From 95a5d33c668a328aad10e290b3d19b9f9cb2cdfe Mon Sep 17 00:00:00 2001
From: bbauvin <baptiste.bauvin@centrale-marseille.fr>
Date: Tue, 30 Aug 2016 17:43:47 -0400
Subject: [PATCH] Debugged added fake data available for hdf5, seems to be
working, need to limit time for svm
---
Code/MonoMutliViewClassifiers/ExecClassif.py | 137 +++---
.../Metrics/__init__.py | 7 +
.../Monoview/ExecClassifMonoView.py | 12 +-
.../MonoviewClassifiers/Adaboost.py | 31 +-
.../MonoviewClassifiers/DecisionTree.py | 18 +-
.../MonoviewClassifiers/KNN.py | 18 +-
.../MonoviewClassifiers/RandomForest.py | 22 +-
.../MonoviewClassifiers/SGD.py | 21 +-
.../MonoviewClassifiers/SVMLinear.py | 19 +-
.../MonoviewClassifiers/SVMPoly.py | 25 +-
.../MonoviewClassifiers/SVMRBF.py | 19 +-
.../Multiview/ExecMultiview.py | 8 +-
.../Multiview/Fusion/Fusion.py | 17 +-
.../Multiview/Fusion/Methods/EarlyFusion.py | 4 +-
.../EarlyFusionPackage/WeightedLinear.py | 12 +-
.../Methods/EarlyFusionPackage/__init__.py | 7 +
.../Multiview/Fusion/Methods/LateFusion.py | 3 +-
.../LateFusionPackage/BayesianInference.py | 14 +-
.../LateFusionPackage/MajorityVoting.py | 10 +-
.../Methods/LateFusionPackage/SVMForLinear.py | 4 +-
.../LateFusionPackage/WeightedLinear.py | 10 +-
.../Methods/LateFusionPackage/__init__.py | 7 +
.../Multiview/Fusion/Methods/__init__.py | 3 +-
.../Fusion/Methods/poulet/__init__.py | 7 -
.../Multiview/Fusion/__init__.py | 2 +-
.../Multiview/GetMultiviewDb.py | 79 ++--
.../Mumbo/Classifiers/DecisionTree.py | 2 +-
.../Multiview/Mumbo/Mumbo.py | 4 +-
.../ResultAnalysis.py | 19 +-
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 1 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 1 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 10 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 41 ++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 41 ++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 41 ++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 41 ++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 41 ++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 19 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 52 +++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 52 +++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 52 +++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 74 ++++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 89 ++++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 250 +++++++++++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 1 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 8 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 327 ++++++++++++++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 21 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 18 +
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 36 ++
...A__RNASeq_Clinic-ModifiedMultiOmic-LOG.log | 18 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 351 +++++++++++++++
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 411 ++++++++++++++++++
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 1 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 1 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 1 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 1 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 24 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 19 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 19 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 17 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 195 +++++++++
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 1 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 8 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 1 +
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 162 +++++++
...hyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log | 8 +
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 0
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 0
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 0
...k-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log | 63 +++
.../Results/poulet20160830-103357.png | Bin 0 -> 18696 bytes
.../Results/poulet20160830-103441.png | Bin 0 -> 18696 bytes
.../Results/poulet20160830-103912.png | Bin 0 -> 18696 bytes
.../Results/poulet20160830-104001.png | Bin 0 -> 18696 bytes
Code/MonoMutliViewClassifiers/__init__.py | 2 +
124 files changed, 3338 insertions(+), 241 deletions(-)
delete mode 100644 Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/poulet/__init__.py
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-100913-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101023-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101050-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101154-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101345-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101352-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101503-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101558-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101646-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101740-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101751-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101941-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-101956-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-102012-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-102220-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-102230-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-102638-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-102655-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-102950-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-103111-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-103400-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
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create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-110552-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
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create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-113634-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
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create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-163114-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-163354-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-164435-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-165446-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-170755-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-170857-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-172028-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160829-175309-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-100943-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-101446-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-101634-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-102454-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-102653-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-102706-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-102823-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-102929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-103201-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-103354-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-103441-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-103912-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-104000-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
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create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-111651-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
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create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-112132-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-112630-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-113306-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-113333-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-113715-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
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create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-114801-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-115133-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-115229-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-115605-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
create mode 100644 Code/MonoMutliViewClassifiers/Results/20160830-115919-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
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create mode 100644 Code/MonoMutliViewClassifiers/Results/poulet20160830-103357.png
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diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index 54b27ba1..964e0351 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -10,6 +10,7 @@ import time
import logging
from joblib import Parallel, delayed
from ResultAnalysis import resultAnalysis
+import itertools
import numpy as np
import MonoviewClassifiers
@@ -21,7 +22,7 @@ parser = argparse.ArgumentParser(
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('--name', metavar='STRING', action='store', help='Name of Database (default: %(default)s)',
- default='ModifiedMultiOmic')
+ default='MultiOmic')
groupStandard.add_argument('--type', metavar='STRING', action='store', help='Type of database : .hdf5 or .csv',
default='.hdf5')
groupStandard.add_argument('--views', metavar='STRING', action='store',help='Name of the views selected for learning',
@@ -43,10 +44,10 @@ groupClass.add_argument('--CL_split', metavar='FLOAT', action='store',
groupClass.add_argument('--CL_nbFolds', metavar='INT', action='store', help='Number of folds in cross validation',
type=int, default=5 )
groupClass.add_argument('--CL_nb_class', metavar='INT', action='store', help='Number of classes, -1 for all', type=int,
- default=4)
+ default=2)
groupClass.add_argument('--CL_classes', metavar='STRING', action='store',
help='Classes used in the dataset (names of the folders) if not filled, random classes will be '
- 'selected ex. walrus:mole:leopard', default="")
+ 'selected ex. walrus:mole:leopard', default="jambon:poney")
groupClass.add_argument('--CL_type', metavar='STRING', action='store',
help='Determine whether to use Multiview, Monoview, or Benchmark, separate with : if multiple',
default='Benchmark')
@@ -59,7 +60,10 @@ groupClass.add_argument('--CL_algos_multiview', metavar='STRING', action='store'
groupClass.add_argument('--CL_cores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int,
default=1)
groupClass.add_argument('--CL_metrics', metavar='STRING', action='store',
- help='Determine which metric to use, separate with ":" if multiple, if empty, considering all', default='')
+ help='Determine which metric to use, separate with ":" if multiple, if empty, considering all, '
+ 'first one will be used for gridsearch', default='')
+groupClass.add_argument('--CL_GS_iter', metavar='INT', action='store',
+ help='Determine how many Randomized grid search tests to do', type=int, default=30)
groupRF = parser.add_argument_group('Random Forest arguments')
groupRF.add_argument('--CL_RF_trees', metavar='STRING', action='store', help='GridSearch: Determine the trees',
@@ -155,6 +159,10 @@ datasetLength = DATASET.get("Metadata").attrs["datasetLength"]
NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
views = [str(DATASET.get("View"+str(viewIndex)).attrs["name"]) for viewIndex in range(NB_VIEW)]
NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
+metrics = args.CL_metrics.split(":")
+if metrics == [""]:
+ metrics = [["accuracy_score", None]]
+metric = metrics[0]
logging.info("Start:\t Finding all available mono- & multiview algorithms")
@@ -167,18 +175,20 @@ if args.CL_type.split(":")==["Benchmark"]:
for fusionModulesName in fusionModulesNames]
fusionClasses = [getattr(fusionModule, fusionModulesName+"Classifier")
for fusionModulesName, fusionModule in zip(fusionModulesNames, fusionModules)]
- fusionMethods = dict((fusionModulesName, [subclass.__name__ for subclass in fusionClasse.__subclasses__() ])
- for fusionModulesName, fusionClasse in zip(fusionModulesNames, fusionClasses))
+ fusionMethods = dict((fusionModulesName, [name for _, name, isPackage in
+ pkgutil.iter_modules(["Multiview/Fusion/Methods/"+fusionModulesName+"Package"])
+ if not isPackage])
+ for fusionModulesName, fusionClasse in zip(fusionModulesNames, fusionClasses))
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"]]
+ pkgutil.iter_modules(['Multiview/Mumbo/Classifiers'])
+ if not isPackage and not name in ["SubSampling", "ModifiedMulticlass", "Kover"]]
allMultiviewAlgos = {"Fusion": allFusionAlgos, "Mumbo": allMumboAlgos}
- benchmark = {"Monoview": allMonoviewAlgos, "Multiview" : allMultiviewAlgos}
+ benchmark = {"Monoview": allMonoviewAlgos, "Multiview": allMultiviewAlgos}
if "Multiview" in args.CL_type.strip(":"):
benchmark["Multiview"] = {}
@@ -188,9 +198,9 @@ if "Multiview" in args.CL_type.strip(":"):
benchmark["Multiview"]["Fusion"]= {}
benchmark["Multiview"]["Fusion"]["Methods"] = dict((fusionType, []) for fusionType in args.FU_types.split(":"))
if "LateFusion" in args.FU_types.split(":"):
- benchmark["Multiview"]["Fusion"]["LateFusion"] = args.FU_late_methods.split(":")
+ benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"] = args.FU_late_methods.split(":")
if "EarlyFusion" in args.FU_types.split(":"):
- benchmark["Multiview"]["Fusion"]["EarlyFusion"] = args.FU_early_methods.split(":")
+ benchmark["Multiview"]["Fusion"]["Methods"]["EarlyFusion"] = args.FU_early_methods.split(":")
benchmark["Multiview"]["Fusion"]["Classifiers"] = args.FU_cl_names.split(":")
@@ -199,10 +209,9 @@ if "Monoview" in args.CL_type.strip(":"):
fusionClassifierConfig = "a"
-fusionMethodConfig = "a"
+fusionMethodConfig = ["q", "b"]
mumboClassifierConfig = "a"
mumboclassifierNames = "a"
-metrics = args.CL_metrics.split(":")
RandomForestKWARGS = {"0":map(int, args.CL_RF_trees.split())}
SVMLinearKWARGS = {"0":map(int, args.CL_SVML_C.split(":"))}
@@ -227,13 +236,13 @@ if benchmark["Monoview"]:
argumentDictionaries["Monoview"][str(view)].append(arguments)
bestClassifiers = []
bestClassifiersConfigs = []
-resultsMonoview =[]
+resultsMonoview = []
for viewIndex, viewArguments in enumerate(argumentDictionaries["Monoview"].values()):
- resultsMonoview += (Parallel(n_jobs=nbCores)(
+ resultsMonoview.append( (Parallel(n_jobs=nbCores)(
delayed(ExecMonoview)(DATASET.get("View"+str(viewIndex)), DATASET.get("labels").value, args.name,
args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF, gridSearch=True,
- metrics=metrics[viewIndex], **arguments)
- for arguments in viewArguments))
+ metric=metric, nIter=args.CL_GS_iter, **arguments)
+ for arguments in viewArguments)))
accuracies = [result[1] for result in resultsMonoview[viewIndex]]
classifiersNames = [result[0] for result in resultsMonoview[viewIndex]]
@@ -242,54 +251,54 @@ for viewIndex, viewArguments in enumerate(argumentDictionaries["Monoview"].value
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}}
-# 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}}
-# 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, metrics=metrics, **arguments)
-# for arguments in argumentDictionaries["Multiview"])
-resultsMultiview = []
+
+if benchmark["Multiview"]:
+ if benchmark["Multiview"]["Mumbo"]:
+ for combination in itertools.combinations_with_replacement(range(len(benchmark["Multiview"]["Mumbo"])), NB_VIEW):
+ classifiersNames = [benchmark["Multiview"]["Mumbo"][index] for index in combination]
+ arguments = {"CL_type": "Mumbo",
+ "views": args.views.split(":"),
+ "NB_VIEW": len(args.views.split(":")),
+ "NB_CLASS": len(args.CL_classes.split(":")),
+ "LABELS_NAMES": args.CL_classes.split(":"),
+ "MumboKWARGS": {"classifiersNames": classifiersNames,
+ "maxIter":int(args.MU_iter[0]), "minIter":int(args.MU_iter[1]),
+ "threshold":args.MU_iter[2]}}
+ 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}}
+ 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}}
+ argumentDictionaries["Multiview"].append(arguments)
+
+print len(argumentDictionaries["Multiview"]), len(argumentDictionaries["Monoview"])
+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, metrics=metrics, **arguments)
+ for arguments in argumentDictionaries["Multiview"])
+
results = (resultsMonoview, resultsMultiview)
resultAnalysis(benchmark, results)
-print len(argumentDictionaries["Multiview"]), len(argumentDictionaries["Monoview"])
diff --git a/Code/MonoMutliViewClassifiers/Metrics/__init__.py b/Code/MonoMutliViewClassifiers/Metrics/__init__.py
index e69de29b..9bbd76fb 100644
--- a/Code/MonoMutliViewClassifiers/Metrics/__init__.py
+++ b/Code/MonoMutliViewClassifiers/Metrics/__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/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index 9e2a187c..d6d172e8 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -32,7 +32,7 @@ __date__ = 2016-03-25
def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path, gridSearch=True,
- metrics="accuracy_score", **kwargs):
+ metric=["accuracy_score", None], nIter=30, **kwargs):
t_start = time.time()
directory = os.path.dirname(os.path.abspath(__file__)) + "/Results-ClassMonoView/"
@@ -43,7 +43,6 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
CL_type = kwargs["CL_type"]
classifierKWARGS = kwargs[CL_type+"KWARGS"]
X = X.value
- metrics = [getattr(Metrics, metric) for metric in metrics]
# Determine the Database to extract features
logging.debug("### Main Programm for Classification MonoView")
@@ -62,18 +61,17 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
# Begin Classification RandomForest
logging.debug("Start:\t Classification")
-
classifierModule = getattr(MonoviewClassifiers, CL_type)
classifierGridSearch = getattr(classifierModule, "gridSearch")
- cl_desc = classifierGridSearch(X_train, y_train, nbFolds=nbFolds, nbCores=nbCores, metrics=metrics)
- cl_res = classifierModule.fit(X_train, y_train, NB_CORES=nbCores)
+ cl_desc = classifierGridSearch(X_train, y_train, nbFolds=nbFolds, nbCores=nbCores, metric=metric, nIter=nIter)
+ cl_res = classifierModule.fit(X_train, y_train, NB_CORES=nbCores, **dict((str(index), desc) for index, desc in enumerate(cl_desc)))
t_end = time.time() - t_start
# Add result to Results DF
df_class_res = pd.DataFrame()
- df_class_res = df_class_res.append({'a_class_time':t_end, 'b_cl_desc': cl_desc, 'c_cl_res': cl_res,
- 'd_cl_score': cl_res.best_score_}, ignore_index=True)
+ # df_class_res = df_class_res.append({'a_class_time':t_end, 'b_cl_desc': cl_desc, 'c_cl_res': cl_res,
+ # 'd_cl_score': cl_res.best_score_}, ignore_index=True)
logging.debug("Info:\t Time for Classification: " + str(t_end) + "[s]")
logging.debug("Done:\t Classification")
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py
index df9269d6..a3ba7f67 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/Adaboost.py
@@ -2,18 +2,15 @@ from sklearn.ensemble import AdaBoostClassifier
from sklearn.pipeline import Pipeline
from sklearn.grid_search import RandomizedSearchCV
from sklearn.tree import DecisionTreeClassifier
-from sklearn.utils.testing import all_estimators
-import inspect
-import numpy as np
import Metrics
-
+from scipy.stats import randint
def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
num_estimators = int(kwargs['0'])
- base_estimators = int(kwargs['1'])
+ base_estimators = kwargs['1']
classifier = AdaBoostClassifier(n_estimators=num_estimators, base_estimator=base_estimators)
classifier.fit(DATASET, CLASS_LABELS)
- return "No desc", classifier
+ return classifier
#
# def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
@@ -29,16 +26,22 @@ def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
# return description, detector
-def gridSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nbCores=1):
+def gridSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIter=30, nbCores=1):
+
pipeline = Pipeline([('classifier', AdaBoostClassifier())])
- classifiers = [clf for name, clf in all_estimators(type_filter='classifier')
- if 'sample_weight' in inspect.getargspec(clf().fit)[0]
- and (name != "AdaBoostClassifier" and name !="GradientBoostingClassifier")]
- param= {"classifier__n_estimators": np.random.randint(1, 30, 10),
- "classifier__base_estimator": classifiers}
+ # classifiers = [clf for name, clf in all_estimators(type_filter='classifier')
+ # if 'sample_weight' in inspect.getargspec(clf().fit)[0]
+ # and (name != "AdaBoostClassifier" and name !="GradientBoostingClassifier" )]
+
+ param= {"classifier__n_estimators": randint(1, 15),
+ "classifier__base_estimator": [DecisionTreeClassifier()]}
metricModule = getattr(Metrics, metric[0])
- scorer = metricModule.get_scorer(dict((index, metricConfig) for index, metricConfig in enumerate(metric[1])))
- grid = RandomizedSearchCV(pipeline,param_distributions=param,refit=True,n_jobs=nbCores,scoring='accuracy',cv=nbFolds)
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ scorer = metricModule.get_scorer(**metricKWARGS)
+ grid = RandomizedSearchCV(pipeline, n_iter=nIter, param_distributions=param,refit=True,n_jobs=nbCores,scoring=scorer,cv=nbFolds)
detector = grid.fit(X_train, y_train)
desc_estimators = [detector.best_params_["classifier__n_estimators"],
detector.best_params_["classifier__base_estimator"]]
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/DecisionTree.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/DecisionTree.py
index ce7e739b..5dd79f65 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/DecisionTree.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/DecisionTree.py
@@ -1,14 +1,14 @@
from sklearn.tree import DecisionTreeClassifier
from sklearn.pipeline import Pipeline # Pipelining in classification
-from sklearn.grid_search import GridSearchCV
-import numpy as np
+from sklearn.grid_search import RandomizedSearchCV
import Metrics
+from scipy.stats import randint
def fit(DATASET, CLASS_LABELS, NB_CORES=1, **kwargs):
maxDepth = int(kwargs['0'])
classifier = DecisionTreeClassifier(max_depth=maxDepth)
classifier.fit(DATASET, CLASS_LABELS)
- return "No desc", classifier
+ return classifier
# def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
@@ -24,12 +24,16 @@ def fit(DATASET, CLASS_LABELS, NB_CORES=1, **kwargs):
# return description, DT_detector
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
+def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
pipeline_DT = Pipeline([('classifier', DecisionTreeClassifier())])
- param_DT = {"classifier__max_depth":np.random.randint(1, 30, 10)}
+ param_DT = {"classifier__max_depth": randint(1, 30)}
metricModule = getattr(Metrics, metric[0])
- scorer = metricModule.get_scorer(dict((index, metricConfig) for index, metricConfig in enumerate(metric[1])))
- grid_DT = GridSearchCV(pipeline_DT, param_grid=param_DT, refit=True, n_jobs=nbCores, scoring='accuracy',
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ scorer = metricModule.get_scorer(**metricKWARGS)
+ grid_DT = RandomizedSearchCV(pipeline_DT, n_iter=nIter, param_distributions=param_DT, refit=True, n_jobs=nbCores, scoring=scorer,
cv=nbFolds)
DT_detector = grid_DT.fit(X_train, y_train)
desc_params = [DT_detector.best_params_["classifier__max_depth"]]
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py
index 5e513325..e8edfbef 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/KNN.py
@@ -1,14 +1,14 @@
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline # Pipelining in classification
-from sklearn.grid_search import GridSearchCV
-import numpy as np
+from sklearn.grid_search import RandomizedSearchCV
import Metrics
+from scipy.stats import randint
def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
nNeighbors = int(kwargs['0'])
classifier = KNeighborsClassifier(n_neighbors=nNeighbors)
classifier.fit(DATASET, CLASS_LABELS)
- return "No desc", classifier
+ return classifier
# def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
@@ -24,12 +24,16 @@ def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
# return description, KNN_detector
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
+def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30 ):
pipeline_KNN = Pipeline([('classifier', KNeighborsClassifier())])
- param_KNN = {"classifier__n_neighbors": np.random.randint(1, 30, 10)}
+ param_KNN = {"classifier__n_neighbors": randint(1, 50)}
metricModule = getattr(Metrics, metric[0])
- scorer = metricModule.get_scorer(dict((index, metricConfig) for index, metricConfig in enumerate(metric[1])))
- grid_KNN = GridSearchCV(pipeline_KNN, param_grid=param_KNN, refit=True, n_jobs=nbCores, scoring='accuracy',
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ scorer = metricModule.get_scorer(**metricKWARGS)
+ grid_KNN = RandomizedSearchCV(pipeline_KNN, n_iter=nIter, param_distributions=param_KNN, refit=True, n_jobs=nbCores, scoring=scorer,
cv=nbFolds)
KNN_detector = grid_KNN.fit(X_train, y_train)
desc_params = [KNN_detector.best_params_["classifier__n_neighbors"]]
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py
index 445fdfec..b40cf6e3 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/RandomForest.py
@@ -1,15 +1,15 @@
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
-from sklearn.grid_search import GridSearchCV
+from sklearn.grid_search import RandomizedSearchCV
import Metrics
-
+from scipy.stats import randint
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 "No desc", classifier
+ return classifier
# def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
@@ -43,15 +43,21 @@ def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
# return description, rf_detector
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
+def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
pipeline_rf = Pipeline([('classifier', RandomForestClassifier())])
- param_rf = {"classifier__n_estimators": np.random.randint(1, 30, 10)}
+ param_rf = {"classifier__n_estimators": randint(1, 30),
+ "classifier__max_depth":randint(1, 30)}
metricModule = getattr(Metrics, metric[0])
- scorer = metricModule.get_scorer(dict((index, metricConfig) for index, metricConfig in enumerate(metric[1])))
- grid_rf = GridSearchCV(pipeline_rf,param_grid=param_rf,refit=True,n_jobs=nbCores,scoring='accuracy',cv=nbFolds)
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ scorer = metricModule.get_scorer(**metricKWARGS)
+ grid_rf = RandomizedSearchCV(pipeline_rf, n_iter=nIter,param_distributions=param_rf,refit=True,n_jobs=nbCores,scoring=scorer,cv=nbFolds)
rf_detector = grid_rf.fit(X_train, y_train)
- desc_estimators = [rf_detector.best_params_["classifier__n_estimators"]]
+ desc_estimators = [rf_detector.best_params_["classifier__n_estimators"],
+ rf_detector.best_params_["classifier__max_depth"]]
return desc_estimators
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py
index 15627703..4323e744 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SGD.py
@@ -1,20 +1,19 @@
from sklearn.linear_model import SGDClassifier
from sklearn.pipeline import Pipeline # Pipelining in classification
-from sklearn.grid_search import GridSearchCV
-import numpy as np
+from sklearn.grid_search import RandomizedSearchCV
import Metrics
-
+from scipy.stats import uniform
def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
loss = kwargs['0']
penalty = kwargs['1']
try:
- alpha = int(kwargs['2'])
+ alpha = float(kwargs['2'])
except:
alpha = 0.15
classifier = SGDClassifier(loss=loss, penalty=penalty, alpha=alpha)
classifier.fit(DATASET, CLASS_LABELS)
- return "No desc", classifier
+ return classifier
# def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
@@ -32,16 +31,20 @@ def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
# return description, SGD_detector
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
+def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
pipeline_SGD = Pipeline([('classifier', SGDClassifier())])
losses = ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron']
penalties = ["l1", "l2", "elasticnet"]
- alphas = list(np.random.randint(1,10,10))+list(np.random.random_sample(10))
+ alphas = uniform()
param_SGD = {"classifier__loss": losses, "classifier__penalty": penalties,
"classifier__alpha": alphas}
metricModule = getattr(Metrics, metric[0])
- scorer = metricModule.get_scorer(dict((index, metricConfig) for index, metricConfig in enumerate(metric[1])))
- grid_SGD = GridSearchCV(pipeline_SGD, param_grid=param_SGD, refit=True, n_jobs=nbCores, scoring='accuracy',
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ scorer = metricModule.get_scorer(**metricKWARGS)
+ grid_SGD = RandomizedSearchCV(pipeline_SGD, n_iter=nIter, param_distributions=param_SGD, refit=True, n_jobs=nbCores, scoring=scorer,
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"],
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py
index 43619432..523e998b 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMLinear.py
@@ -1,15 +1,14 @@
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline # Pipelining in classification
-from sklearn.grid_search import GridSearchCV
-import numpy as np
+from sklearn.grid_search import RandomizedSearchCV
import Metrics
-
+from scipy.stats import randint
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 "No desc", classifier
+ return classifier
# def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
@@ -25,12 +24,16 @@ def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
# return description, SVMLinear_detector
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
+def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
pipeline_SVMLinear = Pipeline([('classifier', SVC(kernel="linear"))])
- param_SVMLinear = {"classifier__C":np.random.randint(1,2000,30)}
+ param_SVMLinear = {"classifier__C":randint(1, 10000)}
metricModule = getattr(Metrics, metric[0])
- scorer = metricModule.get_scorer(dict((index, metricConfig) for index, metricConfig in enumerate(metric[1])))
- grid_SVMLinear = GridSearchCV(pipeline_SVMLinear, param_grid=param_SVMLinear, refit=True, n_jobs=nbCores, scoring='accuracy',
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ scorer = metricModule.get_scorer(**metricKWARGS)
+ grid_SVMLinear = RandomizedSearchCV(pipeline_SVMLinear, n_iter=nIter,param_distributions=param_SVMLinear, refit=True, n_jobs=nbCores, scoring=scorer,
cv=nbFolds)
SVMLinear_detector = grid_SVMLinear.fit(X_train, y_train)
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py
index 7db4dd56..7285b29e 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py
@@ -1,16 +1,15 @@
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline # Pipelining in classification
-from sklearn.grid_search import GridSearchCV
-import numpy as np
+from sklearn.grid_search import RandomizedSearchCV
import Metrics
-
+from scipy.stats import randint
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 "No desc", classifier
+ return classifier
# def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
@@ -25,15 +24,19 @@ def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
# return desc_params
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
- pipeline_SVMRBF = Pipeline([('classifier', SVC(kernel="poly"))])
- param_SVMRBF = {"classifier__C": np.random.randint(1,2000,30)}
+def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
+ pipeline_SVMPoly = Pipeline([('classifier', SVC(kernel="poly"))])
+ param_SVMPoly = {"classifier__C": randint(1, 10000), "classifier__degree":randint(1, 30)}
metricModule = getattr(Metrics, metric[0])
- scorer = metricModule.get_scorer(dict((index, metricConfig) for index, metricConfig in enumerate(metric[1])))
- grid_SVMRBF = GridSearchCV(pipeline_SVMRBF, param_grid=param_SVMRBF, refit=True, n_jobs=nbCores, scoring='accuracy',
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ scorer = metricModule.get_scorer(**metricKWARGS)
+ grid_SVMPoly = RandomizedSearchCV(pipeline_SVMPoly, n_iter=nIter, param_distributions=param_SVMPoly, refit=True, n_jobs=nbCores, scoring=scorer,
cv=nbFolds)
- SVMRBF_detector = grid_SVMRBF.fit(X_train, y_train)
- desc_params = [SVMRBF_detector.best_params_["classifier__C"]]
+ SVMRBF_detector = grid_SVMPoly.fit(X_train, y_train)
+ desc_params = [SVMRBF_detector.best_params_["classifier__C"], SVMRBF_detector.best_params_["classifier__degree"]]
return desc_params
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py
index 7c2e9276..481f2ec0 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py
@@ -1,15 +1,14 @@
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline # Pipelining in classification
-from sklearn.grid_search import GridSearchCV
-import numpy as np
+from sklearn.grid_search import RandomizedSearchCV
import Metrics
-
+from scipy.stats import randint
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 "No desc", classifier
+ return classifier
# def fit_gridsearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
@@ -25,12 +24,16 @@ def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
# return description, SVMRBF_detector
-def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], **kwargs):
+def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score", None], nIter=30):
pipeline_SVMRBF = Pipeline([('classifier', SVC(kernel="rbf"))])
- param_SVMRBF = {"classifier__C": np.random.randint(1,2000,30)}
+ param_SVMRBF = {"classifier__C": randint(1, 10000)}
metricModule = getattr(Metrics, metric[0])
- scorer = metricModule.get_scorer(dict((index, metricConfig) for index, metricConfig in enumerate(metric[1])))
- grid_SVMRBF = GridSearchCV(pipeline_SVMRBF, param_grid=param_SVMRBF, refit=True, n_jobs=nbCores, scoring='accuracy',
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ scorer = metricModule.get_scorer(**metricKWARGS)
+ grid_SVMRBF = RandomizedSearchCV(pipeline_SVMRBF, n_iter=nIter, param_distributions=param_SVMRBF, refit=True, n_jobs=nbCores, scoring=scorer,
cv=nbFolds)
SVMRBF_detector = grid_SVMRBF.fit(X_train, y_train)
desc_params = [SVMRBF_detector.best_params_["classifier__C"]]
diff --git a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
index 450624ec..aa2160d5 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
@@ -17,14 +17,14 @@ import time
def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, path, LABELS_DICTIONARY,
- gridSearch=False, metrics=None,**kwargs):
+ gridSearch=False, metric=None, nIter=30, **kwargs):
datasetLength = DATASET.get("Metadata").attrs["datasetLength"]
NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
views = [str(DATASET.get("View"+str(viewIndex)).attrs["name"]) for viewIndex in range(NB_VIEW)]
NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
- if not metrics:
- metrics = ["accuracy_score" for view in range (NB_VIEW)]
+ if not metric:
+ metric = ["accuracy_score", None]
CL_type = kwargs["CL_type"]
views = kwargs["views"]
@@ -82,7 +82,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
if gridSearch:
logging.info("Start:\t Gridsearching best settings for monoview classifiers")
bestSettings, fusionConfig = classifierGridSearch(DATASET, classificationKWARGS, learningIndices
- , metrics=metrics)
+ , metric=metric, nIter=nIter)
classificationKWARGS["classifiersConfigs"] = bestSettings
try:
classificationKWARGS["fusionMethodConfig"] = fusionConfig
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
index 73d82040..e036ef5f 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py
@@ -7,7 +7,7 @@ def makeMonoviewData_hdf5(DATASET, weights=None, usedIndices=None):
if not usedIndices:
uesdIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
- if type(weights)=="NoneType":
+ if weights==None:
weights = np.array([1/NB_VIEW for i in range(NB_VIEW)])
if sum(weights)!=1:
weights = weights/sum(weights)
@@ -16,7 +16,7 @@ def makeMonoviewData_hdf5(DATASET, weights=None, usedIndices=None):
return monoviewData
-def gridSearch_hdf5(DATASET, classificationKWARGS, learningIndices, metrics=None):
+def gridSearch_hdf5(DATASET, classificationKWARGS, learningIndices, metric=None, nIter=30):
fusionTypeName = classificationKWARGS["fusionType"]
fusionTypePackage = globals()[fusionTypeName+"Package"]
fusionMethodModuleName = classificationKWARGS["fusionMethod"]
@@ -28,12 +28,14 @@ def gridSearch_hdf5(DATASET, classificationKWARGS, learningIndices, metrics=None
classifierMethod = getattr(classifierModule, "gridSearch")
if fusionMethodModuleName == "LateFusion":
bestSettings.append(classifierMethod(DATASET.get("View"+str(classifierIndex))[learningIndices],
- DATASET.get("labels")[learningIndices], metrics=metrics[classifierIndex]))
+ DATASET.get("labels")[learningIndices], metric=metric,
+ nIter=nIter))
else:
bestSettings.append(classifierMethod(makeMonoviewData_hdf5(DATASET, usedIndices=learningIndices),
- DATASET.get("labels")[learningIndices], metrics=metrics[classifierIndex]))
+ DATASET.get("labels")[learningIndices], metric=metric,
+ nIter=nIter))
classificationKWARGS["classifiersConfigs"] = bestSettings
- fusionMethodConfig = fusionMethodModule.gridSearch(DATASET, classificationKWARGS, learningIndices)
+ fusionMethodConfig = fusionMethodModule.gridSearch(DATASET, classificationKWARGS, learningIndices, nIter=nIter)
return bestSettings, fusionMethodConfig
@@ -41,8 +43,9 @@ class Fusion:
def __init__(self, NB_VIEW, DATASET_LENGTH, CLASS_LABELS, NB_CORES=1,**kwargs):
fusionType = kwargs['fusionType']
fusionMethod = kwargs['fusionMethod']
- fusionTypeModule = globals()[fusionType]
- fusionMethodClass = getattr(fusionTypeModule, fusionMethod)
+ fusionTypePackage = globals()[fusionType+"Package"]
+ fusionMethodModule = getattr(fusionTypePackage, fusionMethod)
+ fusionMethodClass = getattr(fusionMethodModule, fusionMethod)
nbCores = NB_CORES
classifierKWARGS = dict((key, value) for key, value in kwargs.iteritems() if key not in ['fusionType', 'fusionMethod'])
self.classifier = fusionMethodClass(NB_CORES=nbCores, **classifierKWARGS)
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusion.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusion.py
index 627e1bb4..0512b141 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusion.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusion.py
@@ -3,8 +3,6 @@
import numpy as np
-import MonoviewClassifiers
-
class EarlyFusionClassifier(object):
def __init__(self, monoviewClassifiersNames, monoviewClassifiersConfigs, NB_CORES=1):
@@ -18,7 +16,7 @@ class EarlyFusionClassifier(object):
if not usedIndices:
uesdIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
- if type(weights)=="NoneType":
+ if weights== None:
weights = np.array([1/NB_VIEW for i in range(NB_VIEW)])
if sum(weights)!=1:
weights = weights/sum(weights)
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
index 4965f831..56aaf146 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/WeightedLinear.py
@@ -1,15 +1,15 @@
-from EarlyFusion import EarlyFusionClassifier
+from ...Methods.EarlyFusion import EarlyFusionClassifier
import MonoviewClassifiers
import numpy as np
from sklearn.metrics import accuracy_score
-def gridSearch(DATASET, classificationKWARGS, trainIndices):
+def gridSearch(DATASET, classificationKWARGS, trainIndices, nIter=30):
bestScore = 0.0
bestConfig = None
if classificationKWARGS["fusionMethodConfig"][0] is not None:
- for i in range(0):
- randomWeightsArray = np.random.random_sample(len(DATASET.get("Metadata").attrs["nbView"]))
+ for i in range(nIter):
+ randomWeightsArray = np.random.random_sample(DATASET.get("Metadata").attrs["nbView"])
normalizedArray = randomWeightsArray/np.sum(randomWeightsArray)
classificationKWARGS["fusionMethodConfig"][0] = normalizedArray
classifier = WeightedLinear(1, **classificationKWARGS)
@@ -24,7 +24,7 @@ def gridSearch(DATASET, classificationKWARGS, trainIndices):
class WeightedLinear(EarlyFusionClassifier):
def __init__(self, NB_CORES=1, **kwargs):
- EarlyFusionClassifier.__init__(self, kwargs['classifiersNames'], kwargs['monoviewClassifiersConfigs'],
+ EarlyFusionClassifier.__init__(self, kwargs['classifiersNames'], kwargs['classifiersConfigs'],
NB_CORES=NB_CORES)
self.weights = np.array(map(float, kwargs['fusionMethodConfig'][0]))
@@ -33,7 +33,7 @@ class WeightedLinear(EarlyFusionClassifier):
trainIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
self.makeMonoviewData_hdf5(DATASET, weights=self.weights, usedIndices=trainIndices)
monoviewClassifierModule = getattr(MonoviewClassifiers, self.monoviewClassifierName)
- desc, self.monoviewClassifier = monoviewClassifierModule.fit(self.monoviewData, DATASET.get("labels")[trainIndices],
+ self.monoviewClassifier = monoviewClassifierModule.fit(self.monoviewData, DATASET.get("labels")[trainIndices],
NB_CORES=self.nbCores,
**dict((str(configIndex),config) for configIndex,config in
enumerate(self.monoviewClassifiersConfig)))
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/__init__.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/__init__.py
index e69de29b..9bbd76fb 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/__init__.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusionPackage/__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/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusion.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusion.py
index 844f9969..2564c79c 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusion.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusion.py
@@ -36,13 +36,12 @@ class LateFusionClassifier(object):
if trainIndices == None:
trainIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
nbView = DATASET.get("Metadata").attrs["nbView"]
- monoviewResults = Parallel(n_jobs=self.nbCores)(
+ self.monoviewClassifiers = Parallel(n_jobs=self.nbCores)(
delayed(fifMonoviewClassifier)(self.monoviewClassifiersNames[viewIndex],
DATASET.get("View"+str(viewIndex))[trainIndices, :],
DATASET.get("labels")[trainIndices],
self.monoviewClassifiersConfigs[viewIndex])
for viewIndex in range(nbView))
- self.monoviewClassifiers = [monoviewClassifier for desc, monoviewClassifier in monoviewResults]
# class WeightedLinear(LateFusionClassifier):
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/BayesianInference.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/BayesianInference.py
index af908e11..94935792 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/BayesianInference.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/BayesianInference.py
@@ -1,14 +1,14 @@
-from LateFusion import LateFusionClassifier
+from ...Methods.LateFusion import LateFusionClassifier
import MonoviewClassifiers
import numpy as np
from sklearn.metrics import accuracy_score
-def gridSearch(DATASET, classificationKWARGS, trainIndices):
+def gridSearch(DATASET, classificationKWARGS, trainIndices, nIter=30):
bestScore = 0.0
bestConfig = None
if classificationKWARGS["fusionMethodConfig"][0] is not None:
- for i in range(0):
- randomWeightsArray = np.random.random_sample(len(DATASET.get("Metadata").attrs["nbView"]))
+ for i in range(nIter):
+ randomWeightsArray = np.random.random_sample(DATASET.get("Metadata").attrs["nbView"])
normalizedArray = randomWeightsArray/np.sum(randomWeightsArray)
classificationKWARGS["fusionMethodConfig"][0] = normalizedArray
classifier = BayesianInference(1, **classificationKWARGS)
@@ -23,12 +23,12 @@ def gridSearch(DATASET, classificationKWARGS, trainIndices):
class BayesianInference(LateFusionClassifier):
def __init__(self, NB_CORES=1, **kwargs):
- LateFusionClassifier.__init__(self, kwargs['classifiersNames'], kwargs['monoviewClassifiersConfigs'],
+ LateFusionClassifier.__init__(self, kwargs['classifiersNames'], kwargs['classifiersConfigs'],
NB_CORES=NB_CORES)
self.weights = np.array(map(float, kwargs['fusionMethodConfig'][0]))
def predict_hdf5(self, DATASET, usedIndices=None):
- nbView = DATASET.get("nbView").value
+ nbView = DATASET.get("Metadata").attrs["nbView"]
if usedIndices == None:
usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
if sum(self.weights)!=1.0:
@@ -40,7 +40,7 @@ class BayesianInference(LateFusionClassifier):
viewScores[viewIndex] = np.power(self.monoviewClassifiers[viewIndex].predict_proba(DATASET.get("View" + str(viewIndex))
[usedIndices]),
self.weights[viewIndex])
- predictedLabels = np.argmax(np.prod(viewScores, axis=1), axis=1)
+ predictedLabels = np.argmax(np.prod(viewScores, axis=0), axis=1)
else:
predictedLabels = []
return predictedLabels
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/MajorityVoting.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/MajorityVoting.py
index ce837a4c..166f5ce7 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/MajorityVoting.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/MajorityVoting.py
@@ -1,15 +1,15 @@
-from LateFusion import LateFusionClassifier
+from ...Methods.LateFusion import LateFusionClassifier
import MonoviewClassifiers
import numpy as np
from sklearn.metrics import accuracy_score
-def gridSearch(DATASET, classificationKWARGS, trainIndices):
+def gridSearch(DATASET, classificationKWARGS, trainIndices, nIter=30):
bestScore = 0.0
bestConfig = None
if classificationKWARGS["fusionMethodConfig"][0] is not None:
- for i in range(0):
- randomWeightsArray = np.random.random_sample(len(DATASET.get("Metadata").attrs["nbView"]))
+ for i in range(nIter):
+ randomWeightsArray = np.random.random_sample(DATASET.get("Metadata").attrs["nbView"])
normalizedArray = randomWeightsArray/np.sum(randomWeightsArray)
classificationKWARGS["fusionMethodConfig"][0] = normalizedArray
classifier = MajorityVoting(1, **classificationKWARGS)
@@ -24,7 +24,7 @@ def gridSearch(DATASET, classificationKWARGS, trainIndices):
class MajorityVoting(LateFusionClassifier):
def __init__(self, NB_CORES=1, **kwargs):
- LateFusionClassifier.__init__(self, kwargs['classifiersNames'], kwargs['monoviewClassifiersConfigs'],
+ LateFusionClassifier.__init__(self, kwargs['classifiersNames'], kwargs['classifiersConfigs'],
NB_CORES=NB_CORES)
self.weights = np.array(map(float, kwargs['fusionMethodConfig'][0]))
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/SVMForLinear.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/SVMForLinear.py
index a6464406..6dbc3c24 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/SVMForLinear.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/SVMForLinear.py
@@ -1,4 +1,4 @@
-from LateFusion import LateFusionClassifier
+from ...Methods.LateFusion import LateFusionClassifier
import MonoviewClassifiers
import numpy as np
from sklearn.multiclass import OneVsOneClassifier
@@ -11,7 +11,7 @@ def gridSearch(DATASET, classificationKWARGS, trainIndices):
class SVMForLinear(LateFusionClassifier):
def __init__(self, NB_CORES=1, **kwargs):
- LateFusionClassifier.__init__(self, kwargs['classifiersNames'], kwargs['monoviewClassifiersConfigs'],
+ LateFusionClassifier.__init__(self, kwargs['classifiersNames'], kwargs['classifiersConfigs'],
NB_CORES=NB_CORES)
self.SVMClassifier = None
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/WeightedLinear.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/WeightedLinear.py
index 3ba4b76b..64f5c97e 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/WeightedLinear.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/WeightedLinear.py
@@ -1,15 +1,15 @@
-from LateFusion import LateFusionClassifier
+from ...Methods.LateFusion import LateFusionClassifier
import MonoviewClassifiers
import numpy as np
from sklearn.metrics import accuracy_score
-def gridSearch(DATASET, classificationKWARGS, trainIndices):
+def gridSearch(DATASET, classificationKWARGS, trainIndices, nIter=30):
bestScore = 0.0
bestConfig = None
if classificationKWARGS["fusionMethodConfig"][0] is not None:
- for i in range(0):
- randomWeightsArray = np.random.random_sample(len(DATASET.get("Metadata").attrs["nbView"]))
+ for i in range(nIter):
+ randomWeightsArray = np.random.random_sample(DATASET.get("Metadata").attrs["nbView"])
normalizedArray = randomWeightsArray/np.sum(randomWeightsArray)
classificationKWARGS["fusionMethodConfig"][0] = normalizedArray
classifier = WeightedLinear(1, **classificationKWARGS)
@@ -24,7 +24,7 @@ def gridSearch(DATASET, classificationKWARGS, trainIndices):
class WeightedLinear(LateFusionClassifier):
def __init__(self, NB_CORES=1, **kwargs):
- LateFusionClassifier.__init__(self, kwargs['classifiersNames'], kwargs['monoviewClassifiersConfigs'],
+ LateFusionClassifier.__init__(self, kwargs['classifiersNames'], kwargs['classifiersConfigs'],
NB_CORES=NB_CORES)
self.weights = map(float, kwargs['fusionMethodConfig'][0])
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/__init__.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/__init__.py
index e69de29b..9bbd76fb 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/__init__.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/LateFusionPackage/__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/MonoMutliViewClassifiers/Multiview/Fusion/Methods/__init__.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/__init__.py
index b99d85d7..3ce1d337 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/__init__.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/__init__.py
@@ -1 +1,2 @@
-from . import EarlyFusionPackage, LateFusionPackage
\ No newline at end of file
+from . import EarlyFusion, LateFusion, LateFusionPackage, EarlyFusionPackage
+__all__ = ["EarlyFusionPackage", "LateFusionPackage"]
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/poulet/__init__.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/poulet/__init__.py
deleted file mode 100644
index 9bbd76fb..00000000
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/poulet/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-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/MonoMutliViewClassifiers/Multiview/Fusion/__init__.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/__init__.py
index 5ae1818a..9b0e79fa 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/__init__.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/__init__.py
@@ -1,2 +1,2 @@
-from . import Fusion, analyzeResults
+from . import Fusion, analyzeResults, Methods
__all__ = ["Fusion", "Methods"]
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
index c7d4a6c1..a69ea79e 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
@@ -24,9 +24,9 @@ def getDataset(pathToDB, viewNames, DBName):
return np.array(dataset)
-def getFakeDB(features, pathF, name , NB_CLASS, LABELS_NAME):
+def getFakeDBhdf5(features, pathF, name , NB_CLASS, LABELS_NAME):
NB_VIEW = len(features)
- DATASET_LENGTH = int(pathF)
+ DATASET_LENGTH = 300
VIEW_DIMENSIONS = np.random.random_integers(5, 20, NB_VIEW)
DATA = dict((indx,
@@ -37,7 +37,23 @@ def getFakeDB(features, pathF, name , NB_CLASS, LABELS_NAME):
CLASS_LABELS = np.random.random_integers(0, NB_CLASS-1, DATASET_LENGTH)
LABELS_DICTIONARY = dict((indx, feature) for indx, feature in enumerate(features))
- return DATA, CLASS_LABELS, LABELS_DICTIONARY, DATASET_LENGTH
+ datasetFile = h5py.File(pathF+"Fake.hdf5", "w")
+ for index, viewData in enumerate(DATA.values()):
+ viewDset = datasetFile.create_dataset("View"+str(index), viewData.shape)
+ viewDset[...] = viewData
+ viewDset.attrs["name"] = "View"+str(index)
+ labelsDset = datasetFile.create_dataset("labels", CLASS_LABELS.shape)
+ labelsDset[...] = CLASS_LABELS
+ labelsDset.attrs["name"] = "Labels"
+
+ metaDataGrp = datasetFile.create_group("Metadata")
+ metaDataGrp.attrs["nbView"] = NB_VIEW
+ metaDataGrp.attrs["nbClass"] = NB_CLASS
+ metaDataGrp.attrs["datasetLength"] = len(CLASS_LABELS)
+ labelDictionary = {0:"No", 1:"Yes"}
+ datasetFile.close()
+ datasetFile = h5py.File(pathF+"Fake.hdf5", "r")
+ return datasetFile, LABELS_DICTIONARY
def getAwaLabels(nbLabels, pathToAwa):
@@ -385,17 +401,7 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
clinicalDset.attrs["name"] = "Clinic_"
logging.debug("Done:\t Getting Clinical Data")
- labelFile = open(path+'brca_labels_triple-negatif.csv')
- labels = np.array([int(line.strip().split(',')[1]) for line in labelFile])
- labelsDset = datasetFile.create_dataset("labels", labels.shape)
- labelsDset[...] = labels
- labelsDset.attrs["name"] = "Labels"
- metaDataGrp = datasetFile.create_group("Metadata")
- metaDataGrp.attrs["nbView"] = 5
- metaDataGrp.attrs["nbClass"] = 2
- metaDataGrp.attrs["datasetLength"] = len(labels)
- labelDictionary = {0:"No", 1:"Yes"}
logging.debug("Start:\t Getting Modified RNASeq Data")
RNASeq = datasetFile["View2"][...]
@@ -408,24 +414,35 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
mrnaseqDset.attrs["name"] = "MRNASeq"
logging.debug("Done:\t Getting Modified RNASeq Data")
- datasetFile = h5py.File(path+"ModifiedMultiOmic.hdf5", "r")
- logging.debug("Start:\t Getting Binary RNASeq Data")
- binarizedRNASeqDset = datasetFile.create_dataset("View5", shape=(len(labels), len(rnaseqData)*(len(rnaseqData)-1)/2), dtype=bool)
- for exampleIndex in range(len(labels)):
- offseti=0
- rnaseqData = datasetFile["View2"][exampleIndex]
- for i, idata in enumerate(rnaseqData):
- for j, jdata in enumerate(rnaseqData):
- if i < j:
- binarizedRNASeqDset[offseti+j] = idata > jdata
- offseti += len(rnaseqData)-i-1
- binarizedRNASeqDset.attrs["name"] = "BRNASeq"
- i=0
- for featureIndex in range(len(rnaseqData)*(len(rnaseqData)-1)/2):
- if allSame(binarizedRNASeqDset[:, featureIndex]):
- i+=1
- print i
- logging.debug("Done:\t Getting Binary RNASeq Data")
+ labelFile = open(path+'brca_labels_triple-negatif.csv')
+ labels = np.array([int(line.strip().split(',')[1]) for line in labelFile])
+ labelsDset = datasetFile.create_dataset("labels", labels.shape)
+ labelsDset[...] = labels
+ labelsDset.attrs["name"] = "Labels"
+
+ metaDataGrp = datasetFile.create_group("Metadata")
+ metaDataGrp.attrs["nbView"] = 5
+ metaDataGrp.attrs["nbClass"] = 2
+ metaDataGrp.attrs["datasetLength"] = len(labels)
+ labelDictionary = {0:"No", 1:"Yes"}
+ # datasetFile = h5py.File(path+"ModifiedMultiOmic.hdf5", "r")
+ # logging.debug("Start:\t Getting Binary RNASeq Data")
+ # binarizedRNASeqDset = datasetFile.create_dataset("View5", shape=(len(labels), len(rnaseqData)*(len(rnaseqData)-1)/2), dtype=bool)
+ # for exampleIndex in range(len(labels)):
+ # offseti=0
+ # rnaseqData = datasetFile["View2"][exampleIndex]
+ # for i, idata in enumerate(rnaseqData):
+ # for j, jdata in enumerate(rnaseqData):
+ # if i < j:
+ # binarizedRNASeqDset[offseti+j] = idata > jdata
+ # offseti += len(rnaseqData)-i-1
+ # binarizedRNASeqDset.attrs["name"] = "BRNASeq"
+ # i=0
+ # for featureIndex in range(len(rnaseqData)*(len(rnaseqData)-1)/2):
+ # if allSame(binarizedRNASeqDset[:, featureIndex]):
+ # i+=1
+ # print i
+ # logging.debug("Done:\t Getting Binary RNASeq Data")
datasetFile.close()
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/DecisionTree.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/DecisionTree.py
index 97d57a57..d6d947aa 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/DecisionTree.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/DecisionTree.py
@@ -31,7 +31,7 @@ def getConfig(classifierConfig):
return 'with depth ' + str(depth) + ', ' + ' sub-sampled at ' + str(subSampling) + ' '
-def gridSearch(data, labels, metrics="accuracy_score"):
+def gridSearch(data, labels, metric="accuracy_score"):
minSubSampling = 1.0/(len(labels)/2)
bestSettings = []
bestResults = []
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py
index 19dfc884..3f482152 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py
@@ -42,7 +42,7 @@ def trainWeakClassifier_hdf5(classifierName, monoviewDataset, CLASS_LABELS, DATA
return classifier, classes, isBad, averageAccuracy
-def gridSearch_hdf5(DATASET, classificationKWARGS, learningIndices, metrics=None):
+def gridSearch_hdf5(DATASET, classificationKWARGS, learningIndices, metric=None, nIter=None):
classifiersNames = classificationKWARGS["classifiersNames"]
bestSettings = []
for classifierIndex, classifierName in enumerate(classifiersNames):
@@ -50,7 +50,7 @@ def gridSearch_hdf5(DATASET, classificationKWARGS, learningIndices, metrics=None
classifierModule = globals()[classifierName] # Permet d'appeler une fonction avec une string
classifierMethod = getattr(classifierModule, "gridSearch")
bestSettings.append(classifierMethod(DATASET.get("View"+str(classifierIndex))[learningIndices],
- DATASET.get("labels")[learningIndices], metrics=metrics[classifierIndex]))
+ DATASET.get("labels")[learningIndices], metric=metric))
logging.debug("\tDone:\t Gridsearch for "+classifierName)
return bestSettings, None
diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index 1145fad9..99d3abce 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -8,26 +8,17 @@ def resultAnalysis(benchmark, results):
nbResults = len(mono)+len(multi)
accuracies = [float(accuracy)*100 for [a, accuracy, c, d] in mono]+[float(accuracy)*100 for a, b, c, d, accuracy in multi]
f = pylab.figure()
+ try:
+ fig = plt.gcf()
+ fig.subplots_adjust(bottom=2.0)
+ except:
+ pass
ax = f.add_axes([0.1, 0.1, 0.8, 0.8])
ax.set_title("Accuracies on validation set for each classifier")
ax.bar(range(nbResults), accuracies, align='center')
ax.set_xticks(range(nbResults))
ax.set_xticklabels(names, rotation="vertical")
- try:
- fig = plt.gcf()
- fig.subplots_adjust(bottom=0.8)
- except:
- pass
- # plt.bar(range(nbResults), accuracies, 1)
- # plt.xlabel('ClassLabels')
- # plt.ylabel('Precision in %')
- # plt.title('Results of benchmark-Classification')
- # plt.axis([0, nbResults, 0, 100])
- # plt.xticks(range(nbResults), rotation="vertical")
- # Makes sure that the file does not yet exist
f.savefig("Results/poulet"+time.strftime("%Y%m%d-%H%M%S")+".png")
- #plt.close()
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-100913-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-100913-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..98ed54ed
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-100913-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1 @@
+2016-08-29 10:09:14,077 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101023-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101023-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..3936a601
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101023-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:10:23,014 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:10:23,449 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:10:23,449 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:10:23,449 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:10:23,504 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:10:23,504 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:10:23,504 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:10:23,504 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101050-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101050-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..6df3ccc4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101050-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:10:50,980 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:10:51,009 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:10:51,009 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:10:51,009 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:10:51,084 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:10:51,084 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:10:51,084 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:10:51,085 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101154-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101154-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..7e1bbd2c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101154-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:11:54,701 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:11:54,714 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:11:54,714 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:11:54,714 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:11:54,732 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:11:54,732 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:11:54,732 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:11:54,732 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101345-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101345-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..46f9c6f3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101345-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:13:45,809 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:13:45,822 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:13:45,823 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:13:45,823 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:13:45,838 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:13:45,838 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:13:45,838 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:13:45,838 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101352-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101352-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..8be8e66a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101352-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1 @@
+2016-08-29 10:13:52,819 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101503-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101503-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..f589fa98
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101503-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:15:03,270 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:15:03,283 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:15:03,283 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:15:03,284 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:15:03,297 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:15:03,297 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:15:03,298 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:15:03,298 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101558-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101558-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..cdb502cf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101558-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:15:58,072 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:15:58,097 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:15:58,097 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:15:58,097 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:15:58,120 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:15:58,120 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:15:58,121 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:15:58,121 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101646-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101646-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..06313cca
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101646-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:16:46,408 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:16:46,431 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:16:46,431 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:16:46,431 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:16:46,482 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:16:46,483 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:16:46,483 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:16:46,483 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101740-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101740-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..a2dd7d2b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101740-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:17:40,406 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:17:40,426 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:17:40,427 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:17:40,427 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:17:40,453 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:17:40,453 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:17:40,453 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:17:40,453 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101751-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101751-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..e1a22711
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101751-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:17:51,798 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:17:51,820 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:17:51,820 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:17:51,820 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:17:51,843 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:17:51,844 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:17:51,844 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:17:51,844 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101941-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101941-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..812afead
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101941-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:19:41,708 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:19:41,729 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:19:41,729 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:19:41,729 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:19:41,753 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:19:41,753 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:19:41,754 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:19:41,754 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-101956-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-101956-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..3015c4d1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-101956-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:19:56,966 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:19:56,988 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:19:56,989 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:19:56,989 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:19:57,015 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:19:57,015 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:19:57,015 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:19:57,015 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-102012-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-102012-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..4d24f175
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-102012-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:20:12,435 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:20:12,475 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:20:12,475 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:20:12,475 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:20:12,504 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:20:12,504 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:20:12,504 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:20:12,504 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-102220-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-102220-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..5804a953
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-102220-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:22:20,103 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:22:20,120 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:22:20,120 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:22:20,120 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:22:20,147 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:22:20,147 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:22:20,147 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:22:20,147 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-102230-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-102230-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..f08e7f53
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-102230-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:22:30,088 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:22:30,109 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:22:30,109 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:22:30,109 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:22:30,133 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:22:30,134 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:22:30,134 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:22:30,134 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-102638-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-102638-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..2ca2bbe5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-102638-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:26:38,598 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:26:38,625 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:26:38,625 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:26:38,625 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:26:38,648 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:26:38,648 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:26:38,648 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:26:38,648 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-102655-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-102655-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..ec9d9130
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-102655-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:26:55,242 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:26:55,258 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:26:55,258 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:26:55,258 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:26:55,282 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:26:55,282 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:26:55,282 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:26:55,282 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-102950-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-102950-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..a7c2811b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-102950-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:29:50,267 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:29:50,286 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:29:50,286 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:29:50,286 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:29:50,309 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:29:50,310 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:29:50,310 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:29:50,310 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-103111-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-103111-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..693210bc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-103111-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:31:11,631 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:31:11,656 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:31:11,656 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:31:11,656 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:31:11,681 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:31:11,682 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:31:11,682 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:31:11,682 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-103400-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-103400-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..ef29922b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-103400-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:34:00,795 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:34:00,815 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:34:00,815 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:34:00,815 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:34:00,839 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:34:00,839 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:34:00,839 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:34:00,839 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-103541-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-103541-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..d6bfde15
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-103541-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:35:41,981 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:35:42,005 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:35:42,005 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:35:42,005 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:35:42,031 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:35:42,031 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:35:42,031 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:35:42,031 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-103807-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-103807-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..6c0b71b1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-103807-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:38:07,944 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:38:07,961 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:38:07,962 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:38:07,962 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:38:07,985 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:38:07,985 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:38:07,985 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:38:07,985 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-103947-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-103947-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..94ce9abb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-103947-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,10 @@
+2016-08-29 10:39:47,039 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:39:47,055 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:39:47,055 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:39:47,055 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:39:47,081 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:39:47,081 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:39:47,081 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:39:47,081 DEBUG: Start: Classification
+2016-08-29 10:40:55,241 DEBUG: Info: Time for Classification: 68.1998140812[s]
+2016-08-29 10:40:55,241 DEBUG: Done: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-104427-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-104427-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..96bde205
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-104427-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 10:44:27,654 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:44:27,666 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:44:27,667 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:44:27,667 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:44:27,689 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:44:27,689 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:44:27,689 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:44:27,689 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-104736-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-104736-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..fc023d5f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-104736-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,41 @@
+2016-08-29 10:47:36,028 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 10:47:36,061 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:47:36,061 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 10:47:36,061 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:47:36,094 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:47:36,094 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:47:36,094 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:47:36,094 DEBUG: Start: Classification
+2016-08-29 10:48:53,387 DEBUG: Info: Time for Classification: 77.3440101147[s]
+2016-08-29 10:48:53,387 DEBUG: Done: Classification
+2016-08-29 10:48:53,453 DEBUG: Start: Statistic Results
+2016-08-29 10:48:53,453 INFO: Accuracy :0.771428571429
+2016-08-29 10:48:53,606 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:48:53,606 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 10:48:53,606 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:48:53,621 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:48:53,621 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:48:53,621 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:48:53,621 DEBUG: Start: Classification
+2016-08-29 10:50:00,677 DEBUG: Info: Time for Classification: 67.2197928429[s]
+2016-08-29 10:50:00,677 DEBUG: Done: Classification
+2016-08-29 10:50:00,680 DEBUG: Start: Statistic Results
+2016-08-29 10:50:00,681 INFO: Accuracy :0.819047619048
+2016-08-29 10:50:00,697 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:50:00,698 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 10:50:00,698 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:50:00,722 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:50:00,722 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:50:00,722 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:50:00,723 DEBUG: Start: Classification
+2016-08-29 10:50:26,356 DEBUG: Info: Time for Classification: 25.6708378792[s]
+2016-08-29 10:50:26,356 DEBUG: Done: Classification
+2016-08-29 10:50:27,661 DEBUG: Start: Statistic Results
+2016-08-29 10:50:27,661 INFO: Accuracy :0.866666666667
+2016-08-29 10:50:27,678 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 10:50:27,679 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 10:50:27,679 DEBUG: Start: Determine Train/Test split
+2016-08-29 10:50:27,702 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 10:50:27,702 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 10:50:27,702 DEBUG: Done: Determine Train/Test split
+2016-08-29 10:50:27,702 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-110552-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-110552-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..bbce0c1f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-110552-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,41 @@
+2016-08-29 11:05:52,286 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 11:05:52,312 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:05:52,312 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 11:05:52,312 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:05:52,343 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:05:52,343 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:05:52,343 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:05:52,343 DEBUG: Start: Classification
+2016-08-29 11:07:01,782 DEBUG: Info: Time for Classification: 69.4608111382[s]
+2016-08-29 11:07:01,782 DEBUG: Done: Classification
+2016-08-29 11:07:01,847 DEBUG: Start: Statistic Results
+2016-08-29 11:07:01,847 INFO: Accuracy :0.761904761905
+2016-08-29 11:07:02,104 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:07:02,104 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 11:07:02,105 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:07:02,123 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:07:02,124 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:07:02,124 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:07:02,124 DEBUG: Start: Classification
+2016-08-29 11:07:58,361 DEBUG: Info: Time for Classification: 56.5090019703[s]
+2016-08-29 11:07:58,361 DEBUG: Done: Classification
+2016-08-29 11:07:58,364 DEBUG: Start: Statistic Results
+2016-08-29 11:07:58,364 INFO: Accuracy :0.847619047619
+2016-08-29 11:07:58,376 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:07:58,376 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 11:07:58,376 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:07:58,391 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:07:58,391 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:07:58,391 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:07:58,391 DEBUG: Start: Classification
+2016-08-29 11:08:22,883 DEBUG: Info: Time for Classification: 24.5160729885[s]
+2016-08-29 11:08:22,883 DEBUG: Done: Classification
+2016-08-29 11:08:24,141 DEBUG: Start: Statistic Results
+2016-08-29 11:08:24,141 INFO: Accuracy :0.819047619048
+2016-08-29 11:08:24,154 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:08:24,154 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 11:08:24,154 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:08:24,167 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:08:24,167 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:08:24,167 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:08:24,167 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-113228-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-113228-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..eea4e0e2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-113228-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 11:32:28,555 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 11:32:28,929 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:32:28,929 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 11:32:28,929 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:32:28,956 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:32:28,956 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:32:28,957 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:32:28,957 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-113336-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-113336-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..6c73282c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-113336-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 11:33:36,885 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 11:33:36,898 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:33:36,898 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 11:33:36,898 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:33:36,912 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:33:36,912 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:33:36,912 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:33:36,912 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-113435-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-113435-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..ebe97f82
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-113435-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 11:34:35,315 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 11:34:35,327 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:34:35,327 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 11:34:35,327 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:34:35,341 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:34:35,341 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:34:35,341 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:34:35,341 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-113503-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-113503-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..095f822a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-113503-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 11:35:03,863 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 11:35:03,874 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:35:03,874 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 11:35:03,874 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:35:03,888 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:35:03,888 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:35:03,888 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:35:03,888 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-113527-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-113527-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..e4d04078
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-113527-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 11:35:27,343 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 11:35:27,356 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:35:27,356 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 11:35:27,356 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:35:27,369 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:35:27,370 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:35:27,370 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:35:27,370 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-113545-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-113545-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..d8192fc8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-113545-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 11:35:45,321 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 11:35:45,333 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:35:45,334 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 11:35:45,334 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:35:45,347 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:35:45,347 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:35:45,348 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:35:45,348 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-113603-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-113603-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..f29ce0d7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-113603-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 11:36:03,040 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 11:36:03,052 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:36:03,052 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 11:36:03,052 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:36:03,066 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:36:03,066 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:36:03,066 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:36:03,066 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-113620-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-113620-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..e215d7cf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-113620-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 11:36:20,212 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 11:36:20,223 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:36:20,223 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 11:36:20,223 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:36:20,237 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:36:20,237 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:36:20,237 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:36:20,237 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-113634-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-113634-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..18c25cb5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-113634-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 11:36:34,528 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 11:36:34,541 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:36:34,541 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 11:36:34,541 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:36:34,554 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:36:34,555 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:36:34,555 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:36:34,555 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-113643-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-113643-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..b2da76d1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-113643-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,41 @@
+2016-08-29 11:36:43,371 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 11:36:43,384 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:36:43,384 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 11:36:43,384 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:36:43,398 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:36:43,398 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:36:43,398 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:36:43,399 DEBUG: Start: Classification
+2016-08-29 11:38:00,511 DEBUG: Info: Time for Classification: 77.1025300026[s]
+2016-08-29 11:38:00,511 DEBUG: Done: Classification
+2016-08-29 11:38:00,522 DEBUG: Start: Statistic Results
+2016-08-29 11:38:00,522 INFO: Accuracy :0.809523809524
+2016-08-29 11:38:00,536 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:38:00,536 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 11:38:00,536 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:38:00,548 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:38:00,548 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:38:00,548 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:38:00,548 DEBUG: Start: Classification
+2016-08-29 11:38:56,773 DEBUG: Info: Time for Classification: 56.2486650944[s]
+2016-08-29 11:38:56,773 DEBUG: Done: Classification
+2016-08-29 11:38:56,777 DEBUG: Start: Statistic Results
+2016-08-29 11:38:56,777 INFO: Accuracy :0.8
+2016-08-29 11:38:56,788 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:38:56,788 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 11:38:56,788 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:38:56,802 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:38:56,802 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:38:56,803 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:38:56,803 DEBUG: Start: Classification
+2016-08-29 11:39:21,163 DEBUG: Info: Time for Classification: 24.3840839863[s]
+2016-08-29 11:39:21,163 DEBUG: Done: Classification
+2016-08-29 11:39:22,430 DEBUG: Start: Statistic Results
+2016-08-29 11:39:22,431 INFO: Accuracy :0.866666666667
+2016-08-29 11:39:22,442 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 11:39:22,442 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 11:39:22,442 DEBUG: Start: Determine Train/Test split
+2016-08-29 11:39:22,456 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 11:39:22,456 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 11:39:22,456 DEBUG: Done: Determine Train/Test split
+2016-08-29 11:39:22,456 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-161224-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-161224-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..57aa82c6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-161224-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,41 @@
+2016-08-29 16:12:24,709 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 16:12:24,741 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:12:24,741 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 16:12:24,741 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:12:24,762 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:12:24,762 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:12:24,762 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:12:24,762 DEBUG: Start: Classification
+2016-08-29 16:13:31,023 DEBUG: Info: Time for Classification: 66.301500082[s]
+2016-08-29 16:13:31,023 DEBUG: Done: Classification
+2016-08-29 16:13:31,028 DEBUG: Start: Statistic Results
+2016-08-29 16:13:31,029 INFO: Accuracy :0.8
+2016-08-29 16:13:31,040 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:13:31,040 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 16:13:31,041 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:13:31,052 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:13:31,052 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:13:31,052 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:13:31,052 DEBUG: Start: Classification
+2016-08-29 16:14:39,410 DEBUG: Info: Time for Classification: 68.3794088364[s]
+2016-08-29 16:14:39,410 DEBUG: Done: Classification
+2016-08-29 16:14:39,413 DEBUG: Start: Statistic Results
+2016-08-29 16:14:39,413 INFO: Accuracy :0.809523809524
+2016-08-29 16:14:39,425 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:14:39,425 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 16:14:39,425 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:14:39,439 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:14:39,440 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:14:39,440 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:14:39,440 DEBUG: Start: Classification
+2016-08-29 16:15:04,957 DEBUG: Info: Time for Classification: 25.5417969227[s]
+2016-08-29 16:15:04,957 DEBUG: Done: Classification
+2016-08-29 16:15:06,265 DEBUG: Start: Statistic Results
+2016-08-29 16:15:06,265 INFO: Accuracy :0.819047619048
+2016-08-29 16:15:06,279 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:15:06,279 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 16:15:06,279 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:15:06,293 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:15:06,293 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:15:06,293 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:15:06,293 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-162734-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-162734-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..ada58403
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-162734-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 16:27:34,694 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 16:27:34,964 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:27:34,965 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 16:27:34,965 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:27:34,980 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:27:34,980 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:27:34,980 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:27:34,980 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-162804-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-162804-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..eb8d6c15
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-162804-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,41 @@
+2016-08-29 16:28:04,635 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 16:28:04,652 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:28:04,652 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 16:28:04,652 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:28:04,667 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:28:04,667 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:28:04,667 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:28:04,667 DEBUG: Start: Classification
+2016-08-29 16:29:18,596 DEBUG: Info: Time for Classification: 73.9371509552[s]
+2016-08-29 16:29:18,596 DEBUG: Done: Classification
+2016-08-29 16:29:18,602 DEBUG: Start: Statistic Results
+2016-08-29 16:29:18,602 INFO: Accuracy :0.828571428571
+2016-08-29 16:29:18,614 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:29:18,615 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 16:29:18,615 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:29:18,627 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:29:18,627 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:29:18,628 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:29:18,628 DEBUG: Start: Classification
+2016-08-29 16:30:27,563 DEBUG: Info: Time for Classification: 68.9593780041[s]
+2016-08-29 16:30:27,564 DEBUG: Done: Classification
+2016-08-29 16:30:27,567 DEBUG: Start: Statistic Results
+2016-08-29 16:30:27,567 INFO: Accuracy :0.771428571429
+2016-08-29 16:30:27,578 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:30:27,578 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 16:30:27,578 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:30:27,593 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:30:27,593 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:30:27,593 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:30:27,593 DEBUG: Start: Classification
+2016-08-29 16:30:54,275 DEBUG: Info: Time for Classification: 26.7061460018[s]
+2016-08-29 16:30:54,275 DEBUG: Done: Classification
+2016-08-29 16:30:55,635 DEBUG: Start: Statistic Results
+2016-08-29 16:30:55,635 INFO: Accuracy :0.866666666667
+2016-08-29 16:30:55,648 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:30:55,648 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 16:30:55,648 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:30:55,661 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:30:55,661 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:30:55,661 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:30:55,661 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-163114-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-163114-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..4161cbe0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-163114-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,19 @@
+2016-08-29 16:31:14,680 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 16:31:14,693 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:31:14,693 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 16:31:14,693 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:31:14,707 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:31:14,707 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:31:14,707 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:31:14,707 DEBUG: Start: Classification
+2016-08-29 16:32:26,973 DEBUG: Info: Time for Classification: 72.2895948887[s]
+2016-08-29 16:32:26,973 DEBUG: Done: Classification
+2016-08-29 16:32:26,978 DEBUG: Start: Statistic Results
+2016-08-29 16:32:26,978 INFO: Accuracy :0.695238095238
+2016-08-29 16:32:26,990 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:32:26,990 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 16:32:26,990 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:32:27,001 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:32:27,001 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:32:27,001 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:32:27,001 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-163354-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-163354-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..d6dc7593
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-163354-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,52 @@
+2016-08-29 16:33:54,279 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 16:33:54,292 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:33:54,292 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 16:33:54,292 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:33:54,305 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:33:54,305 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:33:54,305 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:33:54,305 DEBUG: Start: Classification
+2016-08-29 16:34:58,988 DEBUG: Info: Time for Classification: 64.7065241337[s]
+2016-08-29 16:34:58,988 DEBUG: Done: Classification
+2016-08-29 16:34:58,993 DEBUG: Start: Statistic Results
+2016-08-29 16:34:58,993 INFO: Accuracy :0.8
+2016-08-29 16:34:59,005 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:34:59,005 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 16:34:59,005 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:34:59,016 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:34:59,016 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:34:59,016 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:34:59,016 DEBUG: Start: Classification
+2016-08-29 16:36:02,531 DEBUG: Info: Time for Classification: 63.5361440182[s]
+2016-08-29 16:36:02,531 DEBUG: Done: Classification
+2016-08-29 16:36:02,534 DEBUG: Start: Statistic Results
+2016-08-29 16:36:02,535 INFO: Accuracy :0.761904761905
+2016-08-29 16:36:02,542 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:36:02,543 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 16:36:02,543 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:36:02,554 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:36:02,555 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:36:02,555 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:36:02,555 DEBUG: Start: Classification
+2016-08-29 16:36:26,497 DEBUG: Info: Time for Classification: 23.9610130787[s]
+2016-08-29 16:36:26,497 DEBUG: Done: Classification
+2016-08-29 16:36:27,746 DEBUG: Start: Statistic Results
+2016-08-29 16:36:27,746 INFO: Accuracy :0.895238095238
+2016-08-29 16:36:27,758 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:36:27,758 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 16:36:27,758 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:36:27,771 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:36:27,771 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:36:27,771 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:36:27,771 DEBUG: Start: Classification
+2016-08-29 16:37:14,329 DEBUG: Info: Time for Classification: 46.5805008411[s]
+2016-08-29 16:37:14,329 DEBUG: Done: Classification
+2016-08-29 16:37:14,335 DEBUG: Start: Statistic Results
+2016-08-29 16:37:14,335 INFO: Accuracy :0.92380952381
+2016-08-29 16:37:14,347 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:37:14,347 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-29 16:37:14,347 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:37:14,359 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:37:14,359 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:37:14,359 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:37:14,359 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-164435-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-164435-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..ac4b900a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-164435-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,52 @@
+2016-08-29 16:44:35,350 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 16:44:35,362 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:44:35,362 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 16:44:35,362 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:44:35,376 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:44:35,376 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:44:35,376 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:44:35,376 DEBUG: Start: Classification
+2016-08-29 16:45:30,063 DEBUG: Info: Time for Classification: 54.7101948261[s]
+2016-08-29 16:45:30,063 DEBUG: Done: Classification
+2016-08-29 16:45:30,068 DEBUG: Start: Statistic Results
+2016-08-29 16:45:30,068 INFO: Accuracy :0.838095238095
+2016-08-29 16:45:30,079 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:45:30,080 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 16:45:30,080 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:45:30,091 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:45:30,091 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:45:30,091 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:45:30,091 DEBUG: Start: Classification
+2016-08-29 16:46:30,600 DEBUG: Info: Time for Classification: 60.5300340652[s]
+2016-08-29 16:46:30,600 DEBUG: Done: Classification
+2016-08-29 16:46:30,603 DEBUG: Start: Statistic Results
+2016-08-29 16:46:30,603 INFO: Accuracy :0.828571428571
+2016-08-29 16:46:30,615 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:46:30,615 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 16:46:30,615 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:46:30,629 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:46:30,629 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:46:30,629 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:46:30,629 DEBUG: Start: Classification
+2016-08-29 16:46:55,375 DEBUG: Info: Time for Classification: 24.7699568272[s]
+2016-08-29 16:46:55,375 DEBUG: Done: Classification
+2016-08-29 16:46:56,632 DEBUG: Start: Statistic Results
+2016-08-29 16:46:56,632 INFO: Accuracy :0.847619047619
+2016-08-29 16:46:56,644 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:46:56,645 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 16:46:56,645 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:46:56,658 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:46:56,658 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:46:56,658 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:46:56,659 DEBUG: Start: Classification
+2016-08-29 16:47:39,147 DEBUG: Info: Time for Classification: 42.5119411945[s]
+2016-08-29 16:47:39,147 DEBUG: Done: Classification
+2016-08-29 16:47:39,152 DEBUG: Start: Statistic Results
+2016-08-29 16:47:39,152 INFO: Accuracy :0.885714285714
+2016-08-29 16:47:39,160 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:47:39,160 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-29 16:47:39,160 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:47:39,171 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:47:39,171 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:47:39,172 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:47:39,172 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-165446-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-165446-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..2933f265
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-165446-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,52 @@
+2016-08-29 16:54:46,227 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 16:54:46,239 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:54:46,239 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 16:54:46,239 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:54:46,253 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:54:46,253 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:54:46,254 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:54:46,254 DEBUG: Start: Classification
+2016-08-29 16:55:59,652 DEBUG: Info: Time for Classification: 73.4229171276[s]
+2016-08-29 16:55:59,652 DEBUG: Done: Classification
+2016-08-29 16:55:59,657 DEBUG: Start: Statistic Results
+2016-08-29 16:55:59,657 INFO: Accuracy :0.8
+2016-08-29 16:55:59,669 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:55:59,669 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 16:55:59,669 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:55:59,680 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:55:59,680 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:55:59,680 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:55:59,680 DEBUG: Start: Classification
+2016-08-29 16:56:53,704 DEBUG: Info: Time for Classification: 54.0451388359[s]
+2016-08-29 16:56:53,704 DEBUG: Done: Classification
+2016-08-29 16:56:53,711 DEBUG: Start: Statistic Results
+2016-08-29 16:56:53,711 INFO: Accuracy :0.761904761905
+2016-08-29 16:56:53,723 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:56:53,723 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 16:56:53,723 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:56:53,737 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:56:53,737 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:56:53,737 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:56:53,737 DEBUG: Start: Classification
+2016-08-29 16:57:18,331 DEBUG: Info: Time for Classification: 24.6174499989[s]
+2016-08-29 16:57:18,331 DEBUG: Done: Classification
+2016-08-29 16:57:19,611 DEBUG: Start: Statistic Results
+2016-08-29 16:57:19,611 INFO: Accuracy :0.857142857143
+2016-08-29 16:57:19,623 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:57:19,623 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 16:57:19,624 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:57:19,637 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:57:19,637 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:57:19,638 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:57:19,638 DEBUG: Start: Classification
+2016-08-29 16:58:07,785 DEBUG: Info: Time for Classification: 48.1718809605[s]
+2016-08-29 16:58:07,786 DEBUG: Done: Classification
+2016-08-29 16:58:07,791 DEBUG: Start: Statistic Results
+2016-08-29 16:58:07,791 INFO: Accuracy :0.828571428571
+2016-08-29 16:58:07,803 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 16:58:07,803 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-29 16:58:07,803 DEBUG: Start: Determine Train/Test split
+2016-08-29 16:58:07,816 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 16:58:07,817 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 16:58:07,817 DEBUG: Done: Determine Train/Test split
+2016-08-29 16:58:07,817 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-170755-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-170755-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..0f7d7ee6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-170755-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-29 17:07:55,205 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 17:07:55,218 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:07:55,219 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 17:07:55,219 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:07:55,232 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:07:55,232 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:07:55,232 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:07:55,232 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-170857-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-170857-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..3c38718b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-170857-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,74 @@
+2016-08-29 17:08:57,904 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 17:08:57,917 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:08:57,918 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 17:08:57,918 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:08:57,931 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:08:57,931 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:08:57,931 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:08:57,931 DEBUG: Start: Classification
+2016-08-29 17:10:06,685 DEBUG: Info: Time for Classification: 68.778083086[s]
+2016-08-29 17:10:06,685 DEBUG: Done: Classification
+2016-08-29 17:10:06,690 DEBUG: Start: Statistic Results
+2016-08-29 17:10:06,691 INFO: Accuracy :0.866666666667
+2016-08-29 17:10:06,702 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:10:06,702 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 17:10:06,702 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:10:06,713 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:10:06,713 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:10:06,713 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:10:06,713 DEBUG: Start: Classification
+2016-08-29 17:10:46,716 DEBUG: Info: Time for Classification: 40.023277998[s]
+2016-08-29 17:10:46,716 DEBUG: Done: Classification
+2016-08-29 17:10:46,719 DEBUG: Start: Statistic Results
+2016-08-29 17:10:46,719 INFO: Accuracy :0.790476190476
+2016-08-29 17:10:46,731 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:10:46,731 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 17:10:46,731 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:10:46,744 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:10:46,744 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:10:46,744 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:10:46,745 DEBUG: Start: Classification
+2016-08-29 17:11:01,689 DEBUG: Info: Time for Classification: 14.9678399563[s]
+2016-08-29 17:11:01,689 DEBUG: Done: Classification
+2016-08-29 17:11:02,981 DEBUG: Start: Statistic Results
+2016-08-29 17:11:02,981 INFO: Accuracy :0.819047619048
+2016-08-29 17:11:02,994 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:11:02,994 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 17:11:02,994 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:11:03,008 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:11:03,008 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:11:03,008 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:11:03,008 DEBUG: Start: Classification
+2016-08-29 17:11:46,405 DEBUG: Info: Time for Classification: 43.4208889008[s]
+2016-08-29 17:11:46,405 DEBUG: Done: Classification
+2016-08-29 17:11:46,412 DEBUG: Start: Statistic Results
+2016-08-29 17:11:46,413 INFO: Accuracy :0.866666666667
+2016-08-29 17:11:46,422 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:11:46,422 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-29 17:11:46,423 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:11:46,435 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:11:46,435 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:11:46,435 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:11:46,435 DEBUG: Start: Classification
+2016-08-29 17:16:45,480 DEBUG: Info: Time for Classification: 299.065301895[s]
+2016-08-29 17:16:45,480 DEBUG: Done: Classification
+2016-08-29 17:16:45,489 DEBUG: Start: Statistic Results
+2016-08-29 17:16:45,490 INFO: Accuracy :0.885714285714
+2016-08-29 17:16:45,498 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:16:45,499 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-29 17:16:45,499 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:16:45,510 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:16:45,510 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:16:45,510 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:16:45,510 DEBUG: Start: Classification
+2016-08-29 17:17:14,549 DEBUG: Info: Time for Classification: 29.0579118729[s]
+2016-08-29 17:17:14,549 DEBUG: Done: Classification
+2016-08-29 17:17:14,891 DEBUG: Start: Statistic Results
+2016-08-29 17:17:14,891 INFO: Accuracy :0.847619047619
+2016-08-29 17:17:14,899 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:17:14,899 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-29 17:17:14,899 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:17:14,909 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:17:14,909 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:17:14,909 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:17:14,910 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-172028-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-172028-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..33c0108d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-172028-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,89 @@
+2016-08-29 17:20:28,923 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 17:20:28,935 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:20:28,936 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 17:20:28,936 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:20:28,949 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:20:28,949 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:20:28,949 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:20:28,950 DEBUG: Start: Classification
+2016-08-29 17:21:38,751 DEBUG: Info: Time for Classification: 69.825948[s]
+2016-08-29 17:21:38,752 DEBUG: Done: Classification
+2016-08-29 17:21:38,757 DEBUG: Start: Statistic Results
+2016-08-29 17:21:38,757 INFO: Accuracy :0.790476190476
+2016-08-29 17:21:38,768 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:21:38,768 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 17:21:38,768 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:21:38,779 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:21:38,780 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:21:38,780 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:21:38,780 DEBUG: Start: Classification
+2016-08-29 17:22:12,472 DEBUG: Info: Time for Classification: 33.7131619453[s]
+2016-08-29 17:22:12,472 DEBUG: Done: Classification
+2016-08-29 17:22:12,475 DEBUG: Start: Statistic Results
+2016-08-29 17:22:12,475 INFO: Accuracy :0.780952380952
+2016-08-29 17:22:12,487 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:22:12,487 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 17:22:12,487 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:22:12,500 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:22:12,500 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:22:12,500 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:22:12,501 DEBUG: Start: Classification
+2016-08-29 17:22:27,403 DEBUG: Info: Time for Classification: 14.9256680012[s]
+2016-08-29 17:22:27,403 DEBUG: Done: Classification
+2016-08-29 17:22:28,653 DEBUG: Start: Statistic Results
+2016-08-29 17:22:28,654 INFO: Accuracy :0.866666666667
+2016-08-29 17:22:28,666 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:22:28,666 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 17:22:28,666 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:22:28,679 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:22:28,680 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:22:28,680 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:22:28,680 DEBUG: Start: Classification
+2016-08-29 17:23:06,935 DEBUG: Info: Time for Classification: 38.2793560028[s]
+2016-08-29 17:23:06,935 DEBUG: Done: Classification
+2016-08-29 17:23:06,942 DEBUG: Start: Statistic Results
+2016-08-29 17:23:06,943 INFO: Accuracy :0.847619047619
+2016-08-29 17:23:06,954 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:23:06,955 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-29 17:23:06,955 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:23:06,969 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:23:06,969 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:23:06,969 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:23:06,969 DEBUG: Start: Classification
+2016-08-29 17:27:52,404 DEBUG: Info: Time for Classification: 285.460083008[s]
+2016-08-29 17:27:52,405 DEBUG: Done: Classification
+2016-08-29 17:27:52,414 DEBUG: Start: Statistic Results
+2016-08-29 17:27:52,414 INFO: Accuracy :0.847619047619
+2016-08-29 17:27:52,423 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:27:52,423 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-29 17:27:52,423 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:27:52,435 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:27:52,435 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:27:52,435 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:27:52,435 DEBUG: Start: Classification
+2016-08-29 17:28:17,707 DEBUG: Info: Time for Classification: 25.2910339832[s]
+2016-08-29 17:28:17,707 DEBUG: Done: Classification
+2016-08-29 17:28:18,029 DEBUG: Start: Statistic Results
+2016-08-29 17:28:18,029 INFO: Accuracy :0.866666666667
+2016-08-29 17:28:18,037 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:28:18,038 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-29 17:28:18,038 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:28:18,049 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:28:18,049 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:28:18,049 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:28:18,049 DEBUG: Start: Classification
+2016-08-29 17:30:54,635 DEBUG: Info: Time for Classification: 156.604124069[s]
+2016-08-29 17:30:54,635 DEBUG: Done: Classification
+2016-08-29 17:30:54,996 DEBUG: Start: Statistic Results
+2016-08-29 17:30:54,997 INFO: Accuracy :0.914285714286
+2016-08-29 17:30:55,005 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:30:55,005 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-08-29 17:30:55,005 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:30:55,016 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:30:55,016 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:30:55,016 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:30:55,017 DEBUG: Start: Classification
+2016-08-29 17:31:21,638 DEBUG: Info: Time for Classification: 26.6399168968[s]
+2016-08-29 17:31:21,638 DEBUG: Done: Classification
+2016-08-29 17:31:21,995 DEBUG: Start: Statistic Results
+2016-08-29 17:31:21,995 INFO: Accuracy :0.92380952381
diff --git a/Code/MonoMutliViewClassifiers/Results/20160829-175309-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160829-175309-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..2098ea93
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160829-175309-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,250 @@
+2016-08-29 17:53:09,721 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-29 17:53:09,741 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:53:09,741 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 17:53:09,741 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:53:09,769 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:53:09,769 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:53:09,769 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:53:09,769 DEBUG: Start: Classification
+2016-08-29 17:54:18,537 DEBUG: Info: Time for Classification: 68.8128628731[s]
+2016-08-29 17:54:18,537 DEBUG: Done: Classification
+2016-08-29 17:54:18,542 DEBUG: Start: Statistic Results
+2016-08-29 17:54:18,543 INFO: Accuracy :0.752380952381
+2016-08-29 17:54:18,563 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:54:18,563 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 17:54:18,563 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:54:18,574 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:54:18,574 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:54:18,575 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:54:18,575 DEBUG: Start: Classification
+2016-08-29 17:55:01,610 DEBUG: Info: Time for Classification: 43.0636219978[s]
+2016-08-29 17:55:01,610 DEBUG: Done: Classification
+2016-08-29 17:55:01,613 DEBUG: Start: Statistic Results
+2016-08-29 17:55:01,613 INFO: Accuracy :0.847619047619
+2016-08-29 17:55:01,623 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:55:01,623 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 17:55:01,623 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:55:01,637 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:55:01,637 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:55:01,637 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:55:01,637 DEBUG: Start: Classification
+2016-08-29 17:55:17,053 DEBUG: Info: Time for Classification: 15.4375040531[s]
+2016-08-29 17:55:17,053 DEBUG: Done: Classification
+2016-08-29 17:55:18,349 DEBUG: Start: Statistic Results
+2016-08-29 17:55:18,349 INFO: Accuracy :0.857142857143
+2016-08-29 17:55:18,371 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:55:18,371 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 17:55:18,371 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:55:18,391 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:55:18,392 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:55:18,392 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:55:18,392 DEBUG: Start: Classification
+2016-08-29 17:55:46,780 DEBUG: Info: Time for Classification: 28.4254119396[s]
+2016-08-29 17:55:46,780 DEBUG: Done: Classification
+2016-08-29 17:55:46,784 DEBUG: Start: Statistic Results
+2016-08-29 17:55:46,785 INFO: Accuracy :0.780952380952
+2016-08-29 17:55:46,803 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 17:55:46,803 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-29 17:55:46,803 DEBUG: Start: Determine Train/Test split
+2016-08-29 17:55:46,823 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 17:55:46,823 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 17:55:46,823 DEBUG: Done: Determine Train/Test split
+2016-08-29 17:55:46,823 DEBUG: Start: Classification
+2016-08-29 18:00:35,467 DEBUG: Info: Time for Classification: 288.678122044[s]
+2016-08-29 18:00:35,467 DEBUG: Done: Classification
+2016-08-29 18:00:35,477 DEBUG: Start: Statistic Results
+2016-08-29 18:00:35,478 INFO: Accuracy :0.866666666667
+2016-08-29 18:00:35,493 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:00:35,493 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-29 18:00:35,494 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:00:35,507 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 18:00:35,508 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 18:00:35,508 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:00:35,508 DEBUG: Start: Classification
+2016-08-29 18:01:01,704 DEBUG: Info: Time for Classification: 26.2219488621[s]
+2016-08-29 18:01:01,704 DEBUG: Done: Classification
+2016-08-29 18:01:02,046 DEBUG: Start: Statistic Results
+2016-08-29 18:01:02,047 INFO: Accuracy :0.87619047619
+2016-08-29 18:01:02,058 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:01:02,058 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-29 18:01:02,058 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:01:02,071 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 18:01:02,071 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 18:01:02,071 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:01:02,071 DEBUG: Start: Classification
+2016-08-29 18:03:49,809 DEBUG: Info: Time for Classification: 167.760786057[s]
+2016-08-29 18:03:49,810 DEBUG: Done: Classification
+2016-08-29 18:03:50,168 DEBUG: Start: Statistic Results
+2016-08-29 18:03:50,168 INFO: Accuracy :0.952380952381
+2016-08-29 18:03:50,180 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:03:50,180 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-08-29 18:03:50,180 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:03:50,193 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-29 18:03:50,193 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-29 18:03:50,193 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:03:50,193 DEBUG: Start: Classification
+2016-08-29 18:04:16,487 DEBUG: Info: Time for Classification: 26.3166110516[s]
+2016-08-29 18:04:16,487 DEBUG: Done: Classification
+2016-08-29 18:04:16,856 DEBUG: Start: Statistic Results
+2016-08-29 18:04:16,856 INFO: Accuracy :0.933333333333
+2016-08-29 18:04:16,882 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:04:16,882 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 18:04:16,883 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:04:16,883 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-29 18:04:16,883 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-29 18:04:16,884 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:04:16,884 DEBUG: Start: Classification
+2016-08-29 18:04:18,942 DEBUG: Info: Time for Classification: 2.08398890495[s]
+2016-08-29 18:04:18,942 DEBUG: Done: Classification
+2016-08-29 18:04:18,944 DEBUG: Start: Statistic Results
+2016-08-29 18:04:18,944 INFO: Accuracy :0.87619047619
+2016-08-29 18:04:18,945 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:04:18,945 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 18:04:18,945 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:04:18,946 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-29 18:04:18,946 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-29 18:04:18,946 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:04:18,946 DEBUG: Start: Classification
+2016-08-29 18:04:20,134 DEBUG: Info: Time for Classification: 1.1890399456[s]
+2016-08-29 18:04:20,134 DEBUG: Done: Classification
+2016-08-29 18:04:20,136 DEBUG: Start: Statistic Results
+2016-08-29 18:04:20,136 INFO: Accuracy :0.87619047619
+2016-08-29 18:04:20,137 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:04:20,137 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 18:04:20,138 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:04:20,138 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-29 18:04:20,138 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-29 18:04:20,138 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:04:20,138 DEBUG: Start: Classification
+2016-08-29 18:04:20,749 DEBUG: Info: Time for Classification: 0.612411022186[s]
+2016-08-29 18:04:20,750 DEBUG: Done: Classification
+2016-08-29 18:04:20,794 DEBUG: Start: Statistic Results
+2016-08-29 18:04:20,794 INFO: Accuracy :0.790476190476
+2016-08-29 18:04:20,796 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:04:20,796 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 18:04:20,796 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:04:20,797 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-29 18:04:20,797 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-29 18:04:20,797 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:04:20,797 DEBUG: Start: Classification
+2016-08-29 18:04:35,959 DEBUG: Info: Time for Classification: 15.1630759239[s]
+2016-08-29 18:04:35,959 DEBUG: Done: Classification
+2016-08-29 18:04:35,963 DEBUG: Start: Statistic Results
+2016-08-29 18:04:35,964 INFO: Accuracy :0.828571428571
+2016-08-29 18:04:35,965 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:04:35,965 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-29 18:04:35,965 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:04:35,966 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-29 18:04:35,966 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-29 18:04:35,966 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:04:35,966 DEBUG: Start: Classification
+2016-08-29 18:04:53,803 DEBUG: Info: Time for Classification: 17.8378360271[s]
+2016-08-29 18:04:53,803 DEBUG: Done: Classification
+2016-08-29 18:04:53,804 DEBUG: Start: Statistic Results
+2016-08-29 18:04:53,805 INFO: Accuracy :0.790476190476
+2016-08-29 18:04:53,806 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:04:53,806 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-29 18:04:53,806 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:04:53,807 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-29 18:04:53,807 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-29 18:04:53,807 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:04:53,807 DEBUG: Start: Classification
+2016-08-29 18:05:05,528 DEBUG: Info: Time for Classification: 11.7219748497[s]
+2016-08-29 18:05:05,528 DEBUG: Done: Classification
+2016-08-29 18:05:05,535 DEBUG: Start: Statistic Results
+2016-08-29 18:05:05,536 INFO: Accuracy :0.780952380952
+2016-08-29 18:05:05,537 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:05:05,537 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-29 18:05:05,537 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:05:05,538 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-29 18:05:05,538 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-29 18:05:05,538 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:05:05,538 DEBUG: Start: Classification
+2016-08-29 18:05:28,522 DEBUG: Info: Time for Classification: 22.985229969[s]
+2016-08-29 18:05:28,522 DEBUG: Done: Classification
+2016-08-29 18:05:28,531 DEBUG: Start: Statistic Results
+2016-08-29 18:05:28,531 INFO: Accuracy :0.771428571429
+2016-08-29 18:05:28,532 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:05:28,533 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-08-29 18:05:28,533 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:05:28,533 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-29 18:05:28,533 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-29 18:05:28,534 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:05:28,534 DEBUG: Start: Classification
+2016-08-29 18:05:30,759 DEBUG: Info: Time for Classification: 2.22728586197[s]
+2016-08-29 18:05:30,760 DEBUG: Done: Classification
+2016-08-29 18:05:30,788 DEBUG: Start: Statistic Results
+2016-08-29 18:05:30,788 INFO: Accuracy :0.695238095238
+2016-08-29 18:05:31,924 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:05:31,925 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-29 18:05:31,925 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:05:31,998 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-29 18:05:31,998 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-29 18:05:31,998 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:05:31,999 DEBUG: Start: Classification
+2016-08-29 18:10:30,396 DEBUG: Info: Time for Classification: 299.606837034[s]
+2016-08-29 18:10:30,397 DEBUG: Done: Classification
+2016-08-29 18:10:30,408 DEBUG: Start: Statistic Results
+2016-08-29 18:10:30,409 INFO: Accuracy :0.638095238095
+2016-08-29 18:10:30,542 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:10:30,542 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-29 18:10:30,543 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:10:30,589 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-29 18:10:30,590 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-29 18:10:30,590 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:10:30,590 DEBUG: Start: Classification
+2016-08-29 18:13:17,645 DEBUG: Info: Time for Classification: 167.231894016[s]
+2016-08-29 18:13:17,646 DEBUG: Done: Classification
+2016-08-29 18:13:17,651 DEBUG: Start: Statistic Results
+2016-08-29 18:13:17,652 INFO: Accuracy :0.6
+2016-08-29 18:13:17,682 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:13:17,682 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-29 18:13:17,682 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:13:17,716 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-29 18:13:17,716 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-29 18:13:17,716 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:13:17,716 DEBUG: Start: Classification
+2016-08-29 18:14:05,059 DEBUG: Info: Time for Classification: 47.4056019783[s]
+2016-08-29 18:14:05,059 DEBUG: Done: Classification
+2016-08-29 18:14:08,685 DEBUG: Start: Statistic Results
+2016-08-29 18:14:08,686 INFO: Accuracy :0.733333333333
+2016-08-29 18:14:08,719 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:14:08,719 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-29 18:14:08,719 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:14:08,756 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-29 18:14:08,756 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-29 18:14:08,756 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:14:08,756 DEBUG: Start: Classification
+2016-08-29 18:15:27,068 DEBUG: Info: Time for Classification: 78.3780429363[s]
+2016-08-29 18:15:27,068 DEBUG: Done: Classification
+2016-08-29 18:15:27,079 DEBUG: Start: Statistic Results
+2016-08-29 18:15:27,079 INFO: Accuracy :0.771428571429
+2016-08-29 18:15:27,109 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:15:27,109 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-29 18:15:27,109 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:15:27,142 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-29 18:15:27,143 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-29 18:15:27,143 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:15:27,143 DEBUG: Start: Classification
+2016-08-29 18:29:35,987 DEBUG: Info: Time for Classification: 848.906258821[s]
+2016-08-29 18:29:35,987 DEBUG: Done: Classification
+2016-08-29 18:29:36,013 DEBUG: Start: Statistic Results
+2016-08-29 18:29:36,013 INFO: Accuracy :0.657142857143
+2016-08-29 18:29:36,044 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:29:36,044 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-29 18:29:36,045 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:29:36,082 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-29 18:29:36,082 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-29 18:29:36,082 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:29:36,082 DEBUG: Start: Classification
+2016-08-29 18:31:41,723 DEBUG: Info: Time for Classification: 125.707715034[s]
+2016-08-29 18:31:41,723 DEBUG: Done: Classification
+2016-08-29 18:31:43,162 DEBUG: Start: Statistic Results
+2016-08-29 18:31:43,162 INFO: Accuracy :0.619047619048
+2016-08-29 18:31:43,194 DEBUG: ### Main Programm for Classification MonoView
+2016-08-29 18:31:43,194 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-29 18:31:43,194 DEBUG: Start: Determine Train/Test split
+2016-08-29 18:31:43,228 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-29 18:31:43,228 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-29 18:31:43,228 DEBUG: Done: Determine Train/Test split
+2016-08-29 18:31:43,228 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-100943-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-100943-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..43a7698b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-100943-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1 @@
+2016-08-30 10:09:43,739 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-101446-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-101446-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..5bee1cc1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-101446-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-30 10:14:46,438 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 10:14:46,474 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:14:46,474 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 10:14:46,474 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:14:46,502 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 10:14:46,502 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 10:14:46,503 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:14:46,503 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-101634-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-101634-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..556e2502
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-101634-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,327 @@
+2016-08-30 10:16:34,609 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 10:16:34,621 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:16:34,621 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 10:16:34,621 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:16:34,639 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 10:16:34,639 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 10:16:34,639 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:16:34,639 DEBUG: Start: Classification
+2016-08-30 10:16:44,776 DEBUG: Info: Time for Classification: 10.1228818893[s]
+2016-08-30 10:16:44,776 DEBUG: Done: Classification
+2016-08-30 10:16:44,803 DEBUG: Start: Statistic Results
+2016-08-30 10:16:44,803 INFO: Accuracy :0.8
+2016-08-30 10:16:44,815 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:16:44,815 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-30 10:16:44,815 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:16:44,827 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 10:16:44,827 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 10:16:44,827 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:16:44,827 DEBUG: Start: Classification
+2016-08-30 10:16:54,553 DEBUG: Info: Time for Classification: 9.74853897095[s]
+2016-08-30 10:16:54,553 DEBUG: Done: Classification
+2016-08-30 10:16:54,556 DEBUG: Start: Statistic Results
+2016-08-30 10:16:54,557 INFO: Accuracy :0.790476190476
+2016-08-30 10:16:54,566 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:16:54,566 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-30 10:16:54,566 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:16:54,578 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 10:16:54,578 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 10:16:54,578 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:16:54,578 DEBUG: Start: Classification
+2016-08-30 10:16:57,460 DEBUG: Info: Time for Classification: 2.90179514885[s]
+2016-08-30 10:16:57,460 DEBUG: Done: Classification
+2016-08-30 10:16:58,781 DEBUG: Start: Statistic Results
+2016-08-30 10:16:58,781 INFO: Accuracy :0.8
+2016-08-30 10:16:58,796 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:16:58,797 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-30 10:16:58,797 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:16:58,809 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 10:16:58,809 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 10:16:58,809 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:16:58,809 DEBUG: Start: Classification
+2016-08-30 10:16:59,325 DEBUG: Info: Time for Classification: 0.53910112381[s]
+2016-08-30 10:16:59,325 DEBUG: Done: Classification
+2016-08-30 10:16:59,329 DEBUG: Start: Statistic Results
+2016-08-30 10:16:59,330 INFO: Accuracy :0.761904761905
+2016-08-30 10:16:59,342 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:16:59,342 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-30 10:16:59,343 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:16:59,358 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 10:16:59,358 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 10:16:59,358 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:16:59,358 DEBUG: Start: Classification
+2016-08-30 10:17:00,543 DEBUG: Info: Time for Classification: 1.21113300323[s]
+2016-08-30 10:17:00,543 DEBUG: Done: Classification
+2016-08-30 10:17:00,554 DEBUG: Start: Statistic Results
+2016-08-30 10:17:00,554 INFO: Accuracy :0.714285714286
+2016-08-30 10:17:00,569 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:00,570 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-30 10:17:00,570 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:00,588 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 10:17:00,588 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 10:17:00,588 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:00,588 DEBUG: Start: Classification
+2016-08-30 10:17:08,388 DEBUG: Info: Time for Classification: 7.83058905602[s]
+2016-08-30 10:17:08,389 DEBUG: Done: Classification
+2016-08-30 10:17:08,689 DEBUG: Start: Statistic Results
+2016-08-30 10:17:08,690 INFO: Accuracy :0.87619047619
+2016-08-30 10:17:08,703 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:08,704 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-30 10:17:08,704 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:08,716 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 10:17:08,716 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 10:17:08,716 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:08,716 DEBUG: Start: Classification
+2016-08-30 10:17:16,673 DEBUG: Info: Time for Classification: 7.98002386093[s]
+2016-08-30 10:17:16,673 DEBUG: Done: Classification
+2016-08-30 10:17:17,017 DEBUG: Start: Statistic Results
+2016-08-30 10:17:17,017 INFO: Accuracy :0.87619047619
+2016-08-30 10:17:17,031 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:17,031 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Methyl_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-08-30 10:17:17,031 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:17,044 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 10:17:17,044 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 10:17:17,044 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:17,044 DEBUG: Start: Classification
+2016-08-30 10:17:24,297 DEBUG: Info: Time for Classification: 7.27610015869[s]
+2016-08-30 10:17:24,297 DEBUG: Done: Classification
+2016-08-30 10:17:24,592 DEBUG: Start: Statistic Results
+2016-08-30 10:17:24,593 INFO: Accuracy :0.866666666667
+2016-08-30 10:17:24,616 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:24,616 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 10:17:24,617 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:24,617 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 10:17:24,617 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 10:17:24,618 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:24,618 DEBUG: Start: Classification
+2016-08-30 10:17:24,892 DEBUG: Info: Time for Classification: 0.294700860977[s]
+2016-08-30 10:17:24,892 DEBUG: Done: Classification
+2016-08-30 10:17:24,893 DEBUG: Start: Statistic Results
+2016-08-30 10:17:24,894 INFO: Accuracy :0.790476190476
+2016-08-30 10:17:24,895 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:24,895 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-30 10:17:24,895 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:24,896 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 10:17:24,896 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 10:17:24,896 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:24,896 DEBUG: Start: Classification
+2016-08-30 10:17:24,985 DEBUG: Info: Time for Classification: 0.0903129577637[s]
+2016-08-30 10:17:24,985 DEBUG: Done: Classification
+2016-08-30 10:17:24,987 DEBUG: Start: Statistic Results
+2016-08-30 10:17:24,987 INFO: Accuracy :0.819047619048
+2016-08-30 10:17:24,988 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:24,988 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-30 10:17:24,988 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:24,989 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 10:17:24,989 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 10:17:24,989 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:24,989 DEBUG: Start: Classification
+2016-08-30 10:17:25,103 DEBUG: Info: Time for Classification: 0.115062952042[s]
+2016-08-30 10:17:25,103 DEBUG: Done: Classification
+2016-08-30 10:17:25,148 DEBUG: Start: Statistic Results
+2016-08-30 10:17:25,149 INFO: Accuracy :0.72380952381
+2016-08-30 10:17:25,150 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:25,150 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-30 10:17:25,150 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:25,151 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 10:17:25,151 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 10:17:25,151 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:25,151 DEBUG: Start: Classification
+2016-08-30 10:17:25,713 DEBUG: Info: Time for Classification: 0.563421010971[s]
+2016-08-30 10:17:25,713 DEBUG: Done: Classification
+2016-08-30 10:17:25,717 DEBUG: Start: Statistic Results
+2016-08-30 10:17:25,717 INFO: Accuracy :0.857142857143
+2016-08-30 10:17:25,719 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:25,719 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-30 10:17:25,719 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:25,720 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 10:17:25,720 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 10:17:25,720 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:25,720 DEBUG: Start: Classification
+2016-08-30 10:17:25,800 DEBUG: Info: Time for Classification: 0.0812940597534[s]
+2016-08-30 10:17:25,800 DEBUG: Done: Classification
+2016-08-30 10:17:25,802 DEBUG: Start: Statistic Results
+2016-08-30 10:17:25,803 INFO: Accuracy :0.666666666667
+2016-08-30 10:17:25,804 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:25,804 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-30 10:17:25,805 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:25,806 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 10:17:25,806 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 10:17:25,806 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:25,806 DEBUG: Start: Classification
+2016-08-30 10:17:44,549 DEBUG: Info: Time for Classification: 18.7451300621[s]
+2016-08-30 10:17:44,549 DEBUG: Done: Classification
+2016-08-30 10:17:44,558 DEBUG: Start: Statistic Results
+2016-08-30 10:17:44,558 INFO: Accuracy :0.8
+2016-08-30 10:17:44,560 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:44,560 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-30 10:17:44,560 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:44,561 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 10:17:44,561 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 10:17:44,561 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:44,561 DEBUG: Start: Classification
+2016-08-30 10:17:44,604 DEBUG: Info: Time for Classification: 0.0441439151764[s]
+2016-08-30 10:17:44,604 DEBUG: Done: Classification
+2016-08-30 10:17:44,605 DEBUG: Start: Statistic Results
+2016-08-30 10:17:44,606 INFO: Accuracy :0.304761904762
+2016-08-30 10:17:44,607 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:44,607 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA__ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-08-30 10:17:44,607 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:44,608 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 10:17:44,608 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 10:17:44,608 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:44,608 DEBUG: Start: Classification
+2016-08-30 10:17:45,263 DEBUG: Info: Time for Classification: 0.656535148621[s]
+2016-08-30 10:17:45,264 DEBUG: Done: Classification
+2016-08-30 10:17:45,292 DEBUG: Start: Statistic Results
+2016-08-30 10:17:45,292 INFO: Accuracy :0.771428571429
+2016-08-30 10:17:46,418 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:17:46,418 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 10:17:46,419 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:17:46,523 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-30 10:17:46,523 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-30 10:17:46,523 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:17:46,523 DEBUG: Start: Classification
+2016-08-30 10:18:37,100 DEBUG: Info: Time for Classification: 51.806524992[s]
+2016-08-30 10:18:37,100 DEBUG: Done: Classification
+2016-08-30 10:18:37,111 DEBUG: Start: Statistic Results
+2016-08-30 10:18:37,111 INFO: Accuracy :0.657142857143
+2016-08-30 10:18:37,299 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:18:37,299 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-30 10:18:37,299 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:18:37,365 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-30 10:18:37,365 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-30 10:18:37,365 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:18:37,365 DEBUG: Start: Classification
+2016-08-30 10:18:45,045 DEBUG: Info: Time for Classification: 7.92616009712[s]
+2016-08-30 10:18:45,045 DEBUG: Done: Classification
+2016-08-30 10:18:45,051 DEBUG: Start: Statistic Results
+2016-08-30 10:18:45,051 INFO: Accuracy :0.666666666667
+2016-08-30 10:18:45,093 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:18:45,093 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-30 10:18:45,094 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:18:45,147 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-30 10:18:45,147 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-30 10:18:45,147 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:18:45,147 DEBUG: Start: Classification
+2016-08-30 10:18:53,751 DEBUG: Info: Time for Classification: 8.69405412674[s]
+2016-08-30 10:18:53,752 DEBUG: Done: Classification
+2016-08-30 10:18:57,272 DEBUG: Start: Statistic Results
+2016-08-30 10:18:57,272 INFO: Accuracy :0.733333333333
+2016-08-30 10:18:57,324 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:18:57,324 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-30 10:18:57,324 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:18:57,389 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-30 10:18:57,390 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-30 10:18:57,390 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:18:57,390 DEBUG: Start: Classification
+2016-08-30 10:18:57,934 DEBUG: Info: Time for Classification: 0.645569086075[s]
+2016-08-30 10:18:57,934 DEBUG: Done: Classification
+2016-08-30 10:18:57,940 DEBUG: Start: Statistic Results
+2016-08-30 10:18:57,940 INFO: Accuracy :0.514285714286
+2016-08-30 10:18:57,982 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:18:57,982 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-30 10:18:57,982 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:18:58,035 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-30 10:18:58,035 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-30 10:18:58,035 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:18:58,035 DEBUG: Start: Classification
+2016-08-30 10:19:05,580 DEBUG: Info: Time for Classification: 7.63366723061[s]
+2016-08-30 10:19:05,580 DEBUG: Done: Classification
+2016-08-30 10:19:05,626 DEBUG: Start: Statistic Results
+2016-08-30 10:19:05,626 INFO: Accuracy :0.552380952381
+2016-08-30 10:19:05,671 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:19:05,671 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-30 10:19:05,671 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:19:05,730 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-30 10:19:05,730 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-30 10:19:05,730 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:19:05,730 DEBUG: Start: Classification
+2016-08-30 10:19:50,442 DEBUG: Info: Time for Classification: 44.8092310429[s]
+2016-08-30 10:19:50,442 DEBUG: Done: Classification
+2016-08-30 10:19:52,058 DEBUG: Start: Statistic Results
+2016-08-30 10:19:52,058 INFO: Accuracy :0.647619047619
+2016-08-30 10:19:52,098 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:19:52,099 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-30 10:19:52,099 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:19:52,135 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-30 10:19:52,135 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-30 10:19:52,135 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:19:52,135 DEBUG: Start: Classification
+2016-08-30 10:19:53,501 DEBUG: Info: Time for Classification: 1.43281602859[s]
+2016-08-30 10:19:53,501 DEBUG: Done: Classification
+2016-08-30 10:19:53,538 DEBUG: Start: Statistic Results
+2016-08-30 10:19:53,538 INFO: Accuracy :0.257142857143
+2016-08-30 10:19:53,568 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:19:53,568 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-08-30 10:19:53,568 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:19:53,602 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-30 10:19:53,602 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-30 10:19:53,602 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:19:53,602 DEBUG: Start: Classification
+2016-08-30 10:20:40,756 DEBUG: Info: Time for Classification: 47.2164580822[s]
+2016-08-30 10:20:40,756 DEBUG: Done: Classification
+2016-08-30 10:20:42,788 DEBUG: Start: Statistic Results
+2016-08-30 10:20:42,789 INFO: Accuracy :0.761904761905
+2016-08-30 10:20:42,817 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:20:42,818 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 10:20:42,818 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:20:42,818 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-08-30 10:20:42,818 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-08-30 10:20:42,818 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:20:42,818 DEBUG: Start: Classification
+2016-08-30 10:20:42,868 DEBUG: Info: Time for Classification: 0.0714671611786[s]
+2016-08-30 10:20:42,869 DEBUG: Done: Classification
+2016-08-30 10:20:42,870 DEBUG: Start: Statistic Results
+2016-08-30 10:20:42,870 INFO: Accuracy :0.771428571429
+2016-08-30 10:20:42,871 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:20:42,871 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-30 10:20:42,872 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:20:42,872 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-08-30 10:20:42,872 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-08-30 10:20:42,872 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:20:42,872 DEBUG: Start: Classification
+2016-08-30 10:20:42,905 DEBUG: Info: Time for Classification: 0.0336558818817[s]
+2016-08-30 10:20:42,905 DEBUG: Done: Classification
+2016-08-30 10:20:42,907 DEBUG: Start: Statistic Results
+2016-08-30 10:20:42,907 INFO: Accuracy :0.847619047619
+2016-08-30 10:20:42,908 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:20:42,908 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-30 10:20:42,908 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:20:42,908 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-08-30 10:20:42,909 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-08-30 10:20:42,909 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:20:42,909 DEBUG: Start: Classification
+2016-08-30 10:20:42,946 DEBUG: Info: Time for Classification: 0.0375559329987[s]
+2016-08-30 10:20:42,946 DEBUG: Done: Classification
+2016-08-30 10:20:42,953 DEBUG: Start: Statistic Results
+2016-08-30 10:20:42,953 INFO: Accuracy :0.704761904762
+2016-08-30 10:20:42,954 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:20:42,954 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-30 10:20:42,954 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:20:42,955 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-08-30 10:20:42,955 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-08-30 10:20:42,955 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:20:42,955 DEBUG: Start: Classification
+2016-08-30 10:20:43,489 DEBUG: Info: Time for Classification: 0.53512597084[s]
+2016-08-30 10:20:43,489 DEBUG: Done: Classification
+2016-08-30 10:20:43,493 DEBUG: Start: Statistic Results
+2016-08-30 10:20:43,494 INFO: Accuracy :0.790476190476
+2016-08-30 10:20:43,495 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:20:43,495 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-30 10:20:43,495 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:20:43,495 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-08-30 10:20:43,495 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-08-30 10:20:43,495 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:20:43,495 DEBUG: Start: Classification
+2016-08-30 10:20:43,539 DEBUG: Info: Time for Classification: 0.0446429252625[s]
+2016-08-30 10:20:43,539 DEBUG: Done: Classification
+2016-08-30 10:20:43,541 DEBUG: Start: Statistic Results
+2016-08-30 10:20:43,541 INFO: Accuracy :0.609523809524
+2016-08-30 10:20:43,542 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 10:20:43,542 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-30 10:20:43,542 DEBUG: Start: Determine Train/Test split
+2016-08-30 10:20:43,543 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-08-30 10:20:43,543 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-08-30 10:20:43,543 DEBUG: Done: Determine Train/Test split
+2016-08-30 10:20:43,543 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-102454-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-102454-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..6eb1779d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-102454-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,21 @@
+2016-08-30 10:24:54,277 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 10:24:54,280 INFO: ### Main Programm for Multiview Classification
+2016-08-30 10:24:54,280 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-08-30 10:24:54,280 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-30 10:24:54,281 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-30 10:24:54,281 INFO: Info: Shape of RNASeq_ :(347, 73599)
+2016-08-30 10:24:54,282 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-30 10:24:54,282 INFO: Done: Read Database Files
+2016-08-30 10:24:54,282 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 10:24:54,286 INFO: Done: Determine validation split
+2016-08-30 10:24:54,286 INFO: Start: Determine 5 folds
+2016-08-30 10:24:54,294 INFO: Info: Length of Learning Sets: 196
+2016-08-30 10:24:54,294 INFO: Info: Length of Testing Sets: 48
+2016-08-30 10:24:54,294 INFO: Info: Length of Validation Set: 103
+2016-08-30 10:24:54,294 INFO: Done: Determine folds
+2016-08-30 10:24:54,294 INFO: Start: Learning with Mumbo and 5 folds
+2016-08-30 10:24:54,294 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-30 10:24:54,295 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-30 10:24:58,093 DEBUG: Info: Best Reslut : 0.515409836066
+2016-08-30 10:24:58,093 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:24:58,095 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-102653-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-102653-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..79a51492
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-102653-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,18 @@
+2016-08-30 10:26:53,326 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 10:26:53,329 INFO: ### Main Programm for Multiview Classification
+2016-08-30 10:26:53,330 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-08-30 10:26:53,330 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-30 10:26:53,331 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-30 10:26:53,331 INFO: Info: Shape of RNASeq_ :(347, 73599)
+2016-08-30 10:26:53,332 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-30 10:26:53,332 INFO: Done: Read Database Files
+2016-08-30 10:26:53,332 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 10:26:53,338 INFO: Done: Determine validation split
+2016-08-30 10:26:53,338 INFO: Start: Determine 5 folds
+2016-08-30 10:26:53,349 INFO: Info: Length of Learning Sets: 196
+2016-08-30 10:26:53,349 INFO: Info: Length of Testing Sets: 48
+2016-08-30 10:26:53,349 INFO: Info: Length of Validation Set: 103
+2016-08-30 10:26:53,349 INFO: Done: Determine folds
+2016-08-30 10:26:53,350 INFO: Start: Learning with Mumbo and 5 folds
+2016-08-30 10:26:53,350 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-30 10:26:53,350 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-102706-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-102706-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..b2f8bb9e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-102706-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,36 @@
+2016-08-30 10:27:06,826 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 10:27:06,829 INFO: ### Main Programm for Multiview Classification
+2016-08-30 10:27:06,829 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-08-30 10:27:06,829 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-30 10:27:06,830 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-30 10:27:06,830 INFO: Info: Shape of RNASeq_ :(347, 73599)
+2016-08-30 10:27:06,830 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-30 10:27:06,831 INFO: Done: Read Database Files
+2016-08-30 10:27:06,831 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 10:27:06,834 INFO: Done: Determine validation split
+2016-08-30 10:27:06,834 INFO: Start: Determine 5 folds
+2016-08-30 10:27:06,842 INFO: Info: Length of Learning Sets: 196
+2016-08-30 10:27:06,842 INFO: Info: Length of Testing Sets: 48
+2016-08-30 10:27:06,842 INFO: Info: Length of Validation Set: 103
+2016-08-30 10:27:06,842 INFO: Done: Determine folds
+2016-08-30 10:27:06,842 INFO: Start: Learning with Mumbo and 5 folds
+2016-08-30 10:27:06,842 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-30 10:27:06,843 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
+2016-08-30 10:27:10,624 DEBUG: Info: Best Reslut : 0.508606557377
+2016-08-30 10:27:10,626 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:27:10,626 DEBUG: Start: Gridsearch for DecisionTree on MiRNA__
+2016-08-30 10:27:12,016 DEBUG: Info: Best Reslut : 0.514262295082
+2016-08-30 10:27:12,016 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:27:12,017 DEBUG: Start: Gridsearch for DecisionTree on RNASeq_
+2016-08-30 10:27:19,883 DEBUG: Info: Best Reslut : 0.505
+2016-08-30 10:27:19,886 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:27:19,886 DEBUG: Start: Gridsearch for DecisionTree on Clinic_
+2016-08-30 10:27:21,594 DEBUG: Info: Best Reslut : 0.58762295082
+2016-08-30 10:27:21,594 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:27:21,594 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-30 10:27:21,595 INFO: Start: Fold number 1
+2016-08-30 10:27:23,833 DEBUG: Start: Iteration 1
+2016-08-30 10:27:23,868 DEBUG: View 0 : 0.566820276498
+2016-08-30 10:27:23,878 DEBUG: View 1 : 0.63133640553
+2016-08-30 10:27:23,931 DEBUG: View 2 : 0.63133640553
+2016-08-30 10:27:23,943 DEBUG: View 3 : 0.36866359447
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-102823-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-102823-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
new file mode 100644
index 00000000..f52649ae
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-102823-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log
@@ -0,0 +1,18 @@
+2016-08-30 10:28:23,860 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 10:28:23,862 INFO: ### Main Programm for Multiview Classification
+2016-08-30 10:28:23,862 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-08-30 10:28:23,863 INFO: Info: Shape of Methyl_ :(347, 25978)
+2016-08-30 10:28:23,863 INFO: Info: Shape of MiRNA__ :(347, 1046)
+2016-08-30 10:28:23,864 INFO: Info: Shape of RNASeq_ :(347, 73599)
+2016-08-30 10:28:23,864 INFO: Info: Shape of Clinic_ :(347, 127)
+2016-08-30 10:28:23,864 INFO: Done: Read Database Files
+2016-08-30 10:28:23,864 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 10:28:23,868 INFO: Done: Determine validation split
+2016-08-30 10:28:23,868 INFO: Start: Determine 5 folds
+2016-08-30 10:28:23,876 INFO: Info: Length of Learning Sets: 196
+2016-08-30 10:28:23,876 INFO: Info: Length of Testing Sets: 48
+2016-08-30 10:28:23,876 INFO: Info: Length of Validation Set: 103
+2016-08-30 10:28:23,876 INFO: Done: Determine folds
+2016-08-30 10:28:23,876 INFO: Start: Learning with Mumbo and 5 folds
+2016-08-30 10:28:23,876 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-30 10:28:23,877 DEBUG: Start: Gridsearch for DecisionTree on Methyl_
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-102929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-102929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..1a0f4d80
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-102929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,351 @@
+2016-08-30 10:29:29,871 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 10:29:29,873 INFO: ### Main Programm for Multiview Classification
+2016-08-30 10:29:29,874 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-08-30 10:29:29,874 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 10:29:29,875 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 10:29:29,875 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 10:29:29,876 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 10:29:29,876 INFO: Done: Read Database Files
+2016-08-30 10:29:29,876 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 10:29:29,880 INFO: Done: Determine validation split
+2016-08-30 10:29:29,881 INFO: Start: Determine 5 folds
+2016-08-30 10:29:29,888 INFO: Info: Length of Learning Sets: 196
+2016-08-30 10:29:29,888 INFO: Info: Length of Testing Sets: 48
+2016-08-30 10:29:29,889 INFO: Info: Length of Validation Set: 103
+2016-08-30 10:29:29,889 INFO: Done: Determine folds
+2016-08-30 10:29:29,889 INFO: Start: Learning with Mumbo and 5 folds
+2016-08-30 10:29:29,889 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-30 10:29:29,889 DEBUG: Start: Gridsearch for DecisionTree on Methyl
+2016-08-30 10:29:33,972 DEBUG: Info: Best Reslut : 0.542131147541
+2016-08-30 10:29:33,973 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:29:33,973 DEBUG: Start: Gridsearch for DecisionTree on MiRNA_
+2016-08-30 10:29:35,385 DEBUG: Info: Best Reslut : 0.518278688525
+2016-08-30 10:29:35,385 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:29:35,386 DEBUG: Start: Gridsearch for DecisionTree on RANSeq
+2016-08-30 10:29:44,374 DEBUG: Info: Best Reslut : 0.530163934426
+2016-08-30 10:29:44,377 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:29:44,378 DEBUG: Start: Gridsearch for DecisionTree on Clinic
+2016-08-30 10:29:46,222 DEBUG: Info: Best Reslut : 0.564016393443
+2016-08-30 10:29:46,222 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:29:46,222 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-30 10:29:46,222 INFO: Start: Fold number 1
+2016-08-30 10:29:48,537 DEBUG: Start: Iteration 1
+2016-08-30 10:29:48,675 DEBUG: View 0 : 0.617224880383
+2016-08-30 10:29:48,685 DEBUG: View 1 : 0.617224880383
+2016-08-30 10:29:49,169 DEBUG: View 2 : 0.516746411483
+2016-08-30 10:29:49,180 DEBUG: View 3 : 0.483253588517
+2016-08-30 10:29:49,237 DEBUG: Best view : Methyl
+2016-08-30 10:29:49,338 DEBUG: Start: Iteration 2
+2016-08-30 10:29:49,359 DEBUG: View 0 : 0.459330143541
+2016-08-30 10:29:49,369 DEBUG: View 1 : 0.454545454545
+2016-08-30 10:29:49,429 DEBUG: View 2 : 0.607655502392
+2016-08-30 10:29:49,440 DEBUG: View 3 : 0.497607655502
+2016-08-30 10:29:49,513 DEBUG: Best view : RANSeq
+2016-08-30 10:29:49,721 DEBUG: Start: Iteration 3
+2016-08-30 10:29:49,741 DEBUG: View 0 : 0.473684210526
+2016-08-30 10:29:49,751 DEBUG: View 1 : 0.55023923445
+2016-08-30 10:29:49,811 DEBUG: View 2 : 0.421052631579
+2016-08-30 10:29:49,823 DEBUG: View 3 : 0.516746411483
+2016-08-30 10:29:49,896 DEBUG: Best view : MiRNA_
+2016-08-30 10:29:50,175 DEBUG: Start: Iteration 4
+2016-08-30 10:29:50,196 DEBUG: View 0 : 0.626794258373
+2016-08-30 10:29:50,206 DEBUG: View 1 : 0.631578947368
+2016-08-30 10:29:50,266 DEBUG: View 2 : 0.488038277512
+2016-08-30 10:29:50,279 DEBUG: View 3 : 0.535885167464
+2016-08-30 10:29:50,356 DEBUG: Best view : MiRNA_
+2016-08-30 10:29:50,716 DEBUG: Start: Iteration 5
+2016-08-30 10:29:50,737 DEBUG: View 0 : 0.516746411483
+2016-08-30 10:29:50,746 DEBUG: View 1 : 0.416267942584
+2016-08-30 10:29:50,817 DEBUG: View 2 : 0.397129186603
+2016-08-30 10:29:50,829 DEBUG: View 3 : 0.602870813397
+2016-08-30 10:29:50,907 DEBUG: Best view : Clinic
+2016-08-30 10:29:51,341 DEBUG: Start: Iteration 6
+2016-08-30 10:29:51,363 DEBUG: View 0 : 0.416267942584
+2016-08-30 10:29:51,373 DEBUG: View 1 : 0.717703349282
+2016-08-30 10:29:51,436 DEBUG: View 2 : 0.545454545455
+2016-08-30 10:29:51,448 DEBUG: View 3 : 0.583732057416
+2016-08-30 10:29:51,532 DEBUG: Best view : MiRNA_
+2016-08-30 10:29:52,048 DEBUG: Start: Iteration 7
+2016-08-30 10:29:52,068 DEBUG: View 0 : 0.344497607656
+2016-08-30 10:29:52,078 DEBUG: View 1 : 0.645933014354
+2016-08-30 10:29:52,136 DEBUG: View 2 : 0.502392344498
+2016-08-30 10:29:52,147 DEBUG: View 3 : 0.507177033493
+2016-08-30 10:29:52,234 DEBUG: Best view : MiRNA_
+2016-08-30 10:29:52,819 DEBUG: Start: Iteration 8
+2016-08-30 10:29:52,840 DEBUG: View 0 : 0.421052631579
+2016-08-30 10:29:52,850 DEBUG: View 1 : 0.478468899522
+2016-08-30 10:29:52,909 DEBUG: View 2 : 0.545454545455
+2016-08-30 10:29:52,920 DEBUG: View 3 : 0.602870813397
+2016-08-30 10:29:53,010 DEBUG: Best view : Clinic
+2016-08-30 10:29:53,681 DEBUG: Start: Iteration 9
+2016-08-30 10:29:53,701 DEBUG: View 0 : 0.488038277512
+2016-08-30 10:29:53,711 DEBUG: View 1 : 0.406698564593
+2016-08-30 10:29:53,769 DEBUG: View 2 : 0.540669856459
+2016-08-30 10:29:53,781 DEBUG: View 3 : 0.488038277512
+2016-08-30 10:29:53,879 DEBUG: Best view : RANSeq
+2016-08-30 10:29:54,639 DEBUG: Start: Iteration 10
+2016-08-30 10:29:54,659 DEBUG: View 0 : 0.430622009569
+2016-08-30 10:29:54,669 DEBUG: View 1 : 0.674641148325
+2016-08-30 10:29:54,729 DEBUG: View 2 : 0.655502392344
+2016-08-30 10:29:54,742 DEBUG: View 3 : 0.382775119617
+2016-08-30 10:29:54,840 DEBUG: Best view : MiRNA_
+2016-08-30 10:29:55,684 DEBUG: Start: Iteration 11
+2016-08-30 10:29:55,705 DEBUG: View 0 : 0.397129186603
+2016-08-30 10:29:55,715 DEBUG: View 1 : 0.626794258373
+2016-08-30 10:29:55,776 DEBUG: View 2 : 0.564593301435
+2016-08-30 10:29:55,789 DEBUG: View 3 : 0.430622009569
+2016-08-30 10:29:55,893 DEBUG: Best view : MiRNA_
+2016-08-30 10:29:56,812 DEBUG: Start: Iteration 12
+2016-08-30 10:29:56,832 DEBUG: View 0 : 0.645933014354
+2016-08-30 10:29:56,842 DEBUG: View 1 : 0.392344497608
+2016-08-30 10:29:56,901 DEBUG: View 2 : 0.526315789474
+2016-08-30 10:29:56,912 DEBUG: View 3 : 0.598086124402
+2016-08-30 10:29:57,017 DEBUG: Best view : Methyl
+2016-08-30 10:29:58,014 DEBUG: Start: Iteration 13
+2016-08-30 10:29:58,035 DEBUG: View 0 : 0.679425837321
+2016-08-30 10:29:58,046 DEBUG: View 1 : 0.708133971292
+2016-08-30 10:29:58,104 DEBUG: View 2 : 0.55023923445
+2016-08-30 10:29:58,115 DEBUG: View 3 : 0.574162679426
+2016-08-30 10:29:58,223 DEBUG: Best view : MiRNA_
+2016-08-30 10:29:59,301 DEBUG: Start: Iteration 14
+2016-08-30 10:29:59,322 DEBUG: View 0 : 0.444976076555
+2016-08-30 10:29:59,332 DEBUG: View 1 : 0.693779904306
+2016-08-30 10:29:59,391 DEBUG: View 2 : 0.531100478469
+2016-08-30 10:29:59,402 DEBUG: View 3 : 0.574162679426
+2016-08-30 10:29:59,512 DEBUG: Best view : MiRNA_
+2016-08-30 10:30:00,668 DEBUG: Start: Iteration 15
+2016-08-30 10:30:00,688 DEBUG: View 0 : 0.535885167464
+2016-08-30 10:30:00,698 DEBUG: View 1 : 0.574162679426
+2016-08-30 10:30:00,758 DEBUG: View 2 : 0.569377990431
+2016-08-30 10:30:00,770 DEBUG: View 3 : 0.44976076555
+2016-08-30 10:30:00,886 DEBUG: Best view : MiRNA_
+2016-08-30 10:30:02,118 DEBUG: Start: Iteration 16
+2016-08-30 10:30:02,138 DEBUG: View 0 : 0.626794258373
+2016-08-30 10:30:02,148 DEBUG: View 1 : 0.598086124402
+2016-08-30 10:30:02,207 DEBUG: View 2 : 0.488038277512
+2016-08-30 10:30:02,219 DEBUG: View 3 : 0.526315789474
+2016-08-30 10:30:02,338 DEBUG: Best view : Methyl
+2016-08-30 10:30:03,646 DEBUG: Start: Iteration 17
+2016-08-30 10:30:03,667 DEBUG: View 0 : 0.684210526316
+2016-08-30 10:30:03,676 DEBUG: View 1 : 0.583732057416
+2016-08-30 10:30:03,736 DEBUG: View 2 : 0.473684210526
+2016-08-30 10:30:03,747 DEBUG: View 3 : 0.502392344498
+2016-08-30 10:30:03,871 DEBUG: Best view : Methyl
+2016-08-30 10:30:05,259 DEBUG: Start: Iteration 18
+2016-08-30 10:30:05,279 DEBUG: View 0 : 0.535885167464
+2016-08-30 10:30:05,290 DEBUG: View 1 : 0.473684210526
+2016-08-30 10:30:05,350 DEBUG: View 2 : 0.464114832536
+2016-08-30 10:30:05,362 DEBUG: View 3 : 0.574162679426
+2016-08-30 10:30:05,488 DEBUG: Best view : Clinic
+2016-08-30 10:30:06,969 DEBUG: Start: Iteration 19
+2016-08-30 10:30:06,991 DEBUG: View 0 : 0.492822966507
+2016-08-30 10:30:07,002 DEBUG: View 1 : 0.435406698565
+2016-08-30 10:30:07,061 DEBUG: View 2 : 0.488038277512
+2016-08-30 10:30:07,073 DEBUG: View 3 : 0.535885167464
+2016-08-30 10:30:07,200 DEBUG: Best view : Clinic
+2016-08-30 10:30:08,747 DEBUG: Start: Iteration 20
+2016-08-30 10:30:08,767 DEBUG: View 0 : 0.55023923445
+2016-08-30 10:30:08,777 DEBUG: View 1 : 0.693779904306
+2016-08-30 10:30:08,836 DEBUG: View 2 : 0.569377990431
+2016-08-30 10:30:08,847 DEBUG: View 3 : 0.531100478469
+2016-08-30 10:30:08,979 DEBUG: Best view : MiRNA_
+2016-08-30 10:30:10,609 DEBUG: Start: Iteration 21
+2016-08-30 10:30:10,629 DEBUG: View 0 : 0.583732057416
+2016-08-30 10:30:10,639 DEBUG: View 1 : 0.650717703349
+2016-08-30 10:30:10,698 DEBUG: View 2 : 0.397129186603
+2016-08-30 10:30:10,710 DEBUG: View 3 : 0.478468899522
+2016-08-30 10:30:10,854 DEBUG: Best view : MiRNA_
+2016-08-30 10:30:12,610 DEBUG: Start: Iteration 22
+2016-08-30 10:30:12,630 DEBUG: View 0 : 0.765550239234
+2016-08-30 10:30:12,640 DEBUG: View 1 : 0.44019138756
+2016-08-30 10:30:12,700 DEBUG: View 2 : 0.354066985646
+2016-08-30 10:30:12,712 DEBUG: View 3 : 0.526315789474
+2016-08-30 10:30:12,853 DEBUG: Best view : Methyl
+2016-08-30 10:30:14,721 DEBUG: Start: Iteration 23
+2016-08-30 10:30:14,742 DEBUG: View 0 : 0.497607655502
+2016-08-30 10:30:14,752 DEBUG: View 1 : 0.593301435407
+2016-08-30 10:30:14,811 DEBUG: View 2 : 0.521531100478
+2016-08-30 10:30:14,823 DEBUG: View 3 : 0.521531100478
+2016-08-30 10:30:14,970 DEBUG: Best view : MiRNA_
+2016-08-30 10:30:16,861 DEBUG: Start: Iteration 24
+2016-08-30 10:30:16,881 DEBUG: View 0 : 0.492822966507
+2016-08-30 10:30:16,891 DEBUG: View 1 : 0.416267942584
+2016-08-30 10:30:16,951 DEBUG: View 2 : 0.545454545455
+2016-08-30 10:30:16,963 DEBUG: View 3 : 0.612440191388
+2016-08-30 10:30:17,109 DEBUG: Best view : Clinic
+2016-08-30 10:30:19,363 DEBUG: Start: Iteration 25
+2016-08-30 10:30:19,384 DEBUG: View 0 : 0.511961722488
+2016-08-30 10:30:19,394 DEBUG: View 1 : 0.425837320574
+2016-08-30 10:30:19,455 DEBUG: View 2 : 0.540669856459
+2016-08-30 10:30:19,467 DEBUG: View 3 : 0.401913875598
+2016-08-30 10:30:19,614 DEBUG: Best view : RANSeq
+2016-08-30 10:30:21,662 DEBUG: Start: Iteration 26
+2016-08-30 10:30:21,683 DEBUG: View 0 : 0.411483253589
+2016-08-30 10:30:21,692 DEBUG: View 1 : 0.425837320574
+2016-08-30 10:30:21,752 DEBUG: View 2 : 0.454545454545
+2016-08-30 10:30:21,764 DEBUG: View 3 : 0.521531100478
+2016-08-30 10:30:21,917 DEBUG: Best view : Clinic
+2016-08-30 10:30:24,029 DEBUG: Start: Iteration 27
+2016-08-30 10:30:24,049 DEBUG: View 0 : 0.598086124402
+2016-08-30 10:30:24,059 DEBUG: View 1 : 0.674641148325
+2016-08-30 10:30:24,119 DEBUG: View 2 : 0.665071770335
+2016-08-30 10:30:24,131 DEBUG: View 3 : 0.55980861244
+2016-08-30 10:30:24,288 DEBUG: Best view : RANSeq
+2016-08-30 10:30:26,547 DEBUG: Start: Iteration 28
+2016-08-30 10:30:26,567 DEBUG: View 0 : 0.44019138756
+2016-08-30 10:30:26,577 DEBUG: View 1 : 0.430622009569
+2016-08-30 10:30:26,637 DEBUG: View 2 : 0.44976076555
+2016-08-30 10:30:26,649 DEBUG: View 3 : 0.387559808612
+2016-08-30 10:30:26,650 WARNING: WARNING: All bad for iteration 27
+2016-08-30 10:30:26,813 DEBUG: Best view : RANSeq
+2016-08-30 10:30:29,246 DEBUG: Start: Iteration 29
+2016-08-30 10:30:29,269 DEBUG: View 0 : 0.492822966507
+2016-08-30 10:30:29,279 DEBUG: View 1 : 0.33971291866
+2016-08-30 10:30:29,348 DEBUG: View 2 : 0.602870813397
+2016-08-30 10:30:29,362 DEBUG: View 3 : 0.478468899522
+2016-08-30 10:30:29,536 DEBUG: Best view : RANSeq
+2016-08-30 10:30:32,153 DEBUG: Start: Iteration 30
+2016-08-30 10:30:32,173 DEBUG: View 0 : 0.55980861244
+2016-08-30 10:30:32,183 DEBUG: View 1 : 0.679425837321
+2016-08-30 10:30:32,244 DEBUG: View 2 : 0.387559808612
+2016-08-30 10:30:32,256 DEBUG: View 3 : 0.459330143541
+2016-08-30 10:30:32,421 DEBUG: Best view : MiRNA_
+2016-08-30 10:30:34,947 DEBUG: Start: Iteration 31
+2016-08-30 10:30:34,969 DEBUG: View 0 : 0.622009569378
+2016-08-30 10:30:34,980 DEBUG: View 1 : 0.746411483254
+2016-08-30 10:30:35,035 DEBUG: View 2 : 0.564593301435
+2016-08-30 10:30:35,046 DEBUG: View 3 : 0.468899521531
+2016-08-30 10:30:35,215 DEBUG: Best view : MiRNA_
+2016-08-30 10:30:37,816 DEBUG: Start: Iteration 32
+2016-08-30 10:30:37,837 DEBUG: View 0 : 0.373205741627
+2016-08-30 10:30:37,847 DEBUG: View 1 : 0.641148325359
+2016-08-30 10:30:37,905 DEBUG: View 2 : 0.574162679426
+2016-08-30 10:30:37,915 DEBUG: View 3 : 0.574162679426
+2016-08-30 10:30:38,088 DEBUG: Best view : MiRNA_
+2016-08-30 10:30:40,837 DEBUG: Start: Iteration 33
+2016-08-30 10:30:40,859 DEBUG: View 0 : 0.468899521531
+2016-08-30 10:30:40,869 DEBUG: View 1 : 0.545454545455
+2016-08-30 10:30:40,915 DEBUG: View 2 : 0.397129186603
+2016-08-30 10:30:40,926 DEBUG: View 3 : 0.382775119617
+2016-08-30 10:30:41,105 DEBUG: Best view : MiRNA_
+2016-08-30 10:30:44,011 DEBUG: Start: Iteration 34
+2016-08-30 10:30:44,035 DEBUG: View 0 : 0.468899521531
+2016-08-30 10:30:44,046 DEBUG: View 1 : 0.66985645933
+2016-08-30 10:30:44,095 DEBUG: View 2 : 0.526315789474
+2016-08-30 10:30:44,105 DEBUG: View 3 : 0.531100478469
+2016-08-30 10:30:44,305 DEBUG: Best view : MiRNA_
+2016-08-30 10:30:47,140 DEBUG: Start: Iteration 35
+2016-08-30 10:30:47,160 DEBUG: View 0 : 0.483253588517
+2016-08-30 10:30:47,170 DEBUG: View 1 : 0.497607655502
+2016-08-30 10:30:47,215 DEBUG: View 2 : 0.564593301435
+2016-08-30 10:30:47,224 DEBUG: View 3 : 0.430622009569
+2016-08-30 10:30:47,405 DEBUG: Best view : RANSeq
+2016-08-30 10:30:50,293 DEBUG: Start: Iteration 36
+2016-08-30 10:30:50,314 DEBUG: View 0 : 0.483253588517
+2016-08-30 10:30:50,324 DEBUG: View 1 : 0.507177033493
+2016-08-30 10:30:50,369 DEBUG: View 2 : 0.521531100478
+2016-08-30 10:30:50,379 DEBUG: View 3 : 0.569377990431
+2016-08-30 10:30:50,567 DEBUG: Best view : Clinic
+2016-08-30 10:30:53,529 DEBUG: Start: Iteration 37
+2016-08-30 10:30:53,549 DEBUG: View 0 : 0.66028708134
+2016-08-30 10:30:53,559 DEBUG: View 1 : 0.636363636364
+2016-08-30 10:30:53,605 DEBUG: View 2 : 0.507177033493
+2016-08-30 10:30:53,615 DEBUG: View 3 : 0.564593301435
+2016-08-30 10:30:53,807 DEBUG: Best view : Methyl
+2016-08-30 10:30:56,845 DEBUG: Start: Iteration 38
+2016-08-30 10:30:56,866 DEBUG: View 0 : 0.444976076555
+2016-08-30 10:30:56,876 DEBUG: View 1 : 0.607655502392
+2016-08-30 10:30:56,921 DEBUG: View 2 : 0.535885167464
+2016-08-30 10:30:56,930 DEBUG: View 3 : 0.454545454545
+2016-08-30 10:30:57,121 DEBUG: Best view : MiRNA_
+2016-08-30 10:31:00,227 DEBUG: Start: Iteration 39
+2016-08-30 10:31:00,247 DEBUG: View 0 : 0.708133971292
+2016-08-30 10:31:00,257 DEBUG: View 1 : 0.401913875598
+2016-08-30 10:31:00,302 DEBUG: View 2 : 0.502392344498
+2016-08-30 10:31:00,311 DEBUG: View 3 : 0.483253588517
+2016-08-30 10:31:00,507 DEBUG: Best view : Methyl
+2016-08-30 10:31:03,715 DEBUG: Start: Iteration 40
+2016-08-30 10:31:03,736 DEBUG: View 0 : 0.387559808612
+2016-08-30 10:31:03,748 DEBUG: View 1 : 0.425837320574
+2016-08-30 10:31:03,801 DEBUG: View 2 : 0.392344497608
+2016-08-30 10:31:03,811 DEBUG: View 3 : 0.531100478469
+2016-08-30 10:31:04,030 DEBUG: Best view : Clinic
+2016-08-30 10:31:07,435 DEBUG: Start: Iteration 41
+2016-08-30 10:31:07,455 DEBUG: View 0 : 0.468899521531
+2016-08-30 10:31:07,465 DEBUG: View 1 : 0.55980861244
+2016-08-30 10:31:07,509 DEBUG: View 2 : 0.397129186603
+2016-08-30 10:31:07,518 DEBUG: View 3 : 0.430622009569
+2016-08-30 10:31:07,725 DEBUG: Best view : MiRNA_
+2016-08-30 10:31:11,097 DEBUG: Start: Iteration 42
+2016-08-30 10:31:11,117 DEBUG: View 0 : 0.732057416268
+2016-08-30 10:31:11,127 DEBUG: View 1 : 0.665071770335
+2016-08-30 10:31:11,172 DEBUG: View 2 : 0.583732057416
+2016-08-30 10:31:11,181 DEBUG: View 3 : 0.401913875598
+2016-08-30 10:31:11,385 DEBUG: Best view : Methyl
+2016-08-30 10:31:14,868 DEBUG: Start: Iteration 43
+2016-08-30 10:31:14,889 DEBUG: View 0 : 0.483253588517
+2016-08-30 10:31:14,899 DEBUG: View 1 : 0.349282296651
+2016-08-30 10:31:14,949 DEBUG: View 2 : 0.444976076555
+2016-08-30 10:31:14,958 DEBUG: View 3 : 0.430622009569
+2016-08-30 10:31:14,959 WARNING: WARNING: All bad for iteration 42
+2016-08-30 10:31:15,170 DEBUG: Best view : Methyl
+2016-08-30 10:31:18,860 DEBUG: Start: Iteration 44
+2016-08-30 10:31:18,885 DEBUG: View 0 : 0.382775119617
+2016-08-30 10:31:18,897 DEBUG: View 1 : 0.454545454545
+2016-08-30 10:31:18,951 DEBUG: View 2 : 0.507177033493
+2016-08-30 10:31:18,962 DEBUG: View 3 : 0.507177033493
+2016-08-30 10:31:19,206 DEBUG: Best view : RANSeq
+2016-08-30 10:31:22,894 DEBUG: Start: Iteration 45
+2016-08-30 10:31:22,915 DEBUG: View 0 : 0.44976076555
+2016-08-30 10:31:22,924 DEBUG: View 1 : 0.626794258373
+2016-08-30 10:31:22,970 DEBUG: View 2 : 0.545454545455
+2016-08-30 10:31:22,979 DEBUG: View 3 : 0.569377990431
+2016-08-30 10:31:23,195 DEBUG: Best view : MiRNA_
+2016-08-30 10:31:26,915 DEBUG: Start: Iteration 46
+2016-08-30 10:31:26,935 DEBUG: View 0 : 0.540669856459
+2016-08-30 10:31:26,946 DEBUG: View 1 : 0.516746411483
+2016-08-30 10:31:26,998 DEBUG: View 2 : 0.526315789474
+2016-08-30 10:31:27,008 DEBUG: View 3 : 0.622009569378
+2016-08-30 10:31:27,231 DEBUG: Best view : Clinic
+2016-08-30 10:31:31,037 DEBUG: Start: Iteration 47
+2016-08-30 10:31:31,058 DEBUG: View 0 : 0.535885167464
+2016-08-30 10:31:31,068 DEBUG: View 1 : 0.435406698565
+2016-08-30 10:31:31,112 DEBUG: View 2 : 0.55023923445
+2016-08-30 10:31:31,122 DEBUG: View 3 : 0.602870813397
+2016-08-30 10:31:31,345 DEBUG: Best view : Clinic
+2016-08-30 10:31:35,202 DEBUG: Start: Iteration 48
+2016-08-30 10:31:35,223 DEBUG: View 0 : 0.492822966507
+2016-08-30 10:31:35,233 DEBUG: View 1 : 0.593301435407
+2016-08-30 10:31:35,279 DEBUG: View 2 : 0.545454545455
+2016-08-30 10:31:35,289 DEBUG: View 3 : 0.602870813397
+2016-08-30 10:31:35,516 DEBUG: Best view : Clinic
+2016-08-30 10:31:39,436 DEBUG: Start: Iteration 49
+2016-08-30 10:31:39,456 DEBUG: View 0 : 0.588516746411
+2016-08-30 10:31:39,466 DEBUG: View 1 : 0.55023923445
+2016-08-30 10:31:39,511 DEBUG: View 2 : 0.464114832536
+2016-08-30 10:31:39,520 DEBUG: View 3 : 0.421052631579
+2016-08-30 10:31:39,750 DEBUG: Best view : Methyl
+2016-08-30 10:31:43,781 DEBUG: Start: Iteration 50
+2016-08-30 10:31:43,804 DEBUG: View 0 : 0.430622009569
+2016-08-30 10:31:43,814 DEBUG: View 1 : 0.526315789474
+2016-08-30 10:31:43,862 DEBUG: View 2 : 0.535885167464
+2016-08-30 10:31:43,871 DEBUG: View 3 : 0.421052631579
+2016-08-30 10:31:44,112 DEBUG: Best view : MiRNA_
+2016-08-30 10:31:48,221 DEBUG: Start: Iteration 51
+2016-08-30 10:31:48,241 DEBUG: View 0 : 0.636363636364
+2016-08-30 10:31:48,251 DEBUG: View 1 : 0.612440191388
+2016-08-30 10:31:48,296 DEBUG: View 2 : 0.55980861244
+2016-08-30 10:31:48,306 DEBUG: View 3 : 0.593301435407
+2016-08-30 10:31:48,543 DEBUG: Best view : Methyl
+2016-08-30 10:31:52,711 DEBUG: Start: Iteration 52
+2016-08-30 10:31:52,732 DEBUG: View 0 : 0.535885167464
+2016-08-30 10:31:52,742 DEBUG: View 1 : 0.421052631579
+2016-08-30 10:31:52,787 DEBUG: View 2 : 0.535885167464
+2016-08-30 10:31:52,797 DEBUG: View 3 : 0.540669856459
+2016-08-30 10:31:53,039 DEBUG: Best view : RANSeq
+2016-08-30 10:31:57,458 DEBUG: Start: Iteration 53
+2016-08-30 10:31:57,479 DEBUG: View 0 : 0.488038277512
+2016-08-30 10:31:57,489 DEBUG: View 1 : 0.622009569378
+2016-08-30 10:31:57,541 DEBUG: View 2 : 0.622009569378
+2016-08-30 10:31:57,551 DEBUG: View 3 : 0.411483253589
+2016-08-30 10:31:57,807 DEBUG: Best view : RANSeq
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-103201-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-103201-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..e8f6ff97
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-103201-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,411 @@
+2016-08-30 10:32:01,666 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 10:32:01,668 INFO: ### Main Programm for Multiview Classification
+2016-08-30 10:32:01,668 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-08-30 10:32:01,668 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 10:32:01,669 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 10:32:01,669 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 10:32:01,670 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 10:32:01,670 INFO: Done: Read Database Files
+2016-08-30 10:32:01,670 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 10:32:01,674 INFO: Done: Determine validation split
+2016-08-30 10:32:01,674 INFO: Start: Determine 5 folds
+2016-08-30 10:32:01,682 INFO: Info: Length of Learning Sets: 196
+2016-08-30 10:32:01,682 INFO: Info: Length of Testing Sets: 48
+2016-08-30 10:32:01,682 INFO: Info: Length of Validation Set: 103
+2016-08-30 10:32:01,682 INFO: Done: Determine folds
+2016-08-30 10:32:01,682 INFO: Start: Learning with Mumbo and 5 folds
+2016-08-30 10:32:01,682 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-30 10:32:01,683 DEBUG: Start: Gridsearch for DecisionTree on Methyl
+2016-08-30 10:32:05,444 DEBUG: Info: Best Reslut : 0.538196721311
+2016-08-30 10:32:05,445 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:32:05,445 DEBUG: Start: Gridsearch for DecisionTree on MiRNA_
+2016-08-30 10:32:06,789 DEBUG: Info: Best Reslut : 0.54737704918
+2016-08-30 10:32:06,789 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:32:06,790 DEBUG: Start: Gridsearch for DecisionTree on RANSeq
+2016-08-30 10:32:14,632 DEBUG: Info: Best Reslut : 0.509016393443
+2016-08-30 10:32:14,632 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:32:14,633 DEBUG: Start: Gridsearch for DecisionTree on Clinic
+2016-08-30 10:32:15,897 DEBUG: Info: Best Reslut : 0.500245901639
+2016-08-30 10:32:15,897 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 10:32:15,897 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-30 10:32:15,897 INFO: Start: Fold number 1
+2016-08-30 10:32:18,046 DEBUG: Start: Iteration 1
+2016-08-30 10:32:18,066 DEBUG: View 0 : 0.62441314554
+2016-08-30 10:32:18,076 DEBUG: View 1 : 0.37558685446
+2016-08-30 10:32:18,129 DEBUG: View 2 : 0.50234741784
+2016-08-30 10:32:18,139 DEBUG: View 3 : 0.37558685446
+2016-08-30 10:32:18,192 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:18,289 DEBUG: Start: Iteration 2
+2016-08-30 10:32:18,310 DEBUG: View 0 : 0.464788732394
+2016-08-30 10:32:18,320 DEBUG: View 1 : 0.638497652582
+2016-08-30 10:32:18,365 DEBUG: View 2 : 0.516431924883
+2016-08-30 10:32:18,375 DEBUG: View 3 : 0.619718309859
+2016-08-30 10:32:18,437 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:18,611 DEBUG: Start: Iteration 3
+2016-08-30 10:32:18,632 DEBUG: View 0 : 0.394366197183
+2016-08-30 10:32:18,641 DEBUG: View 1 : 0.638497652582
+2016-08-30 10:32:18,687 DEBUG: View 2 : 0.596244131455
+2016-08-30 10:32:18,696 DEBUG: View 3 : 0.384976525822
+2016-08-30 10:32:18,767 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:19,022 DEBUG: Start: Iteration 4
+2016-08-30 10:32:19,042 DEBUG: View 0 : 0.422535211268
+2016-08-30 10:32:19,052 DEBUG: View 1 : 0.417840375587
+2016-08-30 10:32:19,101 DEBUG: View 2 : 0.474178403756
+2016-08-30 10:32:19,111 DEBUG: View 3 : 0.43661971831
+2016-08-30 10:32:19,111 WARNING: WARNING: All bad for iteration 3
+2016-08-30 10:32:19,186 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:19,519 DEBUG: Start: Iteration 5
+2016-08-30 10:32:19,540 DEBUG: View 0 : 0.413145539906
+2016-08-30 10:32:19,550 DEBUG: View 1 : 0.704225352113
+2016-08-30 10:32:19,595 DEBUG: View 2 : 0.530516431925
+2016-08-30 10:32:19,604 DEBUG: View 3 : 0.619718309859
+2016-08-30 10:32:19,681 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:20,092 DEBUG: Start: Iteration 6
+2016-08-30 10:32:20,113 DEBUG: View 0 : 0.403755868545
+2016-08-30 10:32:20,122 DEBUG: View 1 : 0.511737089202
+2016-08-30 10:32:20,167 DEBUG: View 2 : 0.389671361502
+2016-08-30 10:32:20,176 DEBUG: View 3 : 0.553990610329
+2016-08-30 10:32:20,258 DEBUG: Best view : Clinic
+2016-08-30 10:32:20,746 DEBUG: Start: Iteration 7
+2016-08-30 10:32:20,767 DEBUG: View 0 : 0.591549295775
+2016-08-30 10:32:20,777 DEBUG: View 1 : 0.619718309859
+2016-08-30 10:32:20,822 DEBUG: View 2 : 0.577464788732
+2016-08-30 10:32:20,831 DEBUG: View 3 : 0.516431924883
+2016-08-30 10:32:20,916 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:21,484 DEBUG: Start: Iteration 8
+2016-08-30 10:32:21,505 DEBUG: View 0 : 0.62441314554
+2016-08-30 10:32:21,515 DEBUG: View 1 : 0.544600938967
+2016-08-30 10:32:21,560 DEBUG: View 2 : 0.62441314554
+2016-08-30 10:32:21,569 DEBUG: View 3 : 0.380281690141
+2016-08-30 10:32:21,656 DEBUG: Best view : RANSeq
+2016-08-30 10:32:22,320 DEBUG: Start: Iteration 9
+2016-08-30 10:32:22,341 DEBUG: View 0 : 0.647887323944
+2016-08-30 10:32:22,351 DEBUG: View 1 : 0.629107981221
+2016-08-30 10:32:22,396 DEBUG: View 2 : 0.488262910798
+2016-08-30 10:32:22,405 DEBUG: View 3 : 0.43661971831
+2016-08-30 10:32:22,496 DEBUG: Best view : Methyl
+2016-08-30 10:32:23,250 DEBUG: Start: Iteration 10
+2016-08-30 10:32:23,270 DEBUG: View 0 : 0.577464788732
+2016-08-30 10:32:23,280 DEBUG: View 1 : 0.507042253521
+2016-08-30 10:32:23,325 DEBUG: View 2 : 0.615023474178
+2016-08-30 10:32:23,335 DEBUG: View 3 : 0.370892018779
+2016-08-30 10:32:23,429 DEBUG: Best view : RANSeq
+2016-08-30 10:32:24,282 DEBUG: Start: Iteration 11
+2016-08-30 10:32:24,302 DEBUG: View 0 : 0.427230046948
+2016-08-30 10:32:24,312 DEBUG: View 1 : 0.394366197183
+2016-08-30 10:32:24,357 DEBUG: View 2 : 0.455399061033
+2016-08-30 10:32:24,367 DEBUG: View 3 : 0.600938967136
+2016-08-30 10:32:24,466 DEBUG: Best view : Clinic
+2016-08-30 10:32:25,634 DEBUG: Start: Iteration 12
+2016-08-30 10:32:25,659 DEBUG: View 0 : 0.408450704225
+2016-08-30 10:32:25,669 DEBUG: View 1 : 0.647887323944
+2016-08-30 10:32:25,714 DEBUG: View 2 : 0.389671361502
+2016-08-30 10:32:25,724 DEBUG: View 3 : 0.50234741784
+2016-08-30 10:32:25,824 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:26,830 INFO: Start: Classification
+2016-08-30 10:32:28,538 INFO: Done: Fold number 1
+2016-08-30 10:32:28,538 INFO: Start: Fold number 2
+2016-08-30 10:32:30,671 DEBUG: Start: Iteration 1
+2016-08-30 10:32:30,690 DEBUG: View 0 : 0.615384615385
+2016-08-30 10:32:30,700 DEBUG: View 1 : 0.432692307692
+2016-08-30 10:32:30,750 DEBUG: View 2 : 0.432692307692
+2016-08-30 10:32:30,759 DEBUG: View 3 : 0.5625
+2016-08-30 10:32:30,811 DEBUG: Best view : Methyl
+2016-08-30 10:32:30,909 DEBUG: Start: Iteration 2
+2016-08-30 10:32:30,929 DEBUG: View 0 : 0.586538461538
+2016-08-30 10:32:30,939 DEBUG: View 1 : 0.552884615385
+2016-08-30 10:32:30,984 DEBUG: View 2 : 0.4375
+2016-08-30 10:32:30,993 DEBUG: View 3 : 0.423076923077
+2016-08-30 10:32:31,052 DEBUG: Best view : Methyl
+2016-08-30 10:32:31,233 DEBUG: Start: Iteration 3
+2016-08-30 10:32:31,253 DEBUG: View 0 : 0.567307692308
+2016-08-30 10:32:31,263 DEBUG: View 1 : 0.639423076923
+2016-08-30 10:32:31,307 DEBUG: View 2 : 0.423076923077
+2016-08-30 10:32:31,316 DEBUG: View 3 : 0.615384615385
+2016-08-30 10:32:31,385 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:31,644 DEBUG: Start: Iteration 4
+2016-08-30 10:32:31,664 DEBUG: View 0 : 0.581730769231
+2016-08-30 10:32:31,675 DEBUG: View 1 : 0.644230769231
+2016-08-30 10:32:31,723 DEBUG: View 2 : 0.552884615385
+2016-08-30 10:32:31,733 DEBUG: View 3 : 0.581730769231
+2016-08-30 10:32:31,806 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:32,142 DEBUG: Start: Iteration 5
+2016-08-30 10:32:32,163 DEBUG: View 0 : 0.644230769231
+2016-08-30 10:32:32,172 DEBUG: View 1 : 0.379807692308
+2016-08-30 10:32:32,216 DEBUG: View 2 : 0.451923076923
+2016-08-30 10:32:32,225 DEBUG: View 3 : 0.596153846154
+2016-08-30 10:32:32,303 DEBUG: Best view : Methyl
+2016-08-30 10:32:32,718 DEBUG: Start: Iteration 6
+2016-08-30 10:32:32,738 DEBUG: View 0 : 0.591346153846
+2016-08-30 10:32:32,748 DEBUG: View 1 : 0.4375
+2016-08-30 10:32:32,792 DEBUG: View 2 : 0.4375
+2016-08-30 10:32:32,801 DEBUG: View 3 : 0.519230769231
+2016-08-30 10:32:32,881 DEBUG: Best view : Methyl
+2016-08-30 10:32:33,376 DEBUG: Start: Iteration 7
+2016-08-30 10:32:33,396 DEBUG: View 0 : 0.658653846154
+2016-08-30 10:32:33,406 DEBUG: View 1 : 0.336538461538
+2016-08-30 10:32:33,452 DEBUG: View 2 : 0.557692307692
+2016-08-30 10:32:33,461 DEBUG: View 3 : 0.4375
+2016-08-30 10:32:33,546 DEBUG: Best view : Methyl
+2016-08-30 10:32:34,138 DEBUG: Start: Iteration 8
+2016-08-30 10:32:34,159 DEBUG: View 0 : 0.620192307692
+2016-08-30 10:32:34,168 DEBUG: View 1 : 0.586538461538
+2016-08-30 10:32:34,212 DEBUG: View 2 : 0.461538461538
+2016-08-30 10:32:34,222 DEBUG: View 3 : 0.504807692308
+2016-08-30 10:32:34,307 DEBUG: Best view : Methyl
+2016-08-30 10:32:34,966 DEBUG: Start: Iteration 9
+2016-08-30 10:32:34,986 DEBUG: View 0 : 0.716346153846
+2016-08-30 10:32:34,996 DEBUG: View 1 : 0.451923076923
+2016-08-30 10:32:35,042 DEBUG: View 2 : 0.5625
+2016-08-30 10:32:35,051 DEBUG: View 3 : 0.4375
+2016-08-30 10:32:35,142 DEBUG: Best view : Methyl
+2016-08-30 10:32:35,887 DEBUG: Start: Iteration 10
+2016-08-30 10:32:35,907 DEBUG: View 0 : 0.298076923077
+2016-08-30 10:32:35,917 DEBUG: View 1 : 0.384615384615
+2016-08-30 10:32:35,960 DEBUG: View 2 : 0.394230769231
+2016-08-30 10:32:35,970 DEBUG: View 3 : 0.495192307692
+2016-08-30 10:32:35,970 WARNING: WARNING: All bad for iteration 9
+2016-08-30 10:32:36,064 DEBUG: Best view : Clinic
+2016-08-30 10:32:36,889 DEBUG: Start: Iteration 11
+2016-08-30 10:32:36,910 DEBUG: View 0 : 0.625
+2016-08-30 10:32:36,919 DEBUG: View 1 : 0.572115384615
+2016-08-30 10:32:36,963 DEBUG: View 2 : 0.471153846154
+2016-08-30 10:32:36,973 DEBUG: View 3 : 0.403846153846
+2016-08-30 10:32:37,067 DEBUG: Best view : Methyl
+2016-08-30 10:32:37,969 DEBUG: Start: Iteration 12
+2016-08-30 10:32:37,989 DEBUG: View 0 : 0.519230769231
+2016-08-30 10:32:37,999 DEBUG: View 1 : 0.677884615385
+2016-08-30 10:32:38,044 DEBUG: View 2 : 0.524038461538
+2016-08-30 10:32:38,054 DEBUG: View 3 : 0.581730769231
+2016-08-30 10:32:38,152 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:39,168 INFO: Start: Classification
+2016-08-30 10:32:40,834 INFO: Done: Fold number 2
+2016-08-30 10:32:40,834 INFO: Start: Fold number 3
+2016-08-30 10:32:42,966 DEBUG: Start: Iteration 1
+2016-08-30 10:32:42,985 DEBUG: View 0 : 0.620853080569
+2016-08-30 10:32:42,995 DEBUG: View 1 : 0.379146919431
+2016-08-30 10:32:43,039 DEBUG: View 2 : 0.620853080569
+2016-08-30 10:32:43,048 DEBUG: View 3 : 0.379146919431
+2016-08-30 10:32:43,101 DEBUG: Best view : Methyl
+2016-08-30 10:32:43,202 DEBUG: Start: Iteration 2
+2016-08-30 10:32:43,223 DEBUG: View 0 : 0.654028436019
+2016-08-30 10:32:43,232 DEBUG: View 1 : 0.331753554502
+2016-08-30 10:32:43,279 DEBUG: View 2 : 0.407582938389
+2016-08-30 10:32:43,288 DEBUG: View 3 : 0.431279620853
+2016-08-30 10:32:43,348 DEBUG: Best view : Methyl
+2016-08-30 10:32:43,532 DEBUG: Start: Iteration 3
+2016-08-30 10:32:43,552 DEBUG: View 0 : 0.407582938389
+2016-08-30 10:32:43,562 DEBUG: View 1 : 0.526066350711
+2016-08-30 10:32:43,607 DEBUG: View 2 : 0.578199052133
+2016-08-30 10:32:43,616 DEBUG: View 3 : 0.488151658768
+2016-08-30 10:32:43,686 DEBUG: Best view : RANSeq
+2016-08-30 10:32:43,971 DEBUG: Start: Iteration 4
+2016-08-30 10:32:43,991 DEBUG: View 0 : 0.497630331754
+2016-08-30 10:32:44,001 DEBUG: View 1 : 0.644549763033
+2016-08-30 10:32:44,050 DEBUG: View 2 : 0.587677725118
+2016-08-30 10:32:44,060 DEBUG: View 3 : 0.587677725118
+2016-08-30 10:32:44,135 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:44,497 DEBUG: Start: Iteration 5
+2016-08-30 10:32:44,517 DEBUG: View 0 : 0.60663507109
+2016-08-30 10:32:44,527 DEBUG: View 1 : 0.407582938389
+2016-08-30 10:32:44,571 DEBUG: View 2 : 0.568720379147
+2016-08-30 10:32:44,581 DEBUG: View 3 : 0.582938388626
+2016-08-30 10:32:44,657 DEBUG: Best view : Methyl
+2016-08-30 10:32:45,100 DEBUG: Start: Iteration 6
+2016-08-30 10:32:45,120 DEBUG: View 0 : 0.511848341232
+2016-08-30 10:32:45,130 DEBUG: View 1 : 0.369668246445
+2016-08-30 10:32:45,174 DEBUG: View 2 : 0.45971563981
+2016-08-30 10:32:45,184 DEBUG: View 3 : 0.42654028436
+2016-08-30 10:32:45,263 DEBUG: Best view : Methyl
+2016-08-30 10:32:45,792 DEBUG: Start: Iteration 7
+2016-08-30 10:32:45,813 DEBUG: View 0 : 0.436018957346
+2016-08-30 10:32:45,823 DEBUG: View 1 : 0.649289099526
+2016-08-30 10:32:45,868 DEBUG: View 2 : 0.526066350711
+2016-08-30 10:32:45,877 DEBUG: View 3 : 0.582938388626
+2016-08-30 10:32:45,960 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:46,565 DEBUG: Start: Iteration 8
+2016-08-30 10:32:46,585 DEBUG: View 0 : 0.265402843602
+2016-08-30 10:32:46,595 DEBUG: View 1 : 0.436018957346
+2016-08-30 10:32:46,639 DEBUG: View 2 : 0.625592417062
+2016-08-30 10:32:46,649 DEBUG: View 3 : 0.402843601896
+2016-08-30 10:32:46,734 DEBUG: Best view : RANSeq
+2016-08-30 10:32:47,448 DEBUG: Start: Iteration 9
+2016-08-30 10:32:47,468 DEBUG: View 0 : 0.421800947867
+2016-08-30 10:32:47,479 DEBUG: View 1 : 0.374407582938
+2016-08-30 10:32:47,523 DEBUG: View 2 : 0.545023696682
+2016-08-30 10:32:47,532 DEBUG: View 3 : 0.611374407583
+2016-08-30 10:32:47,621 DEBUG: Best view : Clinic
+2016-08-30 10:32:48,401 DEBUG: Start: Iteration 10
+2016-08-30 10:32:48,422 DEBUG: View 0 : 0.469194312796
+2016-08-30 10:32:48,431 DEBUG: View 1 : 0.63981042654
+2016-08-30 10:32:48,476 DEBUG: View 2 : 0.478672985782
+2016-08-30 10:32:48,485 DEBUG: View 3 : 0.549763033175
+2016-08-30 10:32:48,577 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:49,431 DEBUG: Start: Iteration 11
+2016-08-30 10:32:49,451 DEBUG: View 0 : 0.587677725118
+2016-08-30 10:32:49,461 DEBUG: View 1 : 0.668246445498
+2016-08-30 10:32:49,505 DEBUG: View 2 : 0.54028436019
+2016-08-30 10:32:49,514 DEBUG: View 3 : 0.57345971564
+2016-08-30 10:32:49,609 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:50,544 DEBUG: Start: Iteration 12
+2016-08-30 10:32:50,564 DEBUG: View 0 : 0.526066350711
+2016-08-30 10:32:50,574 DEBUG: View 1 : 0.440758293839
+2016-08-30 10:32:50,618 DEBUG: View 2 : 0.521327014218
+2016-08-30 10:32:50,628 DEBUG: View 3 : 0.592417061611
+2016-08-30 10:32:50,727 DEBUG: Best view : Clinic
+2016-08-30 10:32:51,744 INFO: Start: Classification
+2016-08-30 10:32:53,456 INFO: Done: Fold number 3
+2016-08-30 10:32:53,457 INFO: Start: Fold number 4
+2016-08-30 10:32:55,550 DEBUG: Start: Iteration 1
+2016-08-30 10:32:55,569 DEBUG: View 0 : 0.386473429952
+2016-08-30 10:32:55,579 DEBUG: View 1 : 0.613526570048
+2016-08-30 10:32:55,614 DEBUG: View 2 : 0.613526570048
+2016-08-30 10:32:55,623 DEBUG: View 3 : 0.613526570048
+2016-08-30 10:32:55,675 DEBUG: Best view : MiRNA_
+2016-08-30 10:32:55,767 DEBUG: Start: Iteration 2
+2016-08-30 10:32:55,787 DEBUG: View 0 : 0.449275362319
+2016-08-30 10:32:55,797 DEBUG: View 1 : 0.473429951691
+2016-08-30 10:32:55,841 DEBUG: View 2 : 0.579710144928
+2016-08-30 10:32:55,850 DEBUG: View 3 : 0.3961352657
+2016-08-30 10:32:55,909 DEBUG: Best view : RANSeq
+2016-08-30 10:32:56,100 DEBUG: Start: Iteration 3
+2016-08-30 10:32:56,120 DEBUG: View 0 : 0.565217391304
+2016-08-30 10:32:56,129 DEBUG: View 1 : 0.410628019324
+2016-08-30 10:32:56,174 DEBUG: View 2 : 0.473429951691
+2016-08-30 10:32:56,184 DEBUG: View 3 : 0.51690821256
+2016-08-30 10:32:56,253 DEBUG: Best view : Methyl
+2016-08-30 10:32:56,528 DEBUG: Start: Iteration 4
+2016-08-30 10:32:56,549 DEBUG: View 0 : 0.3961352657
+2016-08-30 10:32:56,558 DEBUG: View 1 : 0.323671497585
+2016-08-30 10:32:56,603 DEBUG: View 2 : 0.555555555556
+2016-08-30 10:32:56,612 DEBUG: View 3 : 0.492753623188
+2016-08-30 10:32:56,684 DEBUG: Best view : RANSeq
+2016-08-30 10:32:57,048 DEBUG: Start: Iteration 5
+2016-08-30 10:32:57,068 DEBUG: View 0 : 0.405797101449
+2016-08-30 10:32:57,078 DEBUG: View 1 : 0.405797101449
+2016-08-30 10:32:57,121 DEBUG: View 2 : 0.531400966184
+2016-08-30 10:32:57,131 DEBUG: View 3 : 0.657004830918
+2016-08-30 10:32:57,206 DEBUG: Best view : Clinic
+2016-08-30 10:32:57,646 DEBUG: Start: Iteration 6
+2016-08-30 10:32:57,667 DEBUG: View 0 : 0.642512077295
+2016-08-30 10:32:57,677 DEBUG: View 1 : 0.43961352657
+2016-08-30 10:32:57,721 DEBUG: View 2 : 0.536231884058
+2016-08-30 10:32:57,730 DEBUG: View 3 : 0.507246376812
+2016-08-30 10:32:57,810 DEBUG: Best view : Methyl
+2016-08-30 10:32:58,332 DEBUG: Start: Iteration 7
+2016-08-30 10:32:58,352 DEBUG: View 0 : 0.458937198068
+2016-08-30 10:32:58,361 DEBUG: View 1 : 0.550724637681
+2016-08-30 10:32:58,405 DEBUG: View 2 : 0.449275362319
+2016-08-30 10:32:58,415 DEBUG: View 3 : 0.536231884058
+2016-08-30 10:32:58,497 DEBUG: Best view : Clinic
+2016-08-30 10:32:59,098 DEBUG: Start: Iteration 8
+2016-08-30 10:32:59,118 DEBUG: View 0 : 0.357487922705
+2016-08-30 10:32:59,127 DEBUG: View 1 : 0.333333333333
+2016-08-30 10:32:59,171 DEBUG: View 2 : 0.555555555556
+2016-08-30 10:32:59,180 DEBUG: View 3 : 0.565217391304
+2016-08-30 10:32:59,267 DEBUG: Best view : Clinic
+2016-08-30 10:32:59,942 DEBUG: Start: Iteration 9
+2016-08-30 10:32:59,962 DEBUG: View 0 : 0.555555555556
+2016-08-30 10:32:59,971 DEBUG: View 1 : 0.714975845411
+2016-08-30 10:33:00,016 DEBUG: View 2 : 0.584541062802
+2016-08-30 10:33:00,025 DEBUG: View 3 : 0.599033816425
+2016-08-30 10:33:00,114 DEBUG: Best view : MiRNA_
+2016-08-30 10:33:00,866 DEBUG: Start: Iteration 10
+2016-08-30 10:33:00,887 DEBUG: View 0 : 0.589371980676
+2016-08-30 10:33:00,896 DEBUG: View 1 : 0.400966183575
+2016-08-30 10:33:00,941 DEBUG: View 2 : 0.425120772947
+2016-08-30 10:33:00,951 DEBUG: View 3 : 0.531400966184
+2016-08-30 10:33:01,042 DEBUG: Best view : Methyl
+2016-08-30 10:33:01,872 DEBUG: Start: Iteration 11
+2016-08-30 10:33:01,892 DEBUG: View 0 : 0.652173913043
+2016-08-30 10:33:01,902 DEBUG: View 1 : 0.323671497585
+2016-08-30 10:33:01,945 DEBUG: View 2 : 0.584541062802
+2016-08-30 10:33:01,955 DEBUG: View 3 : 0.550724637681
+2016-08-30 10:33:02,048 DEBUG: Best view : Methyl
+2016-08-30 10:33:02,963 DEBUG: Start: Iteration 12
+2016-08-30 10:33:02,983 DEBUG: View 0 : 0.420289855072
+2016-08-30 10:33:02,993 DEBUG: View 1 : 0.652173913043
+2016-08-30 10:33:03,037 DEBUG: View 2 : 0.502415458937
+2016-08-30 10:33:03,046 DEBUG: View 3 : 0.458937198068
+2016-08-30 10:33:03,143 DEBUG: Best view : MiRNA_
+2016-08-30 10:33:04,155 INFO: Start: Classification
+2016-08-30 10:33:05,860 INFO: Done: Fold number 4
+2016-08-30 10:33:05,860 INFO: Start: Fold number 5
+2016-08-30 10:33:08,018 DEBUG: Start: Iteration 1
+2016-08-30 10:33:08,037 DEBUG: View 0 : 0.377358490566
+2016-08-30 10:33:08,047 DEBUG: View 1 : 0.377358490566
+2016-08-30 10:33:08,087 DEBUG: View 2 : 0.377358490566
+2016-08-30 10:33:08,097 DEBUG: View 3 : 0.485849056604
+2016-08-30 10:33:08,097 WARNING: WARNING: All bad for iteration 0
+2016-08-30 10:33:08,150 DEBUG: Best view : Clinic
+2016-08-30 10:33:08,244 DEBUG: Start: Iteration 2
+2016-08-30 10:33:08,264 DEBUG: View 0 : 0.603773584906
+2016-08-30 10:33:08,274 DEBUG: View 1 : 0.415094339623
+2016-08-30 10:33:08,320 DEBUG: View 2 : 0.405660377358
+2016-08-30 10:33:08,330 DEBUG: View 3 : 0.632075471698
+2016-08-30 10:33:08,391 DEBUG: Best view : Clinic
+2016-08-30 10:33:08,565 DEBUG: Start: Iteration 3
+2016-08-30 10:33:08,585 DEBUG: View 0 : 0.641509433962
+2016-08-30 10:33:08,595 DEBUG: View 1 : 0.665094339623
+2016-08-30 10:33:08,641 DEBUG: View 2 : 0.400943396226
+2016-08-30 10:33:08,651 DEBUG: View 3 : 0.457547169811
+2016-08-30 10:33:08,722 DEBUG: Best view : MiRNA_
+2016-08-30 10:33:08,973 DEBUG: Start: Iteration 4
+2016-08-30 10:33:08,994 DEBUG: View 0 : 0.632075471698
+2016-08-30 10:33:09,004 DEBUG: View 1 : 0.61320754717
+2016-08-30 10:33:09,056 DEBUG: View 2 : 0.471698113208
+2016-08-30 10:33:09,065 DEBUG: View 3 : 0.481132075472
+2016-08-30 10:33:09,139 DEBUG: Best view : MiRNA_
+2016-08-30 10:33:09,480 DEBUG: Start: Iteration 5
+2016-08-30 10:33:09,501 DEBUG: View 0 : 0.542452830189
+2016-08-30 10:33:09,511 DEBUG: View 1 : 0.674528301887
+2016-08-30 10:33:09,556 DEBUG: View 2 : 0.547169811321
+2016-08-30 10:33:09,566 DEBUG: View 3 : 0.570754716981
+2016-08-30 10:33:09,643 DEBUG: Best view : MiRNA_
+2016-08-30 10:33:10,057 DEBUG: Start: Iteration 6
+2016-08-30 10:33:10,082 DEBUG: View 0 : 0.570754716981
+2016-08-30 10:33:10,092 DEBUG: View 1 : 0.410377358491
+2016-08-30 10:33:10,139 DEBUG: View 2 : 0.547169811321
+2016-08-30 10:33:10,148 DEBUG: View 3 : 0.36320754717
+2016-08-30 10:33:10,233 DEBUG: Best view : Methyl
+2016-08-30 10:33:10,741 DEBUG: Start: Iteration 7
+2016-08-30 10:33:10,765 DEBUG: View 0 : 0.547169811321
+2016-08-30 10:33:10,777 DEBUG: View 1 : 0.594339622642
+2016-08-30 10:33:10,841 DEBUG: View 2 : 0.561320754717
+2016-08-30 10:33:10,851 DEBUG: View 3 : 0.589622641509
+2016-08-30 10:33:10,964 DEBUG: Best view : Methyl
+2016-08-30 10:33:11,636 DEBUG: Start: Iteration 8
+2016-08-30 10:33:11,657 DEBUG: View 0 : 0.52358490566
+2016-08-30 10:33:11,667 DEBUG: View 1 : 0.778301886792
+2016-08-30 10:33:11,712 DEBUG: View 2 : 0.617924528302
+2016-08-30 10:33:11,721 DEBUG: View 3 : 0.580188679245
+2016-08-30 10:33:11,808 DEBUG: Best view : MiRNA_
+2016-08-30 10:33:12,481 DEBUG: Start: Iteration 9
+2016-08-30 10:33:12,502 DEBUG: View 0 : 0.471698113208
+2016-08-30 10:33:12,512 DEBUG: View 1 : 0.707547169811
+2016-08-30 10:33:12,577 DEBUG: View 2 : 0.594339622642
+2016-08-30 10:33:12,591 DEBUG: View 3 : 0.433962264151
+2016-08-30 10:33:12,725 DEBUG: Best view : MiRNA_
+2016-08-30 10:33:13,559 DEBUG: Start: Iteration 10
+2016-08-30 10:33:13,579 DEBUG: View 0 : 0.63679245283
+2016-08-30 10:33:13,589 DEBUG: View 1 : 0.358490566038
+2016-08-30 10:33:13,633 DEBUG: View 2 : 0.566037735849
+2016-08-30 10:33:13,643 DEBUG: View 3 : 0.61320754717
+2016-08-30 10:33:13,740 DEBUG: Best view : Methyl
+2016-08-30 10:33:14,619 DEBUG: Start: Iteration 11
+2016-08-30 10:33:14,640 DEBUG: View 0 : 0.504716981132
+2016-08-30 10:33:14,649 DEBUG: View 1 : 0.382075471698
+2016-08-30 10:33:14,694 DEBUG: View 2 : 0.528301886792
+2016-08-30 10:33:14,703 DEBUG: View 3 : 0.594339622642
+2016-08-30 10:33:14,799 DEBUG: Best view : Clinic
+2016-08-30 10:33:15,762 DEBUG: Start: Iteration 12
+2016-08-30 10:33:15,790 DEBUG: View 0 : 0.52358490566
+2016-08-30 10:33:15,801 DEBUG: View 1 : 0.768867924528
+2016-08-30 10:33:15,855 DEBUG: View 2 : 0.419811320755
+2016-08-30 10:33:15,866 DEBUG: View 3 : 0.63679245283
+2016-08-30 10:33:15,993 DEBUG: Best view : MiRNA_
+2016-08-30 10:33:17,215 INFO: Start: Classification
+2016-08-30 10:33:18,932 INFO: Done: Fold number 5
+2016-08-30 10:33:18,932 INFO: Done: Classification
+2016-08-30 10:33:18,932 INFO: Info: Time for Classification: 77[s]
+2016-08-30 10:33:18,932 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-103354-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-103354-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..5aefc952
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-103354-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1 @@
+2016-08-30 10:33:54,921 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-103441-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-103441-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..28f996c8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-103441-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1 @@
+2016-08-30 10:34:41,540 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-103912-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-103912-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..3b1d3b51
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-103912-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1 @@
+2016-08-30 10:39:12,183 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-104000-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-104000-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..063116f1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-104000-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1 @@
+2016-08-30 10:40:00,884 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-104030-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-104030-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..c2f50254
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-104030-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 10:40:30,687 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 10:40:30,691 INFO: ### Main Programm for Multiview Classification
+2016-08-30 10:40:30,691 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 10:40:30,691 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 10:40:30,692 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 10:40:30,692 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 10:40:30,693 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 10:40:30,693 INFO: Done: Read Database Files
+2016-08-30 10:40:30,693 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 10:40:30,697 INFO: Done: Determine validation split
+2016-08-30 10:40:30,697 INFO: Start: Determine 5 folds
+2016-08-30 10:40:30,707 INFO: Info: Length of Learning Sets: 196
+2016-08-30 10:40:30,707 INFO: Info: Length of Testing Sets: 48
+2016-08-30 10:40:30,707 INFO: Info: Length of Validation Set: 103
+2016-08-30 10:40:30,707 INFO: Done: Determine folds
+2016-08-30 10:40:30,707 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 10:40:30,707 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-104124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-104124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..56cba5c7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-104124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 10:41:24,634 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 10:41:24,637 INFO: ### Main Programm for Multiview Classification
+2016-08-30 10:41:24,637 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 10:41:24,637 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 10:41:24,638 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 10:41:24,638 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 10:41:24,639 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 10:41:24,639 INFO: Done: Read Database Files
+2016-08-30 10:41:24,639 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 10:41:24,643 INFO: Done: Determine validation split
+2016-08-30 10:41:24,643 INFO: Start: Determine 5 folds
+2016-08-30 10:41:24,649 INFO: Info: Length of Learning Sets: 196
+2016-08-30 10:41:24,649 INFO: Info: Length of Testing Sets: 48
+2016-08-30 10:41:24,649 INFO: Info: Length of Validation Set: 103
+2016-08-30 10:41:24,649 INFO: Done: Determine folds
+2016-08-30 10:41:24,649 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 10:41:24,650 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-111552-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-111552-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..9de80239
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-111552-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:15:52,440 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:15:52,443 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:15:52,443 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:15:52,444 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:15:52,444 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:15:52,444 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:15:52,445 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:15:52,445 INFO: Done: Read Database Files
+2016-08-30 11:15:52,445 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:15:52,449 INFO: Done: Determine validation split
+2016-08-30 11:15:52,449 INFO: Start: Determine 5 folds
+2016-08-30 11:15:52,457 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:15:52,457 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:15:52,457 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:15:52,458 INFO: Done: Determine folds
+2016-08-30 11:15:52,458 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:15:52,458 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-111631-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-111631-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..1bf11011
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-111631-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:16:31,858 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:16:31,861 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:16:31,861 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:16:31,861 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:16:31,862 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:16:31,862 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:16:31,862 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:16:31,863 INFO: Done: Read Database Files
+2016-08-30 11:16:31,863 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:16:31,866 INFO: Done: Determine validation split
+2016-08-30 11:16:31,866 INFO: Start: Determine 5 folds
+2016-08-30 11:16:31,874 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:16:31,874 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:16:31,875 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:16:31,875 INFO: Done: Determine folds
+2016-08-30 11:16:31,875 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:16:31,875 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-111651-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-111651-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..3d5ef906
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-111651-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:16:51,463 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:16:51,467 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:16:51,467 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:16:51,468 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:16:51,468 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:16:51,469 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:16:51,469 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:16:51,469 INFO: Done: Read Database Files
+2016-08-30 11:16:51,469 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:16:51,473 INFO: Done: Determine validation split
+2016-08-30 11:16:51,473 INFO: Start: Determine 5 folds
+2016-08-30 11:16:51,480 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:16:51,480 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:16:51,480 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:16:51,480 INFO: Done: Determine folds
+2016-08-30 11:16:51,480 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:16:51,480 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-111721-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-111721-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..4adb9e9a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-111721-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:17:21,256 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:17:21,260 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:17:21,260 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:17:21,260 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:17:21,261 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:17:21,261 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:17:21,262 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:17:21,262 INFO: Done: Read Database Files
+2016-08-30 11:17:21,262 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:17:21,266 INFO: Done: Determine validation split
+2016-08-30 11:17:21,266 INFO: Start: Determine 5 folds
+2016-08-30 11:17:21,275 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:17:21,275 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:17:21,275 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:17:21,275 INFO: Done: Determine folds
+2016-08-30 11:17:21,275 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:17:21,275 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-111801-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-111801-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..e2e98434
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-111801-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:18:01,192 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:18:01,196 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:18:01,196 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:18:01,197 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:18:01,198 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:18:01,199 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:18:01,199 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:18:01,199 INFO: Done: Read Database Files
+2016-08-30 11:18:01,199 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:18:01,203 INFO: Done: Determine validation split
+2016-08-30 11:18:01,203 INFO: Start: Determine 5 folds
+2016-08-30 11:18:01,211 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:18:01,211 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:18:01,211 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:18:01,211 INFO: Done: Determine folds
+2016-08-30 11:18:01,211 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:18:01,211 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-112132-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-112132-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..cafff0e5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-112132-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:21:32,186 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:21:32,189 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:21:32,189 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:21:32,189 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:21:32,189 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:21:32,190 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:21:32,190 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:21:32,191 INFO: Done: Read Database Files
+2016-08-30 11:21:32,191 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:21:32,194 INFO: Done: Determine validation split
+2016-08-30 11:21:32,194 INFO: Start: Determine 5 folds
+2016-08-30 11:21:32,203 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:21:32,203 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:21:32,203 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:21:32,203 INFO: Done: Determine folds
+2016-08-30 11:21:32,203 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:21:32,204 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-112630-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-112630-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..036d6c4f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-112630-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:26:30,166 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:26:30,172 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:26:30,172 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:26:30,173 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:26:30,175 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:26:30,175 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:26:30,176 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:26:30,177 INFO: Done: Read Database Files
+2016-08-30 11:26:30,177 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:26:30,182 INFO: Done: Determine validation split
+2016-08-30 11:26:30,182 INFO: Start: Determine 5 folds
+2016-08-30 11:26:30,189 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:26:30,189 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:26:30,189 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:26:30,189 INFO: Done: Determine folds
+2016-08-30 11:26:30,189 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:26:30,189 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-113306-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-113306-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..1f185bb0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-113306-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,24 @@
+2016-08-30 11:33:06,872 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:33:06,876 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:33:06,876 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-08-30 11:33:06,877 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:33:06,877 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:33:06,878 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:33:06,878 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:33:06,879 INFO: Done: Read Database Files
+2016-08-30 11:33:06,879 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:33:06,883 INFO: Done: Determine validation split
+2016-08-30 11:33:06,884 INFO: Start: Determine 5 folds
+2016-08-30 11:33:06,892 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:33:06,892 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:33:06,892 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:33:06,892 INFO: Done: Determine folds
+2016-08-30 11:33:06,893 INFO: Start: Learning with Mumbo and 5 folds
+2016-08-30 11:33:06,893 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-30 11:33:06,893 DEBUG: Start: Gridsearch for DecisionTree on Methyl
+2016-08-30 11:33:10,734 DEBUG: Info: Best Reslut : 0.511229508197
+2016-08-30 11:33:10,735 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 11:33:10,735 DEBUG: Start: Gridsearch for DecisionTree on MiRNA_
+2016-08-30 11:33:12,103 DEBUG: Info: Best Reslut : 0.566885245902
+2016-08-30 11:33:12,103 DEBUG: Done: Gridsearch for DecisionTree
+2016-08-30 11:33:12,103 DEBUG: Start: Gridsearch for DecisionTree on RANSeq
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-113333-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-113333-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..76aac63b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-113333-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:33:33,283 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:33:33,287 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:33:33,287 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:33:33,287 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:33:33,288 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:33:33,288 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:33:33,289 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:33:33,289 INFO: Done: Read Database Files
+2016-08-30 11:33:33,289 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:33:33,292 INFO: Done: Determine validation split
+2016-08-30 11:33:33,292 INFO: Start: Determine 5 folds
+2016-08-30 11:33:33,300 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:33:33,301 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:33:33,301 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:33:33,301 INFO: Done: Determine folds
+2016-08-30 11:33:33,301 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:33:33,301 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-113715-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-113715-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..bccb04ff
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-113715-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:37:15,915 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:37:15,919 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:37:15,919 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:37:15,920 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:37:15,920 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:37:15,921 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:37:15,921 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:37:15,921 INFO: Done: Read Database Files
+2016-08-30 11:37:15,921 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:37:15,925 INFO: Done: Determine validation split
+2016-08-30 11:37:15,925 INFO: Start: Determine 5 folds
+2016-08-30 11:37:15,933 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:37:15,933 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:37:15,933 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:37:15,933 INFO: Done: Determine folds
+2016-08-30 11:37:15,933 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:37:15,934 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-114018-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-114018-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..60dc01d8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-114018-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,19 @@
+2016-08-30 11:40:18,184 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:40:18,188 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:40:18,188 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:40:18,188 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:40:18,189 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:40:18,189 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:40:18,190 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:40:18,190 INFO: Done: Read Database Files
+2016-08-30 11:40:18,190 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:40:18,194 INFO: Done: Determine validation split
+2016-08-30 11:40:18,194 INFO: Start: Determine 5 folds
+2016-08-30 11:40:18,201 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:40:18,202 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:40:18,202 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:40:18,202 INFO: Done: Determine folds
+2016-08-30 11:40:18,202 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:40:18,202 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-30 11:42:11,831 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-30 11:42:11,831 INFO: Start: Fold number 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-114344-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-114344-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..ffdb5985
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-114344-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,19 @@
+2016-08-30 11:43:44,517 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:43:44,521 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:43:44,521 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:43:44,521 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:43:44,521 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:43:44,522 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:43:44,522 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:43:44,522 INFO: Done: Read Database Files
+2016-08-30 11:43:44,522 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:43:44,526 INFO: Done: Determine validation split
+2016-08-30 11:43:44,526 INFO: Start: Determine 5 folds
+2016-08-30 11:43:44,533 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:43:44,534 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:43:44,534 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:43:44,534 INFO: Done: Determine folds
+2016-08-30 11:43:44,534 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:43:44,534 INFO: Start: Gridsearching best settings for monoview classifiers
+2016-08-30 11:45:38,865 INFO: Done: Gridsearching best settings for monoview classifiers
+2016-08-30 11:45:38,865 INFO: Start: Fold number 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-114801-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-114801-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..b9d58f56
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-114801-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:48:01,091 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:48:01,094 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:48:01,094 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:48:01,094 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:48:01,095 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:48:01,095 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:48:01,096 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:48:01,096 INFO: Done: Read Database Files
+2016-08-30 11:48:01,096 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:48:01,100 INFO: Done: Determine validation split
+2016-08-30 11:48:01,100 INFO: Start: Determine 5 folds
+2016-08-30 11:48:01,108 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:48:01,108 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:48:01,108 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:48:01,109 INFO: Done: Determine folds
+2016-08-30 11:48:01,109 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:48:01,109 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-115133-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-115133-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..15cf2d19
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-115133-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:51:33,943 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:51:33,947 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:51:33,947 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:51:33,948 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:51:33,948 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:51:33,948 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:51:33,949 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:51:33,949 INFO: Done: Read Database Files
+2016-08-30 11:51:33,949 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:51:33,953 INFO: Done: Determine validation split
+2016-08-30 11:51:33,953 INFO: Start: Determine 5 folds
+2016-08-30 11:51:33,961 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:51:33,961 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:51:33,961 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:51:33,961 INFO: Done: Determine folds
+2016-08-30 11:51:33,961 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:51:33,961 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-115229-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-115229-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..43fe7249
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-115229-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:52:29,468 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:52:29,471 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:52:29,472 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:52:29,472 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:52:29,472 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:52:29,473 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:52:29,473 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:52:29,473 INFO: Done: Read Database Files
+2016-08-30 11:52:29,473 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:52:29,477 INFO: Done: Determine validation split
+2016-08-30 11:52:29,477 INFO: Start: Determine 5 folds
+2016-08-30 11:52:29,485 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:52:29,485 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:52:29,485 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:52:29,486 INFO: Done: Determine folds
+2016-08-30 11:52:29,486 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:52:29,486 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-115605-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-115605-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..5301fb8a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-115605-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:56:05,730 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:56:05,733 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:56:05,734 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:56:05,734 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:56:05,735 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:56:05,735 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:56:05,735 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:56:05,736 INFO: Done: Read Database Files
+2016-08-30 11:56:05,736 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:56:05,740 INFO: Done: Determine validation split
+2016-08-30 11:56:05,740 INFO: Start: Determine 5 folds
+2016-08-30 11:56:05,748 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:56:05,748 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:56:05,748 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:56:05,748 INFO: Done: Determine folds
+2016-08-30 11:56:05,748 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:56:05,748 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-115919-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-115919-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..b77b6bd2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-115919-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,17 @@
+2016-08-30 11:59:19,771 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 11:59:19,775 INFO: ### Main Programm for Multiview Classification
+2016-08-30 11:59:19,775 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-08-30 11:59:19,776 INFO: Info: Shape of Methyl :(347, 25978)
+2016-08-30 11:59:19,776 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-08-30 11:59:19,777 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-08-30 11:59:19,777 INFO: Info: Shape of Clinic :(347, 127)
+2016-08-30 11:59:19,777 INFO: Done: Read Database Files
+2016-08-30 11:59:19,777 INFO: Start: Determine validation split for ratio 0.7
+2016-08-30 11:59:19,781 INFO: Done: Determine validation split
+2016-08-30 11:59:19,781 INFO: Start: Determine 5 folds
+2016-08-30 11:59:19,788 INFO: Info: Length of Learning Sets: 196
+2016-08-30 11:59:19,788 INFO: Info: Length of Testing Sets: 48
+2016-08-30 11:59:19,789 INFO: Info: Length of Validation Set: 103
+2016-08-30 11:59:19,789 INFO: Done: Determine folds
+2016-08-30 11:59:19,789 INFO: Start: Learning with Fusion and 5 folds
+2016-08-30 11:59:19,789 INFO: Start: Gridsearching best settings for monoview classifiers
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-120336-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-120336-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..b81da0aa
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-120336-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,195 @@
+2016-08-30 12:03:36,717 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 12:03:36,730 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:03:36,730 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 12:03:36,730 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:03:36,744 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:03:36,744 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:03:36,745 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:03:36,745 DEBUG: Start: Classification
+2016-08-30 12:03:47,963 DEBUG: Info: Time for Classification: 11.2434618473[s]
+2016-08-30 12:03:47,963 DEBUG: Done: Classification
+2016-08-30 12:03:47,987 DEBUG: Start: Statistic Results
+2016-08-30 12:03:47,987 INFO: Accuracy :0.838095238095
+2016-08-30 12:03:47,999 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:03:47,999 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-30 12:03:47,999 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:03:48,010 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:03:48,010 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:03:48,011 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:03:48,011 DEBUG: Start: Classification
+2016-08-30 12:03:58,159 DEBUG: Info: Time for Classification: 10.1701390743[s]
+2016-08-30 12:03:58,159 DEBUG: Done: Classification
+2016-08-30 12:03:58,162 DEBUG: Start: Statistic Results
+2016-08-30 12:03:58,162 INFO: Accuracy :0.819047619048
+2016-08-30 12:03:58,174 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:03:58,175 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-30 12:03:58,175 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:03:58,188 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:03:58,188 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:03:58,188 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:03:58,188 DEBUG: Start: Classification
+2016-08-30 12:04:00,912 DEBUG: Info: Time for Classification: 2.74817919731[s]
+2016-08-30 12:04:00,913 DEBUG: Done: Classification
+2016-08-30 12:04:02,188 DEBUG: Start: Statistic Results
+2016-08-30 12:04:02,189 INFO: Accuracy :0.87619047619
+2016-08-30 12:04:02,201 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:02,201 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-30 12:04:02,201 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:02,215 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:04:02,215 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:04:02,215 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:02,215 DEBUG: Start: Classification
+2016-08-30 12:04:02,958 DEBUG: Info: Time for Classification: 0.766482830048[s]
+2016-08-30 12:04:02,958 DEBUG: Done: Classification
+2016-08-30 12:04:02,962 DEBUG: Start: Statistic Results
+2016-08-30 12:04:02,962 INFO: Accuracy :0.885714285714
+2016-08-30 12:04:02,974 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:02,975 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-30 12:04:02,975 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:02,988 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:04:02,988 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:04:02,988 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:02,989 DEBUG: Start: Classification
+2016-08-30 12:04:04,699 DEBUG: Info: Time for Classification: 1.73435711861[s]
+2016-08-30 12:04:04,699 DEBUG: Done: Classification
+2016-08-30 12:04:04,708 DEBUG: Start: Statistic Results
+2016-08-30 12:04:04,708 INFO: Accuracy :0.761904761905
+2016-08-30 12:04:04,718 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:04,718 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-30 12:04:04,719 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:04,731 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:04:04,731 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:04:04,731 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:04,731 DEBUG: Start: Classification
+2016-08-30 12:04:13,743 DEBUG: Info: Time for Classification: 9.03304004669[s]
+2016-08-30 12:04:13,743 DEBUG: Done: Classification
+2016-08-30 12:04:14,080 DEBUG: Start: Statistic Results
+2016-08-30 12:04:14,080 INFO: Accuracy :0.838095238095
+2016-08-30 12:04:14,089 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:14,089 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-30 12:04:14,089 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:14,101 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:04:14,101 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:04:14,101 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:14,101 DEBUG: Start: Classification
+2016-08-30 12:04:23,971 DEBUG: Info: Time for Classification: 9.88981103897[s]
+2016-08-30 12:04:23,972 DEBUG: Done: Classification
+2016-08-30 12:04:24,391 DEBUG: Start: Statistic Results
+2016-08-30 12:04:24,391 INFO: Accuracy :0.819047619048
+2016-08-30 12:04:24,400 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:24,400 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-08-30 12:04:24,400 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:24,412 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:04:24,412 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:04:24,412 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:24,412 DEBUG: Start: Classification
+2016-08-30 12:04:35,480 DEBUG: Info: Time for Classification: 11.0867400169[s]
+2016-08-30 12:04:35,480 DEBUG: Done: Classification
+2016-08-30 12:04:35,853 DEBUG: Start: Statistic Results
+2016-08-30 12:04:35,854 INFO: Accuracy :0.914285714286
+2016-08-30 12:04:35,855 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:35,855 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 12:04:35,855 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:35,856 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:04:35,856 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:04:35,856 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:35,856 DEBUG: Start: Classification
+2016-08-30 12:04:36,123 DEBUG: Info: Time for Classification: 0.267621040344[s]
+2016-08-30 12:04:36,123 DEBUG: Done: Classification
+2016-08-30 12:04:36,124 DEBUG: Start: Statistic Results
+2016-08-30 12:04:36,125 INFO: Accuracy :0.752380952381
+2016-08-30 12:04:36,126 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:36,126 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-30 12:04:36,126 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:36,127 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:04:36,127 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:04:36,127 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:36,127 DEBUG: Start: Classification
+2016-08-30 12:04:36,393 DEBUG: Info: Time for Classification: 0.267601013184[s]
+2016-08-30 12:04:36,393 DEBUG: Done: Classification
+2016-08-30 12:04:36,395 DEBUG: Start: Statistic Results
+2016-08-30 12:04:36,395 INFO: Accuracy :0.780952380952
+2016-08-30 12:04:36,396 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:36,396 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-30 12:04:36,396 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:36,397 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:04:36,397 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:04:36,397 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:36,397 DEBUG: Start: Classification
+2016-08-30 12:04:36,506 DEBUG: Info: Time for Classification: 0.109934806824[s]
+2016-08-30 12:04:36,506 DEBUG: Done: Classification
+2016-08-30 12:04:36,552 DEBUG: Start: Statistic Results
+2016-08-30 12:04:36,552 INFO: Accuracy :0.714285714286
+2016-08-30 12:04:36,554 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:36,554 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-30 12:04:36,554 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:36,555 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:04:36,555 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:04:36,555 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:36,555 DEBUG: Start: Classification
+2016-08-30 12:04:36,785 DEBUG: Info: Time for Classification: 0.231173038483[s]
+2016-08-30 12:04:36,785 DEBUG: Done: Classification
+2016-08-30 12:04:36,787 DEBUG: Start: Statistic Results
+2016-08-30 12:04:36,787 INFO: Accuracy :0.866666666667
+2016-08-30 12:04:36,788 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:36,789 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-30 12:04:36,789 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:36,789 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:04:36,789 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:04:36,789 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:36,790 DEBUG: Start: Classification
+2016-08-30 12:04:36,904 DEBUG: Info: Time for Classification: 0.115266084671[s]
+2016-08-30 12:04:36,904 DEBUG: Done: Classification
+2016-08-30 12:04:36,905 DEBUG: Start: Statistic Results
+2016-08-30 12:04:36,906 INFO: Accuracy :0.828571428571
+2016-08-30 12:04:36,907 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:36,907 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-30 12:04:36,907 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:36,908 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:04:36,908 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:04:36,908 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:36,908 DEBUG: Start: Classification
+2016-08-30 12:04:45,396 DEBUG: Info: Time for Classification: 8.48903298378[s]
+2016-08-30 12:04:45,396 DEBUG: Done: Classification
+2016-08-30 12:04:45,404 DEBUG: Start: Statistic Results
+2016-08-30 12:04:45,404 INFO: Accuracy :0.761904761905
+2016-08-30 12:04:45,405 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:45,405 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-30 12:04:45,405 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:45,406 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:04:45,406 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:04:45,406 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:45,406 DEBUG: Start: Classification
+2016-08-30 12:04:45,450 DEBUG: Info: Time for Classification: 0.0446889400482[s]
+2016-08-30 12:04:45,450 DEBUG: Done: Classification
+2016-08-30 12:04:45,452 DEBUG: Start: Statistic Results
+2016-08-30 12:04:45,452 INFO: Accuracy :0.266666666667
+2016-08-30 12:04:45,453 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:45,453 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-08-30 12:04:45,453 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:45,454 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:04:45,454 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:04:45,454 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:45,454 DEBUG: Start: Classification
+2016-08-30 12:04:46,106 DEBUG: Info: Time for Classification: 0.653275966644[s]
+2016-08-30 12:04:46,106 DEBUG: Done: Classification
+2016-08-30 12:04:46,135 DEBUG: Start: Statistic Results
+2016-08-30 12:04:46,135 INFO: Accuracy :0.733333333333
+2016-08-30 12:04:46,181 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:04:46,181 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 12:04:46,182 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:04:46,235 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-30 12:04:46,235 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-30 12:04:46,235 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:04:46,236 DEBUG: Start: Classification
+2016-08-30 12:05:24,578 DEBUG: Info: Time for Classification: 38.4417188168[s]
+2016-08-30 12:05:24,578 DEBUG: Done: Classification
+2016-08-30 12:05:24,588 DEBUG: Start: Statistic Results
+2016-08-30 12:05:24,589 INFO: Accuracy :0.542857142857
+2016-08-30 12:05:24,640 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:05:24,640 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-30 12:05:24,640 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:05:24,692 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-08-30 12:05:24,693 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-08-30 12:05:24,693 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:05:24,693 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-120633-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-120633-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..29759730
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-120633-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1 @@
+2016-08-30 12:06:33,599 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-120904-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-120904-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..97a22c85
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-120904-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-30 12:09:04,809 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 12:09:04,823 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:09:04,823 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 12:09:04,823 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:09:04,837 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:09:04,838 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:09:04,838 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:09:04,838 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-120923-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-120923-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..bfec55db
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-120923-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1 @@
+2016-08-30 12:09:23,217 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-121006-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-121006-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..b3084b9a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-121006-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,162 @@
+2016-08-30 12:10:06,567 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 12:10:06,583 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:10:06,583 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 12:10:06,584 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:10:06,605 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:10:06,605 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:10:06,605 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:10:06,606 DEBUG: Start: Classification
+2016-08-30 12:13:40,602 DEBUG: Info: Time for Classification: 214.031033993[s]
+2016-08-30 12:13:40,602 DEBUG: Done: Classification
+2016-08-30 12:13:40,607 DEBUG: Start: Statistic Results
+2016-08-30 12:13:40,607 INFO: Accuracy :0.828571428571
+2016-08-30 12:13:40,893 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:13:40,893 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-30 12:13:40,894 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:13:40,908 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:13:40,908 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:13:40,908 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:13:40,908 DEBUG: Start: Classification
+2016-08-30 12:16:58,671 DEBUG: Info: Time for Classification: 198.055801153[s]
+2016-08-30 12:16:58,671 DEBUG: Done: Classification
+2016-08-30 12:16:58,674 DEBUG: Start: Statistic Results
+2016-08-30 12:16:58,674 INFO: Accuracy :0.771428571429
+2016-08-30 12:16:58,688 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:16:58,688 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-30 12:16:58,688 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:16:58,717 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:16:58,717 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:16:58,717 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:16:58,717 DEBUG: Start: Classification
+2016-08-30 12:18:12,781 DEBUG: Info: Time for Classification: 74.1028950214[s]
+2016-08-30 12:18:12,781 DEBUG: Done: Classification
+2016-08-30 12:18:14,045 DEBUG: Start: Statistic Results
+2016-08-30 12:18:14,045 INFO: Accuracy :0.866666666667
+2016-08-30 12:18:14,058 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:18:14,058 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-30 12:18:14,058 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:18:14,072 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:18:14,072 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:18:14,072 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:18:14,072 DEBUG: Start: Classification
+2016-08-30 12:18:36,574 DEBUG: Info: Time for Classification: 22.5265438557[s]
+2016-08-30 12:18:36,575 DEBUG: Done: Classification
+2016-08-30 12:18:36,580 DEBUG: Start: Statistic Results
+2016-08-30 12:18:36,581 INFO: Accuracy :0.885714285714
+2016-08-30 12:18:36,593 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:18:36,593 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-30 12:18:36,593 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:18:36,608 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:18:36,608 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:18:36,608 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:18:36,608 DEBUG: Start: Classification
+2016-08-30 12:19:07,120 DEBUG: Info: Time for Classification: 30.5368850231[s]
+2016-08-30 12:19:07,120 DEBUG: Done: Classification
+2016-08-30 12:19:07,129 DEBUG: Start: Statistic Results
+2016-08-30 12:19:07,129 INFO: Accuracy :0.895238095238
+2016-08-30 12:19:07,139 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:19:07,139 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-30 12:19:07,139 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:19:07,151 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:19:07,151 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:19:07,151 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:19:07,151 DEBUG: Start: Classification
+2016-08-30 12:20:43,155 DEBUG: Info: Time for Classification: 96.0247499943[s]
+2016-08-30 12:20:43,155 DEBUG: Done: Classification
+2016-08-30 12:20:43,427 DEBUG: Start: Statistic Results
+2016-08-30 12:20:43,427 INFO: Accuracy :0.857142857143
+2016-08-30 12:20:43,436 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:20:43,436 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-30 12:20:43,436 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:20:43,448 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:20:43,448 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:20:43,448 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:20:43,448 DEBUG: Start: Classification
+2016-08-30 12:22:26,658 DEBUG: Info: Time for Classification: 103.229175806[s]
+2016-08-30 12:22:26,658 DEBUG: Done: Classification
+2016-08-30 12:22:26,943 DEBUG: Start: Statistic Results
+2016-08-30 12:22:26,944 INFO: Accuracy :0.87619047619
+2016-08-30 12:22:26,953 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:22:26,953 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-08-30 12:22:26,953 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:22:26,965 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 12:22:26,965 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 12:22:26,965 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:22:26,965 DEBUG: Start: Classification
+2016-08-30 12:24:41,765 DEBUG: Info: Time for Classification: 134.81978488[s]
+2016-08-30 12:24:41,765 DEBUG: Done: Classification
+2016-08-30 12:24:42,111 DEBUG: Start: Statistic Results
+2016-08-30 12:24:42,111 INFO: Accuracy :0.885714285714
+2016-08-30 12:24:42,139 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:24:42,140 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 12:24:42,140 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:24:42,141 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:24:42,141 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:24:42,141 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:24:42,142 DEBUG: Start: Classification
+2016-08-30 12:24:48,311 DEBUG: Info: Time for Classification: 6.19787788391[s]
+2016-08-30 12:24:48,311 DEBUG: Done: Classification
+2016-08-30 12:24:48,312 DEBUG: Start: Statistic Results
+2016-08-30 12:24:48,313 INFO: Accuracy :0.838095238095
+2016-08-30 12:24:48,314 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:24:48,314 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-30 12:24:48,314 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:24:48,315 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:24:48,315 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:24:48,315 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:24:48,315 DEBUG: Start: Classification
+2016-08-30 12:24:53,099 DEBUG: Info: Time for Classification: 4.78557896614[s]
+2016-08-30 12:24:53,099 DEBUG: Done: Classification
+2016-08-30 12:24:53,101 DEBUG: Start: Statistic Results
+2016-08-30 12:24:53,101 INFO: Accuracy :0.752380952381
+2016-08-30 12:24:53,102 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:24:53,102 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-30 12:24:53,102 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:24:53,103 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:24:53,103 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:24:53,103 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:24:53,103 DEBUG: Start: Classification
+2016-08-30 12:24:55,948 DEBUG: Info: Time for Classification: 2.84561300278[s]
+2016-08-30 12:24:55,948 DEBUG: Done: Classification
+2016-08-30 12:24:55,992 DEBUG: Start: Statistic Results
+2016-08-30 12:24:55,992 INFO: Accuracy :0.8
+2016-08-30 12:24:55,994 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:24:55,994 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-30 12:24:55,994 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:24:55,995 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:24:55,995 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:24:55,995 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:24:55,995 DEBUG: Start: Classification
+2016-08-30 12:25:04,528 DEBUG: Info: Time for Classification: 8.534678936[s]
+2016-08-30 12:25:04,528 DEBUG: Done: Classification
+2016-08-30 12:25:04,532 DEBUG: Start: Statistic Results
+2016-08-30 12:25:04,532 INFO: Accuracy :0.838095238095
+2016-08-30 12:25:04,533 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:25:04,533 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-30 12:25:04,533 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:25:04,534 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:25:04,534 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:25:04,534 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:25:04,534 DEBUG: Start: Classification
+2016-08-30 12:25:06,746 DEBUG: Info: Time for Classification: 2.21299600601[s]
+2016-08-30 12:25:06,746 DEBUG: Done: Classification
+2016-08-30 12:25:06,748 DEBUG: Start: Statistic Results
+2016-08-30 12:25:06,748 INFO: Accuracy :0.657142857143
+2016-08-30 12:25:06,750 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:25:06,750 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-30 12:25:06,750 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:25:06,750 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:25:06,751 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:25:06,751 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:25:06,751 DEBUG: Start: Classification
+2016-08-30 12:28:06,160 DEBUG: Info: Time for Classification: 179.410264969[s]
+2016-08-30 12:28:06,160 DEBUG: Done: Classification
+2016-08-30 12:28:06,168 DEBUG: Start: Statistic Results
+2016-08-30 12:28:06,168 INFO: Accuracy :0.780952380952
+2016-08-30 12:28:06,170 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 12:28:06,170 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-08-30 12:28:06,170 DEBUG: Start: Determine Train/Test split
+2016-08-30 12:28:06,171 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-08-30 12:28:06,171 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-08-30 12:28:06,171 DEBUG: Done: Determine Train/Test split
+2016-08-30 12:28:06,171 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-173818-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-173818-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 00000000..703a6426
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-173818-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,8 @@
+2016-08-30 17:38:18,256 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 17:38:18,679 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 17:38:18,679 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 17:38:18,680 DEBUG: Start: Determine Train/Test split
+2016-08-30 17:38:18,716 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-08-30 17:38:18,716 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-08-30 17:38:18,716 DEBUG: Done: Determine Train/Test split
+2016-08-30 17:38:18,716 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-173904-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-173904-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-173935-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-173935-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-173953-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-173953-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20160830-174032-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160830-174032-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
new file mode 100644
index 00000000..6e73e7de
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160830-174032-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
@@ -0,0 +1,63 @@
+2016-08-30 17:40:32,385 INFO: Start: Finding all available mono- & multiview algorithms
+2016-08-30 17:40:32,388 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 17:40:32,388 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-08-30 17:40:32,388 DEBUG: Start: Determine Train/Test split
+2016-08-30 17:40:32,388 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-08-30 17:40:32,388 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-08-30 17:40:32,389 DEBUG: Done: Determine Train/Test split
+2016-08-30 17:40:32,389 DEBUG: Start: Classification
+2016-08-30 17:40:33,567 DEBUG: Info: Time for Classification: 1.13154196739[s]
+2016-08-30 17:40:33,567 DEBUG: Done: Classification
+2016-08-30 17:40:33,594 DEBUG: Start: Statistic Results
+2016-08-30 17:40:33,595 INFO: Accuracy :0.0888888888889
+2016-08-30 17:40:33,596 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 17:40:33,597 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-08-30 17:40:33,597 DEBUG: Start: Determine Train/Test split
+2016-08-30 17:40:33,597 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-08-30 17:40:33,597 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-08-30 17:40:33,597 DEBUG: Done: Determine Train/Test split
+2016-08-30 17:40:33,598 DEBUG: Start: Classification
+2016-08-30 17:40:34,354 DEBUG: Info: Time for Classification: 0.757889986038[s]
+2016-08-30 17:40:34,354 DEBUG: Done: Classification
+2016-08-30 17:40:34,355 DEBUG: Start: Statistic Results
+2016-08-30 17:40:34,356 INFO: Accuracy :0.122222222222
+2016-08-30 17:40:34,357 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 17:40:34,357 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-08-30 17:40:34,357 DEBUG: Start: Determine Train/Test split
+2016-08-30 17:40:34,357 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-08-30 17:40:34,357 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-08-30 17:40:34,357 DEBUG: Done: Determine Train/Test split
+2016-08-30 17:40:34,357 DEBUG: Start: Classification
+2016-08-30 17:40:34,982 DEBUG: Info: Time for Classification: 0.624792098999[s]
+2016-08-30 17:40:34,982 DEBUG: Done: Classification
+2016-08-30 17:40:34,984 DEBUG: Start: Statistic Results
+2016-08-30 17:40:34,985 INFO: Accuracy :0.1
+2016-08-30 17:40:34,986 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 17:40:34,986 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-08-30 17:40:34,986 DEBUG: Start: Determine Train/Test split
+2016-08-30 17:40:34,986 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-08-30 17:40:34,986 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-08-30 17:40:34,986 DEBUG: Done: Determine Train/Test split
+2016-08-30 17:40:34,986 DEBUG: Start: Classification
+2016-08-30 17:40:41,176 DEBUG: Info: Time for Classification: 6.19013905525[s]
+2016-08-30 17:40:41,176 DEBUG: Done: Classification
+2016-08-30 17:40:41,179 DEBUG: Start: Statistic Results
+2016-08-30 17:40:41,179 INFO: Accuracy :0.1
+2016-08-30 17:40:41,180 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 17:40:41,180 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-08-30 17:40:41,180 DEBUG: Start: Determine Train/Test split
+2016-08-30 17:40:41,181 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-08-30 17:40:41,181 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-08-30 17:40:41,181 DEBUG: Done: Determine Train/Test split
+2016-08-30 17:40:41,181 DEBUG: Start: Classification
+2016-08-30 17:40:42,661 DEBUG: Info: Time for Classification: 1.48085689545[s]
+2016-08-30 17:40:42,661 DEBUG: Done: Classification
+2016-08-30 17:40:42,663 DEBUG: Start: Statistic Results
+2016-08-30 17:40:42,663 INFO: Accuracy :0.0888888888889
+2016-08-30 17:40:42,664 DEBUG: ### Main Programm for Classification MonoView
+2016-08-30 17:40:42,664 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-08-30 17:40:42,664 DEBUG: Start: Determine Train/Test split
+2016-08-30 17:40:42,664 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
+2016-08-30 17:40:42,665 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
+2016-08-30 17:40:42,665 DEBUG: Done: Determine Train/Test split
+2016-08-30 17:40:42,665 DEBUG: Start: Classification
diff --git a/Code/MonoMutliViewClassifiers/Results/poulet20160830-103357.png b/Code/MonoMutliViewClassifiers/Results/poulet20160830-103357.png
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diff --git a/Code/MonoMutliViewClassifiers/__init__.py b/Code/MonoMutliViewClassifiers/__init__.py
index e69de29b..980f1899 100644
--- a/Code/MonoMutliViewClassifiers/__init__.py
+++ b/Code/MonoMutliViewClassifiers/__init__.py
@@ -0,0 +1,2 @@
+from . import ExecClassif, ResultAnalysis, Versions, Metrics, MonoviewClassifiers, Monoview, Multiview
+__all__ = ['Metrics', 'Monoview', 'MonoviewClassifiers', 'Multiview']
--
GitLab