.615384615385
+2016-08-24 11:21:50,437 DEBUG: View 3 : 0.608974358974
+2016-08-24 11:21:50,563 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:52,782 DEBUG: Start: Iteration 38
+2016-08-24 11:21:52,799 DEBUG: View 0 : 0.480769230769
+2016-08-24 11:21:52,807 DEBUG: View 1 : 0.576923076923
+2016-08-24 11:21:52,889 DEBUG: View 2 : 0.570512820513
+2016-08-24 11:21:52,896 DEBUG: View 3 : 0.532051282051
+2016-08-24 11:21:53,028 DEBUG: Best view : MiRNA__
+2016-08-24 11:21:55,289 DEBUG: Start: Iteration 39
+2016-08-24 11:21:55,306 DEBUG: View 0 : 0.410256410256
+2016-08-24 11:21:55,314 DEBUG: View 1 : 0.557692307692
+2016-08-24 11:21:55,401 DEBUG: View 2 : 0.570512820513
+2016-08-24 11:21:55,409 DEBUG: View 3 : 0.532051282051
+2016-08-24 11:21:55,541 DEBUG: Best view : RANSeq_
+2016-08-24 11:21:57,876 DEBUG: Start: Iteration 40
+2016-08-24 11:21:57,892 DEBUG: View 0 : 0.423076923077
+2016-08-24 11:21:57,900 DEBUG: View 1 : 0.50641025641
+2016-08-24 11:21:57,985 DEBUG: View 2 : 0.602564102564
+2016-08-24 11:21:57,993 DEBUG: View 3 : 0.5
+2016-08-24 11:21:58,126 DEBUG: Best view : RANSeq_
+2016-08-24 11:22:00,528 DEBUG: Start: Iteration 41
+2016-08-24 11:22:00,545 DEBUG: View 0 : 0.711538461538
+2016-08-24 11:22:00,552 DEBUG: View 1 : 0.641025641026
+2016-08-24 11:22:00,634 DEBUG: View 2 : 0.634615384615
+2016-08-24 11:22:00,642 DEBUG: View 3 : 0.621794871795
+2016-08-24 11:22:00,779 DEBUG: Best view : Methyl_
+2016-08-24 11:22:03,239 DEBUG: Start: Iteration 42
+2016-08-24 11:22:03,255 DEBUG: View 0 : 0.480769230769
+2016-08-24 11:22:03,263 DEBUG: View 1 : 0.551282051282
+2016-08-24 11:22:03,353 DEBUG: View 2 : 0.544871794872
+2016-08-24 11:22:03,360 DEBUG: View 3 : 0.666666666667
+2016-08-24 11:22:03,500 DEBUG: Best view : Clinic_
+2016-08-24 11:22:06,015 DEBUG: Start: Iteration 43
+2016-08-24 11:22:06,031 DEBUG: View 0 : 0.551282051282
+2016-08-24 11:22:06,039 DEBUG: View 1 : 0.564102564103
+2016-08-24 11:22:06,124 DEBUG: View 2 : 0.551282051282
+2016-08-24 11:22:06,132 DEBUG: View 3 : 0.641025641026
+2016-08-24 11:22:06,271 DEBUG: Best view : Clinic_
+2016-08-24 11:22:08,861 DEBUG: Start: Iteration 44
+2016-08-24 11:22:08,878 DEBUG: View 0 : 0.416666666667
+2016-08-24 11:22:08,885 DEBUG: View 1 : 0.647435897436
+2016-08-24 11:22:08,969 DEBUG: View 2 : 0.628205128205
+2016-08-24 11:22:08,976 DEBUG: View 3 : 0.660256410256
+2016-08-24 11:22:09,120 DEBUG: Best view : Clinic_
+2016-08-24 11:22:11,751 DEBUG: Start: Iteration 45
+2016-08-24 11:22:11,767 DEBUG: View 0 : 0.557692307692
+2016-08-24 11:22:11,775 DEBUG: View 1 : 0.365384615385
+2016-08-24 11:22:11,864 DEBUG: View 2 : 0.583333333333
+2016-08-24 11:22:11,871 DEBUG: View 3 : 0.525641025641
+2016-08-24 11:22:12,016 DEBUG: Best view : RANSeq_
+2016-08-24 11:22:14,742 DEBUG: Start: Iteration 46
+2016-08-24 11:22:14,758 DEBUG: View 0 : 0.570512820513
+2016-08-24 11:22:14,766 DEBUG: View 1 : 0.602564102564
+2016-08-24 11:22:14,854 DEBUG: View 2 : 0.564102564103
+2016-08-24 11:22:14,861 DEBUG: View 3 : 0.628205128205
+2016-08-24 11:22:15,009 DEBUG: Best view : Clinic_
+2016-08-24 11:22:17,777 DEBUG: Start: Iteration 47
+2016-08-24 11:22:17,794 DEBUG: View 0 : 0.480769230769
+2016-08-24 11:22:17,802 DEBUG: View 1 : 0.320512820513
+2016-08-24 11:22:17,884 DEBUG: View 2 : 0.49358974359
+2016-08-24 11:22:17,891 DEBUG: View 3 : 0.461538461538
+2016-08-24 11:22:17,891 WARNING: WARNING: All bad for iteration 46
+2016-08-24 11:22:18,042 DEBUG: Best view : RANSeq_
+2016-08-24 11:22:20,878 DEBUG: Start: Iteration 48
+2016-08-24 11:22:20,895 DEBUG: View 0 : 0.544871794872
+2016-08-24 11:22:20,902 DEBUG: View 1 : 0.397435897436
+2016-08-24 11:22:20,985 DEBUG: View 2 : 0.589743589744
+2016-08-24 11:22:20,993 DEBUG: View 3 : 0.621794871795
+2016-08-24 11:22:21,144 DEBUG: Best view : Clinic_
+2016-08-24 11:22:24,050 DEBUG: Start: Iteration 49
+2016-08-24 11:22:24,066 DEBUG: View 0 : 0.589743589744
+2016-08-24 11:22:24,074 DEBUG: View 1 : 0.333333333333
+2016-08-24 11:22:24,159 DEBUG: View 2 : 0.544871794872
+2016-08-24 11:22:24,166 DEBUG: View 3 : 0.512820512821
+2016-08-24 11:22:24,321 DEBUG: Best view : Methyl_
+2016-08-24 11:22:27,282 DEBUG: Start: Iteration 50
+2016-08-24 11:22:27,298 DEBUG: View 0 : 0.50641025641
+2016-08-24 11:22:27,306 DEBUG: View 1 : 0.576923076923
+2016-08-24 11:22:27,388 DEBUG: View 2 : 0.583333333333
+2016-08-24 11:22:27,396 DEBUG: View 3 : 0.589743589744
+2016-08-24 11:22:27,554 DEBUG: Best view : Clinic_
+2016-08-24 11:22:30,568 DEBUG: Start: Iteration 51
+2016-08-24 11:22:30,585 DEBUG: View 0 : 0.429487179487
+2016-08-24 11:22:30,592 DEBUG: View 1 : 0.423076923077
+2016-08-24 11:22:30,681 DEBUG: View 2 : 0.596153846154
+2016-08-24 11:22:30,688 DEBUG: View 3 : 0.583333333333
+2016-08-24 11:22:30,847 DEBUG: Best view : RANSeq_
+2016-08-24 11:22:33,933 DEBUG: Start: Iteration 52
+2016-08-24 11:22:33,950 DEBUG: View 0 : 0.570512820513
+2016-08-24 11:22:33,957 DEBUG: View 1 : 0.653846153846
+2016-08-24 11:22:34,040 DEBUG: View 2 : 0.615384615385
+2016-08-24 11:22:34,048 DEBUG: View 3 : 0.49358974359
+2016-08-24 11:22:34,211 DEBUG: Best view : MiRNA__
+2016-08-24 11:22:37,353 DEBUG: Start: Iteration 53
+2016-08-24 11:22:37,370 DEBUG: View 0 : 0.538461538462
+2016-08-24 11:22:37,378 DEBUG: View 1 : 0.628205128205
+2016-08-24 11:22:37,465 DEBUG: View 2 : 0.653846153846
+2016-08-24 11:22:37,473 DEBUG: View 3 : 0.525641025641
+2016-08-24 11:22:37,635 DEBUG: Best view : RANSeq_
+2016-08-24 11:22:40,837 DEBUG: Start: Iteration 54
+2016-08-24 11:22:40,854 DEBUG: View 0 : 0.589743589744
+2016-08-24 11:22:40,862 DEBUG: View 1 : 0.448717948718
+2016-08-24 11:22:40,943 DEBUG: View 2 : 0.50641025641
+2016-08-24 11:22:40,951 DEBUG: View 3 : 0.583333333333
+2016-08-24 11:22:41,117 DEBUG: Best view : Clinic_
+2016-08-24 11:22:44,382 DEBUG: Start: Iteration 55
+2016-08-24 11:22:44,398 DEBUG: View 0 : 0.634615384615
+2016-08-24 11:22:44,406 DEBUG: View 1 : 0.551282051282
+2016-08-24 11:22:44,485 DEBUG: View 2 : 0.564102564103
+2016-08-24 11:22:44,493 DEBUG: View 3 : 0.448717948718
+2016-08-24 11:22:44,658 DEBUG: Best view : Methyl_
+2016-08-24 11:22:47,984 DEBUG: Start: Iteration 56
+2016-08-24 11:22:48,000 DEBUG: View 0 : 0.544871794872
+2016-08-24 11:22:48,008 DEBUG: View 1 : 0.666666666667
+2016-08-24 11:22:48,091 DEBUG: View 2 : 0.525641025641
+2016-08-24 11:22:48,099 DEBUG: View 3 : 0.589743589744
+2016-08-24 11:22:48,265 DEBUG: Best view : MiRNA__
+2016-08-24 11:22:51,652 DEBUG: Start: Iteration 57
+2016-08-24 11:22:51,668 DEBUG: View 0 : 0.583333333333
+2016-08-24 11:22:51,676 DEBUG: View 1 : 0.397435897436
+2016-08-24 11:22:51,769 DEBUG: View 2 : 0.519230769231
+2016-08-24 11:22:51,776 DEBUG: View 3 : 0.538461538462
+2016-08-24 11:22:51,945 DEBUG: Best view : Methyl_
+2016-08-24 11:22:55,390 DEBUG: Start: Iteration 58
+2016-08-24 11:22:55,406 DEBUG: View 0 : 0.403846153846
+2016-08-24 11:22:55,414 DEBUG: View 1 : 0.410256410256
+2016-08-24 11:22:55,502 DEBUG: View 2 : 0.551282051282
+2016-08-24 11:22:55,510 DEBUG: View 3 : 0.602564102564
+2016-08-24 11:22:55,681 DEBUG: Best view : Clinic_
+2016-08-24 11:22:59,217 DEBUG: Start: Iteration 59
+2016-08-24 11:22:59,234 DEBUG: View 0 : 0.474358974359
+2016-08-24 11:22:59,242 DEBUG: View 1 : 0.512820512821
+2016-08-24 11:22:59,326 DEBUG: View 2 : 0.608974358974
+2016-08-24 11:22:59,333 DEBUG: View 3 : 0.512820512821
+2016-08-24 11:22:59,507 DEBUG: Best view : RANSeq_
+2016-08-24 11:23:03,084 DEBUG: Start: Iteration 60
+2016-08-24 11:23:03,101 DEBUG: View 0 : 0.397435897436
+2016-08-24 11:23:03,110 DEBUG: View 1 : 0.455128205128
+2016-08-24 11:23:03,196 DEBUG: View 2 : 0.653846153846
+2016-08-24 11:23:03,204 DEBUG: View 3 : 0.570512820513
+2016-08-24 11:23:03,381 DEBUG: Best view : RANSeq_
+2016-08-24 11:23:07,042 DEBUG: Start: Iteration 61
+2016-08-24 11:23:07,059 DEBUG: View 0 : 0.705128205128
+2016-08-24 11:23:07,066 DEBUG: View 1 : 0.442307692308
+2016-08-24 11:23:07,154 DEBUG: View 2 : 0.557692307692
+2016-08-24 11:23:07,162 DEBUG: View 3 : 0.621794871795
+2016-08-24 11:23:07,340 DEBUG: Best view : Methyl_
+2016-08-24 11:23:11,059 DEBUG: Start: Iteration 62
+2016-08-24 11:23:11,075 DEBUG: View 0 : 0.391025641026
+2016-08-24 11:23:11,083 DEBUG: View 1 : 0.666666666667
+2016-08-24 11:23:11,180 DEBUG: View 2 : 0.532051282051
+2016-08-24 11:23:11,188 DEBUG: View 3 : 0.570512820513
+2016-08-24 11:23:11,379 DEBUG: Best view : MiRNA__
+2016-08-24 11:23:15,148 DEBUG: Start: Iteration 63
+2016-08-24 11:23:15,164 DEBUG: View 0 : 0.384615384615
+2016-08-24 11:23:15,172 DEBUG: View 1 : 0.532051282051
+2016-08-24 11:23:15,256 DEBUG: View 2 : 0.49358974359
+2016-08-24 11:23:15,263 DEBUG: View 3 : 0.653846153846
+2016-08-24 11:23:15,447 DEBUG: Best view : Clinic_
+2016-08-24 11:23:19,280 DEBUG: Start: Iteration 64
+2016-08-24 11:23:19,296 DEBUG: View 0 : 0.551282051282
+2016-08-24 11:23:19,304 DEBUG: View 1 : 0.679487179487
+2016-08-24 11:23:19,391 DEBUG: View 2 : 0.551282051282
+2016-08-24 11:23:19,398 DEBUG: View 3 : 0.583333333333
+2016-08-24 11:23:19,582 DEBUG: Best view : MiRNA__
+2016-08-24 11:23:23,489 DEBUG: Start: Iteration 65
+2016-08-24 11:23:23,505 DEBUG: View 0 : 0.583333333333
+2016-08-24 11:23:23,513 DEBUG: View 1 : 0.525641025641
+2016-08-24 11:23:23,597 DEBUG: View 2 : 0.602564102564
+2016-08-24 11:23:23,604 DEBUG: View 3 : 0.602564102564
+2016-08-24 11:23:23,795 DEBUG: Best view : Clinic_
+2016-08-24 11:23:27,722 DEBUG: Start: Iteration 66
+2016-08-24 11:23:27,739 DEBUG: View 0 : 0.538461538462
+2016-08-24 11:23:27,746 DEBUG: View 1 : 0.538461538462
+2016-08-24 11:23:27,833 DEBUG: View 2 : 0.525641025641
+2016-08-24 11:23:27,840 DEBUG: View 3 : 0.5
+2016-08-24 11:23:28,046 DEBUG: Best view : MiRNA__
+2016-08-24 11:23:32,077 DEBUG: Start: Iteration 67
+2016-08-24 11:23:32,094 DEBUG: View 0 : 0.403846153846
+2016-08-24 11:23:32,102 DEBUG: View 1 : 0.49358974359
+2016-08-24 11:23:32,186 DEBUG: View 2 : 0.448717948718
+2016-08-24 11:23:32,194 DEBUG: View 3 : 0.615384615385
+2016-08-24 11:23:32,386 DEBUG: Best view : Clinic_
+2016-08-24 11:23:36,441 DEBUG: Start: Iteration 68
+2016-08-24 11:23:36,458 DEBUG: View 0 : 0.660256410256
+2016-08-24 11:23:36,466 DEBUG: View 1 : 0.339743589744
+2016-08-24 11:23:36,553 DEBUG: View 2 : 0.621794871795
+2016-08-24 11:23:36,560 DEBUG: View 3 : 0.685897435897
+2016-08-24 11:23:36,754 DEBUG: Best view : Clinic_
+2016-08-24 11:23:40,869 DEBUG: Start: Iteration 69
+2016-08-24 11:23:40,885 DEBUG: View 0 : 0.602564102564
+2016-08-24 11:23:40,893 DEBUG: View 1 : 0.442307692308
+2016-08-24 11:23:40,971 DEBUG: View 2 : 0.570512820513
+2016-08-24 11:23:40,979 DEBUG: View 3 : 0.596153846154
+2016-08-24 11:23:41,173 DEBUG: Best view : Clinic_
+2016-08-24 11:23:45,325 DEBUG: Start: Iteration 70
+2016-08-24 11:23:45,341 DEBUG: View 0 : 0.435897435897
+2016-08-24 11:23:45,349 DEBUG: View 1 : 0.525641025641
+2016-08-24 11:23:45,436 DEBUG: View 2 : 0.525641025641
+2016-08-24 11:23:45,443 DEBUG: View 3 : 0.621794871795
+2016-08-24 11:23:45,641 DEBUG: Best view : Clinic_
+2016-08-24 11:23:49,870 DEBUG: Start: Iteration 71
+2016-08-24 11:23:49,887 DEBUG: View 0 : 0.551282051282
+2016-08-24 11:23:49,895 DEBUG: View 1 : 0.621794871795
+2016-08-24 11:23:49,978 DEBUG: View 2 : 0.538461538462
+2016-08-24 11:23:49,986 DEBUG: View 3 : 0.634615384615
+2016-08-24 11:23:50,196 DEBUG: Best view : Clinic_
+2016-08-24 11:23:54,507 DEBUG: Start: Iteration 72
+2016-08-24 11:23:54,523 DEBUG: View 0 : 0.512820512821
+2016-08-24 11:23:54,531 DEBUG: View 1 : 0.455128205128
+2016-08-24 11:23:54,611 DEBUG: View 2 : 0.532051282051
+2016-08-24 11:23:54,619 DEBUG: View 3 : 0.551282051282
+2016-08-24 11:23:54,820 DEBUG: Best view : Clinic_
+2016-08-24 11:23:59,164 DEBUG: Start: Iteration 73
+2016-08-24 11:23:59,180 DEBUG: View 0 : 0.416666666667
+2016-08-24 11:23:59,188 DEBUG: View 1 : 0.474358974359
+2016-08-24 11:23:59,273 DEBUG: View 2 : 0.5
+2016-08-24 11:23:59,280 DEBUG: View 3 : 0.525641025641
+2016-08-24 11:23:59,485 DEBUG: Best view : Clinic_
+2016-08-24 11:24:03,917 DEBUG: Start: Iteration 74
+2016-08-24 11:24:03,934 DEBUG: View 0 : 0.564102564103
+2016-08-24 11:24:03,942 DEBUG: View 1 : 0.698717948718
+2016-08-24 11:24:04,029 DEBUG: View 2 : 0.564102564103
+2016-08-24 11:24:04,037 DEBUG: View 3 : 0.5
+2016-08-24 11:24:04,257 DEBUG: Best view : MiRNA__
+2016-08-24 11:24:08,879 DEBUG: Start: Iteration 75
+2016-08-24 11:24:08,896 DEBUG: View 0 : 0.647435897436
+2016-08-24 11:24:08,904 DEBUG: View 1 : 0.397435897436
+2016-08-24 11:24:08,992 DEBUG: View 2 : 0.589743589744
+2016-08-24 11:24:09,000 DEBUG: View 3 : 0.583333333333
+2016-08-24 11:24:09,218 DEBUG: Best view : Methyl_
+2016-08-24 11:24:13,834 DEBUG: Start: Iteration 76
+2016-08-24 11:24:13,851 DEBUG: View 0 : 0.564102564103
+2016-08-24 11:24:13,859 DEBUG: View 1 : 0.75
+2016-08-24 11:24:13,949 DEBUG: View 2 : 0.551282051282
+2016-08-24 11:24:13,957 DEBUG: View 3 : 0.602564102564
+2016-08-24 11:24:14,181 DEBUG: Best view : MiRNA__
+2016-08-24 11:24:18,999 DEBUG: Start: Iteration 77
+2016-08-24 11:24:19,016 DEBUG: View 0 : 0.641025641026
+2016-08-24 11:24:19,023 DEBUG: View 1 : 0.673076923077
+2016-08-24 11:24:19,111 DEBUG: View 2 : 0.519230769231
+2016-08-24 11:24:19,119 DEBUG: View 3 : 0.544871794872
+2016-08-24 11:24:19,332 DEBUG: Best view : MiRNA__
+2016-08-24 11:24:24,134 DEBUG: Start: Iteration 78
+2016-08-24 11:24:24,151 DEBUG: View 0 : 0.397435897436
+2016-08-24 11:24:24,159 DEBUG: View 1 : 0.282051282051
+2016-08-24 11:24:24,256 DEBUG: View 2 : 0.589743589744
+2016-08-24 11:24:24,264 DEBUG: View 3 : 0.653846153846
+2016-08-24 11:24:24,481 DEBUG: Best view : Clinic_
+2016-08-24 11:24:29,491 DEBUG: Start: Iteration 79
+2016-08-24 11:24:29,508 DEBUG: View 0 : 0.487179487179
+2016-08-24 11:24:29,516 DEBUG: View 1 : 0.564102564103
+2016-08-24 11:24:29,610 DEBUG: View 2 : 0.512820512821
+2016-08-24 11:24:29,618 DEBUG: View 3 : 0.621794871795
+2016-08-24 11:24:29,840 DEBUG: Best view : Clinic_
+2016-08-24 11:24:34,815 DEBUG: Start: Iteration 80
+2016-08-24 11:24:34,835 DEBUG: View 0 : 0.435897435897
+2016-08-24 11:24:34,844 DEBUG: View 1 : 0.711538461538
+2016-08-24 11:24:34,954 DEBUG: View 2 : 0.544871794872
+2016-08-24 11:24:34,964 DEBUG: View 3 : 0.551282051282
+2016-08-24 11:24:35,235 DEBUG: Best view : MiRNA__
+2016-08-24 11:24:40,456 DEBUG: Start: Iteration 81
+2016-08-24 11:24:40,474 DEBUG: View 0 : 0.576923076923
+2016-08-24 11:24:40,483 DEBUG: View 1 : 0.346153846154
+2016-08-24 11:24:40,571 DEBUG: View 2 : 0.557692307692
+2016-08-24 11:24:40,579 DEBUG: View 3 : 0.608974358974
+2016-08-24 11:24:40,814 DEBUG: Best view : Clinic_
+2016-08-24 11:24:45,879 DEBUG: Start: Iteration 82
+2016-08-24 11:24:45,896 DEBUG: View 0 : 0.474358974359
+2016-08-24 11:24:45,905 DEBUG: View 1 : 0.50641025641
+2016-08-24 11:24:45,988 DEBUG: View 2 : 0.653846153846
+2016-08-24 11:24:45,996 DEBUG: View 3 : 0.641025641026
+2016-08-24 11:24:46,222 DEBUG: Best view : RANSeq_
+2016-08-24 11:24:51,238 DEBUG: Start: Iteration 83
+2016-08-24 11:24:51,255 DEBUG: View 0 : 0.544871794872
+2016-08-24 11:24:51,264 DEBUG: View 1 : 0.692307692308
+2016-08-24 11:24:51,355 DEBUG: View 2 : 0.576923076923
+2016-08-24 11:24:51,363 DEBUG: View 3 : 0.602564102564
+2016-08-24 11:24:51,591 DEBUG: Best view : MiRNA__
+2016-08-24 11:24:56,849 DEBUG: Start: Iteration 84
+2016-08-24 11:24:56,866 DEBUG: View 0 : 0.602564102564
+2016-08-24 11:24:56,873 DEBUG: View 1 : 0.320512820513
+2016-08-24 11:24:56,962 DEBUG: View 2 : 0.647435897436
+2016-08-24 11:24:56,970 DEBUG: View 3 : 0.576923076923
+2016-08-24 11:24:57,226 DEBUG: Best view : RANSeq_
+2016-08-24 11:25:02,537 DEBUG: Start: Iteration 85
+2016-08-24 11:25:02,553 DEBUG: View 0 : 0.525641025641
+2016-08-24 11:25:02,561 DEBUG: View 1 : 0.615384615385
+2016-08-24 11:25:02,650 DEBUG: View 2 : 0.519230769231
+2016-08-24 11:25:02,657 DEBUG: View 3 : 0.660256410256
+2016-08-24 11:25:02,885 DEBUG: Best view : Clinic_
+2016-08-24 11:25:08,095 DEBUG: Start: Iteration 86
+2016-08-24 11:25:08,112 DEBUG: View 0 : 0.647435897436
+2016-08-24 11:25:08,120 DEBUG: View 1 : 0.525641025641
+2016-08-24 11:25:08,210 DEBUG: View 2 : 0.557692307692
+2016-08-24 11:25:08,218 DEBUG: View 3 : 0.641025641026
+2016-08-24 11:25:08,454 DEBUG: Best view : Clinic_
+2016-08-24 11:25:13,805 DEBUG: Start: Iteration 87
+2016-08-24 11:25:13,829 DEBUG: View 0 : 0.480769230769
+2016-08-24 11:25:13,844 DEBUG: View 1 : 0.628205128205
+2016-08-24 11:25:13,984 DEBUG: View 2 : 0.564102564103
+2016-08-24 11:25:13,998 DEBUG: View 3 : 0.49358974359
+2016-08-24 11:25:14,388 DEBUG: Best view : MiRNA__
+2016-08-24 11:25:19,842 DEBUG: Start: Iteration 88
+2016-08-24 11:25:19,859 DEBUG: View 0 : 0.583333333333
+2016-08-24 11:25:19,867 DEBUG: View 1 : 0.480769230769
+2016-08-24 11:25:19,951 DEBUG: View 2 : 0.551282051282
+2016-08-24 11:25:19,959 DEBUG: View 3 : 0.628205128205
+2016-08-24 11:25:20,266 DEBUG: Best view : Clinic_
+2016-08-24 11:25:25,733 DEBUG: Start: Iteration 89
+2016-08-24 11:25:25,749 DEBUG: View 0 : 0.544871794872
+2016-08-24 11:25:25,757 DEBUG: View 1 : 0.634615384615
+2016-08-24 11:25:25,843 DEBUG: View 2 : 0.50641025641
+2016-08-24 11:25:25,851 DEBUG: View 3 : 0.49358974359
+2016-08-24 11:25:26,091 DEBUG: Best view : MiRNA__
+2016-08-24 11:25:31,511 DEBUG: Start: Iteration 90
+2016-08-24 11:25:31,527 DEBUG: View 0 : 0.615384615385
+2016-08-24 11:25:31,535 DEBUG: View 1 : 0.685897435897
+2016-08-24 11:25:31,618 DEBUG: View 2 : 0.519230769231
+2016-08-24 11:25:31,626 DEBUG: View 3 : 0.564102564103
+2016-08-24 11:25:31,867 DEBUG: Best view : MiRNA__
+2016-08-24 11:25:37,241 DEBUG: Start: Iteration 91
+2016-08-24 11:25:37,257 DEBUG: View 0 : 0.50641025641
+2016-08-24 11:25:37,265 DEBUG: View 1 : 0.692307692308
+2016-08-24 11:25:37,352 DEBUG: View 2 : 0.557692307692
+2016-08-24 11:25:37,360 DEBUG: View 3 : 0.538461538462
+2016-08-24 11:25:37,600 DEBUG: Best view : MiRNA__
+2016-08-24 11:25:43,043 DEBUG: Start: Iteration 92
+2016-08-24 11:25:43,059 DEBUG: View 0 : 0.544871794872
+2016-08-24 11:25:43,067 DEBUG: View 1 : 0.474358974359
+2016-08-24 11:25:43,154 DEBUG: View 2 : 0.615384615385
+2016-08-24 11:25:43,162 DEBUG: View 3 : 0.634615384615
+2016-08-24 11:25:43,406 DEBUG: Best view : Clinic_
+2016-08-24 11:25:48,939 DEBUG: Start: Iteration 93
+2016-08-24 11:25:48,956 DEBUG: View 0 : 0.538461538462
+2016-08-24 11:25:48,964 DEBUG: View 1 : 0.589743589744
+2016-08-24 11:25:49,051 DEBUG: View 2 : 0.621794871795
+2016-08-24 11:25:49,058 DEBUG: View 3 : 0.570512820513
+2016-08-24 11:25:49,305 DEBUG: Best view : RANSeq_
+2016-08-24 11:25:54,890 DEBUG: Start: Iteration 94
+2016-08-24 11:25:54,907 DEBUG: View 0 : 0.435897435897
+2016-08-24 11:25:54,914 DEBUG: View 1 : 0.589743589744
+2016-08-24 11:25:54,997 DEBUG: View 2 : 0.5
+2016-08-24 11:25:55,004 DEBUG: View 3 : 0.608974358974
+2016-08-24 11:25:55,251 DEBUG: Best view : Clinic_
+2016-08-24 11:26:01,345 DEBUG: Start: Iteration 95
+2016-08-24 11:26:01,363 DEBUG: View 0 : 0.570512820513
+2016-08-24 11:26:01,371 DEBUG: View 1 : 0.448717948718
+2016-08-24 11:26:01,460 DEBUG: View 2 : 0.564102564103
+2016-08-24 11:26:01,468 DEBUG: View 3 : 0.544871794872
+2016-08-24 11:26:01,722 DEBUG: Best view : Clinic_
+2016-08-24 11:26:07,817 DEBUG: Start: Iteration 96
+2016-08-24 11:26:07,833 DEBUG: View 0 : 0.602564102564
+2016-08-24 11:26:07,841 DEBUG: View 1 : 0.410256410256
+2016-08-24 11:26:07,923 DEBUG: View 2 : 0.641025641026
+2016-08-24 11:26:07,931 DEBUG: View 3 : 0.653846153846
+2016-08-24 11:26:08,195 DEBUG: Best view : Clinic_
+2016-08-24 11:26:14,008 DEBUG: Start: Iteration 97
+2016-08-24 11:26:14,024 DEBUG: View 0 : 0.596153846154
+2016-08-24 11:26:14,032 DEBUG: View 1 : 0.384615384615
+2016-08-24 11:26:14,119 DEBUG: View 2 : 0.519230769231
+2016-08-24 11:26:14,126 DEBUG: View 3 : 0.621794871795
+2016-08-24 11:26:14,382 DEBUG: Best view : Clinic_
+2016-08-24 11:26:20,301 DEBUG: Start: Iteration 98
+2016-08-24 11:26:20,318 DEBUG: View 0 : 0.621794871795
+2016-08-24 11:26:20,326 DEBUG: View 1 : 0.391025641026
+2016-08-24 11:26:20,418 DEBUG: View 2 : 0.596153846154
+2016-08-24 11:26:20,426 DEBUG: View 3 : 0.653846153846
+2016-08-24 11:26:20,686 DEBUG: Best view : Clinic_
+2016-08-24 11:26:26,648 DEBUG: Start: Iteration 99
+2016-08-24 11:26:26,664 DEBUG: View 0 : 0.615384615385
+2016-08-24 11:26:26,672 DEBUG: View 1 : 0.391025641026
+2016-08-24 11:26:26,757 DEBUG: View 2 : 0.544871794872
+2016-08-24 11:26:26,765 DEBUG: View 3 : 0.628205128205
+2016-08-24 11:26:27,027 DEBUG: Best view : Clinic_
+2016-08-24 11:26:33,016 DEBUG: Start: Iteration 100
+2016-08-24 11:26:33,032 DEBUG: View 0 : 0.628205128205
+2016-08-24 11:26:33,040 DEBUG: View 1 : 0.602564102564
+2016-08-24 11:26:33,124 DEBUG: View 2 : 0.480769230769
+2016-08-24 11:26:33,132 DEBUG: View 3 : 0.647435897436
+2016-08-24 11:26:33,395 DEBUG: Best view : Clinic_
+2016-08-24 11:26:39,429 DEBUG: Start: Iteration 101
+2016-08-24 11:26:39,446 DEBUG: View 0 : 0.717948717949
+2016-08-24 11:26:39,453 DEBUG: View 1 : 0.634615384615
+2016-08-24 11:26:39,539 DEBUG: View 2 : 0.589743589744
+2016-08-24 11:26:39,546 DEBUG: View 3 : 0.628205128205
+2016-08-24 11:26:39,811 DEBUG: Best view : Methyl_
+2016-08-24 11:26:46,118 DEBUG: Start: Iteration 102
+2016-08-24 11:26:46,139 DEBUG: View 0 : 0.615384615385
+2016-08-24 11:26:46,148 DEBUG: View 1 : 0.628205128205
+2016-08-24 11:26:46,237 DEBUG: View 2 : 0.551282051282
+2016-08-24 11:26:46,244 DEBUG: View 3 : 0.634615384615
+2016-08-24 11:26:46,518 DEBUG: Best view : Clinic_
+2016-08-24 11:26:52,740 INFO: Start: Classification
+2016-08-24 11:27:08,084 INFO: Done: Fold number 2
+2016-08-24 11:27:08,084 INFO: Done: Classification
+2016-08-24 11:27:08,084 INFO: Info: Time for Classification: 742[s]
+2016-08-24 11:27:08,084 INFO: Start: Result Analysis for Mumbo
+2016-08-24 11:27:43,126 INFO: Result for Multiview classification with Mumbo
+
+Average accuracy :
+ -On Train : 79.5150501672
+ -On Test : 79.9180327869
+ -On Validation : 83.9805825243
+
+Dataset info :
+ -Database name : ModifiedMultiOmic
+ -Labels : No, Yes
+ -Views : Methyl, MiRNA, RNASEQ, Clinical
+ -2 folds
+ - Validation set length : 103 for learning rate : 0.7
+
+Classification configuration :
+ -Algorithm used : Mumbo
+ -Iterations : 1000
+ -Weak Classifiers : DecisionTree with depth 1, sub-sampled at 0.02 on Methyl
+ -DecisionTree with depth 1, sub-sampled at 0.02 on MiRNA
+ -DecisionTree with depth 1, sub-sampled at 0.1 on RNASEQ
+ -DecisionTree with depth 2, sub-sampled at 0.1 on Clinical
+
+ Mean average accuracies and stats for each fold :
+ - Fold 0
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0566086956522
+ - Percentage of time chosen : 0.91
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.0601552795031
+ - Percentage of time chosen : 0.031
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.0589378881988
+ - Percentage of time chosen : 0.021
+ - On Clinic_ :
+ - Mean average Accuracy : 0.0603664596273
+ - Percentage of time chosen : 0.038
+ - Fold 1
+ - On Methyl_ :
+ - Mean average Accuracy : 0.0546346153846
+ - Percentage of time chosen : 0.909
+ - On MiRNA__ :
+ - Mean average Accuracy : 0.05475
+ - Percentage of time chosen : 0.028
+ - On RANSeq_ :
+ - Mean average Accuracy : 0.0575833333333
+ - Percentage of time chosen : 0.015
+ - On Clinic_ :
+ - Mean average Accuracy : 0.0597243589744
+ - Percentage of time chosen : 0.048
+
+ For each iteration :
+ - Iteration 1
+ Fold 1
+ Accuracy on train : 62.7329192547
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1356903966
+ Accuracy on test : 72.9508196721
+ - Iteration 2
+ Fold 1
+ Accuracy on train : 72.6708074534
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 60.8974358974
+ Accuracy on test : 53.2786885246
+ Accuracy on validation : 63.1067961165
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 66.7841216754
+ Accuracy on test : 65.5737704918
+ - Iteration 3
+ Fold 1
+ Accuracy on train : 72.6708074534
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 74.7572815534
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 60.8974358974
+ Accuracy on test : 53.2786885246
+ Accuracy on validation : 63.1067961165
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 66.7841216754
+ Accuracy on test : 65.5737704918
+ - Iteration 4
+ Fold 1
+ Accuracy on train : 72.6708074534
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 77.6699029126
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 66.0256410256
+ Accuracy on test : 59.0163934426
+ Accuracy on validation : 65.0485436893
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 69.3482242395
+ Accuracy on test : 70.9016393443
+ - Iteration 5
+ Fold 1
+ Accuracy on train : 71.4285714286
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 75.7281553398
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 69.2307692308
+ Accuracy on test : 63.9344262295
+ Accuracy on validation : 66.0194174757
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 70.3296703297
+ Accuracy on test : 72.5409836066
+ - Iteration 6
+ Fold 1
+ Accuracy on train : 71.4285714286
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 76.6990291262
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 78.8461538462
+ Accuracy on test : 69.6721311475
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 75.1373626374
+ Accuracy on test : 75.4098360656
+ - Iteration 7
+ Fold 1
+ Accuracy on train : 75.7763975155
+ Accuracy on test : 85.2459016393
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 78.2051282051
+ Accuracy on test : 72.131147541
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 76.9907628603
+ Accuracy on test : 78.6885245902
+ - Iteration 8
+ Fold 1
+ Accuracy on train : 75.1552795031
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 77.6699029126
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 78.2051282051
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 81.5533980583
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 76.6802038541
+ Accuracy on test : 76.6393442623
+ - Iteration 9
+ Fold 1
+ Accuracy on train : 73.9130434783
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 76.6990291262
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 79.4871794872
+ Accuracy on test : 72.131147541
+ Accuracy on validation : 83.4951456311
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 76.7001114827
+ Accuracy on test : 76.2295081967
+ - Iteration 10
+ Fold 1
+ Accuracy on train : 75.7763975155
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 75.641025641
+ Accuracy on test : 74.5901639344
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 75.7087115783
+ Accuracy on test : 77.868852459
+ - Iteration 11
+ Fold 1
+ Accuracy on train : 75.7763975155
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 74.7572815534
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 76.9230769231
+ Accuracy on test : 74.5901639344
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 76.3497372193
+ Accuracy on test : 78.6885245902
+ - Iteration 12
+ Fold 1
+ Accuracy on train : 76.397515528
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 76.6990291262
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 75.641025641
+ Accuracy on test : 74.5901639344
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 76.0192705845
+ Accuracy on test : 79.0983606557
+ - Iteration 13
+ Fold 1
+ Accuracy on train : 78.2608695652
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 77.5641025641
+ Accuracy on test : 74.5901639344
+ Accuracy on validation : 80.5825242718
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 77.9124860647
+ Accuracy on test : 77.868852459
+ - Iteration 14
+ Fold 1
+ Accuracy on train : 77.0186335404
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 77.5641025641
+ Accuracy on test : 73.7704918033
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 77.2913680522
+ Accuracy on test : 77.4590163934
+ - Iteration 15
+ Fold 1
+ Accuracy on train : 76.397515528
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 75.641025641
+ Accuracy on test : 77.0491803279
+ Accuracy on validation : 77.6699029126
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 76.0192705845
+ Accuracy on test : 79.0983606557
+ - Iteration 16
+ Fold 1
+ Accuracy on train : 75.7763975155
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 77.6699029126
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 80.1282051282
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 81.5533980583
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 77.9523013219
+ Accuracy on test : 79.5081967213
+ - Iteration 17
+ Fold 1
+ Accuracy on train : 76.397515528
+ Accuracy on test : 85.2459016393
+ Accuracy on validation : 78.640776699
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 78.2051282051
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 77.3013218665
+ Accuracy on test : 82.7868852459
+ - Iteration 18
+ Fold 1
+ Accuracy on train : 77.0186335404
+ Accuracy on test : 84.4262295082
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 79.4871794872
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 81.5533980583
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 78.2529065138
+ Accuracy on test : 82.3770491803
+ - Iteration 19
+ Fold 1
+ Accuracy on train : 78.2608695652
+ Accuracy on test : 84.4262295082
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 81.4102564103
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 82.5242718447
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.8355629877
+ Accuracy on test : 82.7868852459
+ - Iteration 20
+ Fold 1
+ Accuracy on train : 78.2608695652
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 80.1282051282
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 81.5533980583
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 79.1945373467
+ Accuracy on test : 81.1475409836
+ - Iteration 21
+ Fold 1
+ Accuracy on train : 78.2608695652
+ Accuracy on test : 85.2459016393
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 79.4871794872
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 78.8740245262
+ Accuracy on test : 83.1967213115
+ - Iteration 22
+ Fold 1
+ Accuracy on train : 77.6397515528
+ Accuracy on test : 84.4262295082
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 77.5641025641
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 77.6019270584
+ Accuracy on test : 83.606557377
+ - Iteration 23
+ Fold 1
+ Accuracy on train : 79.5031055901
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 82.5242718447
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 75.641025641
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 77.5720656155
+ Accuracy on test : 81.9672131148
+ - Iteration 24
+ Fold 1
+ Accuracy on train : 79.5031055901
+ Accuracy on test : 84.4262295082
+ Accuracy on validation : 82.5242718447
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 77.5641025641
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 81.5533980583
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 78.5336040771
+ Accuracy on test : 82.7868852459
+ - Iteration 25
+ Fold 1
+ Accuracy on train : 78.2608695652
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 80.5825242718
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 79.4871794872
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 83.4951456311
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 78.8740245262
+ Accuracy on test : 81.9672131148
+ - Iteration 26
+ Fold 1
+ Accuracy on train : 78.8819875776
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 82.5242718447
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 80.1282051282
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 81.5533980583
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 79.5050963529
+ Accuracy on test : 82.7868852459
+ - Iteration 27
+ Fold 1
+ Accuracy on train : 79.5031055901
+ Accuracy on test : 84.4262295082
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 82.0512820513
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 83.4951456311
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 80.7771938207
+ Accuracy on test : 82.7868852459
+ - Iteration 28
+ Fold 1
+ Accuracy on train : 78.8819875776
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 85.8974358974
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 86.4077669903
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 82.3897117375
+ Accuracy on test : 81.1475409836
+ - Iteration 29
+ Fold 1
+ Accuracy on train : 77.6397515528
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 83.3333333333
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.4865424431
+ Accuracy on test : 81.9672131148
+ - Iteration 30
+ Fold 1
+ Accuracy on train : 78.2608695652
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 82.0512820513
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 83.4951456311
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.1560758082
+ Accuracy on test : 81.9672131148
+ - Iteration 31
+ Fold 1
+ Accuracy on train : 76.397515528
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 82.0512820513
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 83.4951456311
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 79.2243987896
+ Accuracy on test : 81.1475409836
+ - Iteration 32
+ Fold 1
+ Accuracy on train : 77.0186335404
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 78.640776699
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 82.6923076923
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 83.4951456311
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.8554706163
+ Accuracy on test : 81.9672131148
+ - Iteration 33
+ Fold 1
+ Accuracy on train : 78.2608695652
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 79.6116504854
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 82.0512820513
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 83.4951456311
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 80.1560758082
+ Accuracy on test : 80.3278688525
+ - Iteration 34
+ Fold 1
+ Accuracy on train : 77.0186335404
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 78.640776699
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 83.9743589744
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 85.4368932039
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 80.4964962574
+ Accuracy on test : 80.737704918
+ - Iteration 35
+ Fold 1
+ Accuracy on train : 76.397515528
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 77.6699029126
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 82.0512820513
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 84.4660194175
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 79.2243987896
+ Accuracy on test : 79.9180327869
+ - Iteration 36
+ Fold 1
+ Accuracy on train : 78.2608695652
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 82.0512820513
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 85.4368932039
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 80.1560758082
+ Accuracy on test : 80.737704918
+ - Iteration 37
+ Fold 1
+ Accuracy on train : 79.5031055901
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 81.5533980583
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 83.9743589744
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 87.3786407767
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 81.7387322822
+ Accuracy on test : 81.1475409836
+ - Iteration 38
+ Fold 1
+ Accuracy on train : 78.2608695652
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 82.6923076923
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 86.4077669903
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.4765886288
+ Accuracy on test : 80.737704918
+ - Iteration 39
+ Fold 1
+ Accuracy on train : 77.0186335404
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 81.5533980583
+ Selected View : RANSeq_
+ Fold 2
+ Accuracy on train : 82.6923076923
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 86.4077669903
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 79.8554706163
+ Accuracy on test : 81.5573770492
+ - Iteration 40
+ Fold 1
+ Accuracy on train : 78.8819875776
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 81.5533980583
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 83.3333333333
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 86.4077669903
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 81.1076604555
+ Accuracy on test : 81.1475409836
+ - Iteration 41
+ Fold 1
+ Accuracy on train : 77.6397515528
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 80.5825242718
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 83.3333333333
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 86.4077669903
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 80.4865424431
+ Accuracy on test : 80.737704918
+ - Iteration 42
+ Fold 1
+ Accuracy on train : 77.6397515528
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 81.5533980583
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 83.3333333333
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 85.4368932039
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 80.4865424431
+ Accuracy on test : 80.3278688525
+ - Iteration 43
+ Fold 1
+ Accuracy on train : 80.7453416149
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 82.5242718447
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 85.2564102564
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 87.3786407767
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 83.0008759357
+ Accuracy on test : 80.737704918
+ - Iteration 44
+ Fold 1
+ Accuracy on train : 78.8819875776
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 82.5242718447
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 85.2564102564
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 86.4077669903
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 82.069198917
+ Accuracy on test : 80.737704918
+ - Iteration 45
+ Fold 1
+ Accuracy on train : 78.8819875776
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 81.5533980583
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 84.6153846154
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 86.4077669903
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 81.7486860965
+ Accuracy on test : 81.1475409836
+ - Iteration 46
+ Fold 1
+ Accuracy on train : 77.6397515528
+ Accuracy on test : 83.606557377
+ Accuracy on validation : 79.6116504854
+ Selected View : MiRNA__
+ Fold 2
+ Accuracy on train : 85.8974358974
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 87.3786407767
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 81.7685937251
+ Accuracy on test : 81.9672131148
+ - Iteration 47
+ Fold 1
+ Accuracy on train : 78.2608695652
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 80.5825242718
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 85.8974358974
+ Accuracy on test : 80.3278688525
+ Accuracy on validation : 87.3786407767
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 82.0791527313
+ Accuracy on test : 81.1475409836
+ - Iteration 48
+ Fold 1
+ Accuracy on train : 77.6397515528
+ Accuracy on test : 82.7868852459
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 86.5384615385
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 88.3495145631
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 82.0891065456
+ Accuracy on test : 80.737704918
+ - Iteration 49
+ Fold 1
+ Accuracy on train : 77.6397515528
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 80.5825242718
+ Selected View : Clinic_
+ Fold 2
+ Accuracy on train : 85.8974358974
+ Accuracy on test : 79.5081967213
+ Accuracy on validation : 87.3786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 81.7685937251
+ Accuracy on test : 80.3278688525
+ - Iteration 50
+ Fold 1
+ Accuracy on train : 77.0186335404
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 79.6116504854
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 85.2564102564
+ Accuracy on test : 77.868852459
+ Accuracy on validation : 86.4077669903
+ Selected View : Clinic_
+ - Mean :
+ Accuracy on train : 81.1375218984
+ Accuracy on test : 79.5081967213
+ - Iteration 51
+ Fold 1
+ Accuracy on train : 78.2608695652
+ Accuracy on test : 81.9672131148
+ Accuracy on validation : 81.5533980583
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 83.9743589744
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 86.4077669903
+ Selected View : RANSeq_
+ - Mean :
+ Accuracy on train : 81.1176142698
+ Accuracy on test : 80.3278688525
+ - Iteration 52
+ Fold 1
+ Accuracy on train : 77.0186335404
+ Accuracy on test : 81.1475409836
+ Accuracy on validation : 78.640776699
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 83.9743589744
+ Accuracy on test : 78.6885245902
+ Accuracy on validation : 85.4368932039
+ Selected View : MiRNA__
+ - Mean :
+ Accuracy on train : 80.4964962574
+ Accuracy on test : 79.9180327869
+ - Iteration 53
+ Fold 1
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+ Fold 2
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1356903966
+ Accuracy on test : 72.9508196721
+ - Iteration 994
+ Fold 1
+ Accuracy on train : 62.7329192547
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1356903966
+ Accuracy on test : 72.9508196721
+ - Iteration 995
+ Fold 1
+ Accuracy on train : 62.7329192547
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1356903966
+ Accuracy on test : 72.9508196721
+ - Iteration 996
+ Fold 1
+ Accuracy on train : 62.7329192547
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1356903966
+ Accuracy on test : 72.9508196721
+ - Iteration 997
+ Fold 1
+ Accuracy on train : 62.7329192547
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1356903966
+ Accuracy on test : 72.9508196721
+ - Iteration 998
+ Fold 1
+ Accuracy on train : 62.7329192547
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1356903966
+ Accuracy on test : 72.9508196721
+ - Iteration 999
+ Fold 1
+ Accuracy on train : 62.7329192547
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1356903966
+ Accuracy on test : 72.9508196721
+ - Iteration 1000
+ Fold 1
+ Accuracy on train : 62.7329192547
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ Fold 2
+ Accuracy on train : 61.5384615385
+ Accuracy on test : 72.9508196721
+ Accuracy on validation : 73.786407767
+ Selected View : Methyl_
+ - Mean :
+ Accuracy on train : 62.1356903966
+ Accuracy on test : 72.9508196721
+
+Computation time on 1 cores :
+ Database extraction time : 0:00:00
+ Learn Prediction
+ Fold 1 0:06:00 0:00:15
+ Fold 2 0:12:07 0:00:15
+ Total 0:18:07 0:00:30
+ So a total classification time of 0:12:22.
+
+
+2016-08-24 11:27:43,912 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-112743Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Multiview/Results/20160824-112743Results-Mumbo-Methyl-MiRNA-RNASEQ-Clinical-Yes-No-learnRate0.7-ModifiedMultiOmic-accuracyByIteration.png
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